diff --git a/.gitignore b/.gitignore index 84f817e0..77556986 100644 --- a/.gitignore +++ b/.gitignore @@ -204,7 +204,6 @@ ssl-game-controller/ replays renders/ scratch/ -scenario_bench_results/ benchmark_results/ # local agent-tool config dirs diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d2b64f69..7ccef41f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -5,8 +5,8 @@ repos: - id: black args: ["--line-length", "120"] files: \.py$ - - repo: https://github.com/charliermarsh/ruff-pre-commit - rev: v0.0.261 # replace with latest stable version + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.15.12 hooks: - id: ruff args: ["--fix"] # auto-fix issues like formatting, unused imports diff --git a/AGENTS.md b/AGENTS.md index 4f415150..5c520fb9 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -17,48 +17,55 @@ exists yet: say so in your change rather than guessing. | Area | Paths | Doc | Notes | |---|---|---|---| | Strategy / tactics | `utama_core/engine/`, `strategy/`, `tactics/`, `skills/` | `docs/STRATEGY_DEVELOPMENT.md` | Engine changes are rare: a new primitive, not a new strategy | -| Strategy evaluation | `tools/evaluation/`, `tools/scenario_bench.py`, `utama_core/scenario_bench/`, `utama_core/replay/`, `utama_core/analysis/` | `docs/STRATEGY_DEVELOPMENT.md`, `docs/signals.md` | Change evaluation and strategy in separate commits, so results stay comparable | +| Strategy evaluation | `tools/evaluation/`, `utama_core/replay/`, `utama_core/analysis/` | `docs/STRATEGY_DEVELOPMENT.md`, `docs/signals.md` | Change evaluation and strategy in separate commits, so results stay comparable | | Referee | `utama_core/custom_referee/` | `docs/custom_referee.md` | | | Motion planning | `utama_core/motion_planning/`, `tools/motion_planning_benchmark.py` | `docs/motion_planning_comparison.md` | | | Simulation | `utama_core/rsoccer_simulator/`, `vendor/rSim/` | `vendor/rSim/FORK_NOTES.md` | `vendor/rSim` is the C++ physics fork; `rsoccer_simulator` is the Python env on top of it | -| Real robots (radio, vision, controllers) | `utama_core/team_controller/`, `data_processing/` | `TODO` (see `team_controller/README.md`) | | +| Real robots (radio, vision, controllers) | `utama_core/team_controller/`, `utama_core/data_processing/` | `utama_core/team_controller/README.md` (brief) | | ## Glossary One term per thing; reuse these instead of coining new ones. - **Round-robin / tournament run** — every strategy config plays every other once - (`tools/evaluation/round_robin.py`); writes `replays/tournament_/summary.json`. The ground truth for - "which strategy is better". Slow in full; with `--reuse` only matches whose code changed rerun. -- **Standings** — a round-robin's ranking. `round_robin.py` prints wins and draws; - `metric_correlation.py` uses points per match (3 a win, 1 a draw) and goal difference. -- **Start** — one situation (see below) the scenario bench replays for 10 s, tagged with its - type from the candidate's view. In code: `BenchScenario` (`scenario_bench/start.py`), and - "scenario" in flags and enums. -- **Bank** — a versioned list of starts (`utama_core/scenario_bench/banks/bank_vN.json`), - harvested from one round-robin's replays, up to 40 of each situation type. -- **Scenario bench** — `tools/scenario_bench.py`: a fast paired A/B screen of a candidate - against a baseline on a bank, each start against the opponent it came from; it estimates the - change in goals per match as Σ situations per match (real or sim weights) × the change in - P(goal within 10 s). A diagnostic, not a ranking: it cannot see a candidate change how often - situations happen or score from build-up longer than 10 s, so rank with matches. -- **Bench validators** — `tools/bench_vs_standings.py` and `tools/metric_correlation.py` check - how far the bench and cheap proxy metrics agree with round-robin results. + (`tools/evaluation/round_robin.py`); writes `replays/tournament_/summary.json` and adds its matches to the evaluation ledger. + The check for stalls and the occasional health check of the whole catalog; a single + strategy is judged with `evaluate.py` (below). With `--reuse` only matches whose code changed rerun. +- **Standings** — only a round-robin's ranking: wins and draws as `round_robin.py` prints them, + or points per match (3 a win, 1 a draw) as `metric_correlation.py` computes them. Not the + verdict on a strategy; see Evaluation. - **Motion planning benchmark** — `tools/motion_planning_benchmark.py`: the planner alone on - fixed scenarios. Unrelated to the scenario bench or any strategy. + fixed scenarios. Unrelated to any strategy. +- **Moment / regression moment** — a field state (from a replay or by hand) that reproduced a + bug, with a check of the right behaviour (`utama_core/tests/moments/*.json`, `tools/moments.py`). + A cheap end-to-end check, run in `pixi run test`, that a fixed bug stays fixed; the unit test at + the cause is the real protection. Not a ranking and not a gate on strategies. The workflow and + the bugs found so far: `docs/STRATEGY_DEVELOPMENT.md`, "Debugging a foul or failure". - **Pitch zones** — defensive / middle / attacking third, left wing / centre / right wing, the box, danger: defined once, with a diagram, in `docs/pitch_zones.md`. Use those names. -- **Catch rate** — a diagnostic reported from the bank, never a gate (±4–8 pts between runs). +- **Catch rate** — a diagnostic (share of reachable passes received), never a gate (±4–8 pts between runs). +- **Position map / shot map** — on-pitch pictures of a strategy over many matches, drawn by + `tools/pitch_maps.py` (and `evaluate.py --explain` for a candidate against the champion), always + turned so the strategy attacks right. Position map: a heatmap of where the outfield robots stand in + live play, with the ball and without it, plus each robot's mean position. Shot map: every shot at + the spot it was struck from, own and conceded. They explain how a strategy plays; never a ranking. - **Signal** — an interpretable measure of how a strategy plays (shots, danger conceded, real ball losses, stalls ...), for explaining results, never a target. All listed in `docs/signals.md`. +- **Evaluation / ratings / reference pool / ledger** — `tools/evaluation/evaluate.py`: is a + candidate stronger than the champion of a fixed, versioned reference pool + (`tools/evaluation/pools/pool_vN.json`)? Rounds of matches against the pool's opponents under + seeded sim noise, decided on paired results (candidate and champion against the same opponent + under the same noise) and stopping once the answer is clear; ratings (strength ± SD in goals + per match, `analysis/ratings.py`) over every match in the ledger (`replays/eval/ledger.jsonl`). + Goals only: shots, xG and the other signals were tested as rankers and rejected + (`docs/signals.md`, "Ranking"). The day-to-day way to judge a strategy. - **Situation / goal ledger** — a situation starts when a restart goes live or a side wins the ball in open play; the goal ledger (`utama_core/analysis/goal_ledger.py`) gives each goal to the latest situation less than 10 s before it, so goals per match = Σ situations per match × P(goal). Situation types: own / opponent ball won or free kick in each third, own / opponent kickoff or penalty, force start. See `docs/signals.md`. - **Situation rates** — how often each situation type happens per team per live minute, from - real Division B matches or our round-robin (`banks/situation_rates_{real,sim}.json`, - `goal_ledger --rates`): the scenario bench's weights. + real Division B matches or our round-robin (`goal_ledger --rates PATH`). ## Repo map @@ -111,6 +118,8 @@ declarations) — check there before reintroducing one of them. - **Always pass `--headless`** when running any simulator/integration test: `pixi run pytest utama_core/tests/ --headless`. Much faster; there is no reason to run with graphics in an agent loop. +- A git worktree for a parallel agent: run `tools/setup_worktree.sh` inside it (links `.pixi`, + copies the compiled planner/simulator `.so` files; without them tests differ). - `pixi run test` runs the default suite; `pixi run lint` runs the full pre-commit stack (black, ruff, isort) — run both before considering a change done. - `--level quick|full` (defined in the root `conftest.py`, not `utama_core/tests/ diff --git a/README.md b/README.md index 4f15137c..05147fda 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ -[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/First-Order-RoboCup-SSL/Utama-Core) # Utama Core +[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/First-Order-RoboCup-SSL/Utama-Core) + First Order Robotics' software stack for [RoboCup SSL](https://ssl.robocup.org/) (Small Size League), where teams of six autonomous robots play football. It covers vision and referee input, motion planning, robot control, an in-process referee, a fast simulator (rsim), and the @@ -28,6 +29,7 @@ real robots are optional ([external setup](docs/setup_external.md)). | Task | Start here | |---|---| | Writing or changing a strategy, tactic or skill | [docs/STRATEGY_DEVELOPMENT.md](docs/STRATEGY_DEVELOPMENT.md), [docs/strategies.md](docs/strategies.md) | +| Seeing what the robots did, and why | [docs/visual_guide.md](docs/visual_guide.md); `tools/triage.py RUN_DIR` for a run's worst problems | | Judging whether a strategy is better | [docs/STRATEGY_DEVELOPMENT.md](docs/STRATEGY_DEVELOPMENT.md), [docs/signals.md](docs/signals.md), [docs/signal_report.md](docs/signal_report.md) | | The referee | [docs/custom_referee.md](docs/custom_referee.md) | | Motion planning | [docs/motion_planning_comparison.md](docs/motion_planning_comparison.md) | @@ -50,7 +52,6 @@ Everything lives under `utama_core/`: - `run/`: the main loop (`StrategyRunner`) - `replay/`: replay files, the match-result cache and code fingerprints - `analysis/`: offline analyses of tournament runs and replays (ball losses, chances, restarts, stalls) -- `scenario_bench/`: the scenario bench's starts, banks, harvester and scorer - `rsoccer_simulator/`: the Python simulator environment over the rSim physics fork in `vendor/rSim/` - `team_controller/`, `data_processing/`: vision, robot radio and referee input - `dashboard/`: the browser dashboard @@ -71,9 +72,12 @@ Scripts live in `tools/` (tournaments in `tools/evaluation/`) and `examples/`; s ## System design -![Dataflow Diagram](assets/images/pipeline_new.drawio.png) +One tick of `StrategyRunner`, from vision, robot and referee data to robot commands: -How vision, robot and referee data become one `Game` state: [docs/pipeline_method.md](docs/pipeline_method.md). +![One tick of StrategyRunner](docs/img/pipeline/one_tick.png) + +The same tick as a diagram, with every module: [docs/architecture.md](docs/architecture.md). How +vision, robot and referee data become one `Game`: [docs/pipeline_method.md](docs/pipeline_method.md). ## Contributing @@ -87,7 +91,3 @@ Work on a branch and open a pull request into `main`. A pull request needs: 5. an approval from an assigned reviewer. Editor setup, pre-commit troubleshooting and the pixi environments: [docs/contributing.md](docs/contributing.md). - -## Milestones - -- 2024 November 20 - First goal in grSim (featuring ray casting) diff --git a/assets/images/pipeline.drawio.png b/assets/images/pipeline.drawio.png deleted file mode 100644 index e5d9ba58..00000000 Binary files a/assets/images/pipeline.drawio.png and /dev/null differ diff --git a/assets/images/pipeline_new.drawio.png b/assets/images/pipeline_new.drawio.png deleted file mode 100644 index 3c52bb48..00000000 Binary files a/assets/images/pipeline_new.drawio.png and /dev/null differ diff --git a/conftest.py b/conftest.py index ab3c2886..9f9a7e6d 100644 --- a/conftest.py +++ b/conftest.py @@ -8,6 +8,8 @@ # tests, so every machine runs the same code. Set before numba is imported; rsim worker # processes inherit it. os.environ.setdefault("NUMBA_CPU_NAME", "generic") +# Hide pygame's "Hello from the pygame community" banner from test output (tools set it too). +os.environ.setdefault("PYGAME_HIDE_SUPPORT_PROMPT", "1") def pytest_addoption(parser): diff --git a/docs/README.md b/docs/README.md index 8e5c8bcb..12b98934 100644 --- a/docs/README.md +++ b/docs/README.md @@ -4,13 +4,14 @@ Every doc in this folder, by kind. New to the repo: the [README](../README.md) f ## How to -- [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md): writing and evaluating strategies and tactics: the tactic-kernel model, referee restarts, lessons from past tactic bugs, round-robins, the scenario bench, observability tools. Read before any strategy-layer change. +- [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md): writing and evaluating strategies and tactics: the tactic-kernel model, referee restarts, lessons from past tactic bugs, round-robins, observability tools. Read before any strategy-layer change. - [custom_referee.md](custom_referee.md): the in-process `CustomReferee`: architecture, usage, profiles, and its known gaps. - [custom_referee_gui.md](custom_referee_gui.md): the referee operator panel in the dashboard. - [referee_integration.md](referee_integration.md): how referee commands reach robots (`RefereeOverride`) and what each command requires. - [motion_planning_comparison.md](motion_planning_comparison.md): the motion-planning benchmark and how to read it. - [motion_planning_results.md](motion_planning_results.md): its latest full run. - [tools.md](tools.md): every script and pixi task, by purpose. +- [visual_guide.md](visual_guide.md): the game drawn on the pitch from real replays (how each strategy plays, one match over time, a restart, the motion planner), what each picture has shown us, and how to draw it. - [setup_external.md](setup_external.md): grSim, the official GameController and AutoReferee, SSL Vision for real robots. - [contributing.md](contributing.md): editor setup, commits, pull requests and releases. @@ -20,6 +21,7 @@ Every doc in this folder, by kind. New to the repo: the [README](../README.md) f - [scheduling_math_model.md](scheduling_math_model.md): a mathematical model of tactic scheduling and role allocation, a companion to the above. - [custom_referee_design_decisions.md](custom_referee_design_decisions.md): rule-by-rule decisions from auditing `CustomReferee` against the SSL rulebook. - [pipeline_method.md](pipeline_method.md): how vision, robot and referee data are refined into one `Game` state, and one tick from state to robot commands. +- [architecture.md](architecture.md): the one-tick diagram, and the generated package dependency graph with its import-rule check. ## Current results @@ -31,6 +33,7 @@ Every doc in this folder, by kind. New to the repo: the [README](../README.md) f ## Open work - [roadmap.md](roadmap.md): larger open workstreams, and one-line pointers to finished ones. +- [sim_realism.md](sim_realism.md): how far rsim is from real matches, the realism scorecard, and the plan to close the gap with measured, randomized physics. - [testing_gaps.md](testing_gaps.md): kinds of testing gap that let bugs through, numbered (code cites them by number). - [investigation_default_vs_lowblock_stalemate.md](investigation_default_vs_lowblock_stalemate.md): why `default` vs `low_block` stays 0-0; partly fixed. - [investigation_ball_contact_orientation_divergence.md](investigation_ball_contact_orientation_divergence.md): a robot pinned against the ball rotates away from contact; localized, not fixed. diff --git a/docs/STRATEGY_DEVELOPMENT.md b/docs/STRATEGY_DEVELOPMENT.md index 9f5ec3f8..107c9a5e 100644 --- a/docs/STRATEGY_DEVELOPMENT.md +++ b/docs/STRATEGY_DEVELOPMENT.md @@ -26,6 +26,12 @@ default, `Strategy.set_goalkeeper()`) is pinned outside the scheduler. - **`AbstractStrategy`** (`engine/abstract_strategy.py`) — what `StrategyRunner` drives; wraps a `Strategy` built by a factory. +The main classes and who holds or calls whom: + +![Classes of the tactic-kernel model: Strategy, its slots, Partitioner, Tactic and skills](img/strategy/tactic_kernel.png) + +Drawn by `docs/img/strategy/make_tactic_kernel.py`; rerun it when a name in it changes. + **Invariants:** - *Single writer:* the partition is decided once per tick, before any `Tactic` runs, so two tactics can never contend for a robot. Don't add a path that lets a `Tactic` claim or release @@ -39,6 +45,48 @@ default, `Strategy.set_goalkeeper()`) is pinned outside the scheduler. barrier — every slot's `mem` and commitment clears. `STOP`/`HALT` alone only pause; a pause resumed by `NORMAL_START` keeps state. +One tick, from the runner to the robot commands (the branches are the referee cases handled +in `Strategy.tick`): + +```mermaid +sequenceDiagram + participant SR as StrategyRunner
run/strategy_runner.py + participant AS as AbstractStrategy
engine/abstract_strategy.py + participant S as Strategy
engine/strategy.py + participant RO as RefereeOverride
engine/referee_override.py + participant P as Partitioner
e.g. strategy/split_shape.py + participant T as Tactic
tactics/*.py + participant SK as Skills
skills/src/ + participant RC as robot controller + + SR->>AS: step() (side.strategy, after the frame is refined) + AS->>S: tick(game) + S->>S: barrier reset if entering or resuming a restart + alt restart in progress, or STOP / TIMEOUT + S->>RO: tick(game, motion_controller, command) + RO-->>S: legal-position commands for friendly robots + Note over S: no tactic ticks, pinned goalkeeper ticks only if not covered + S-->>AS: override commands + else HALT + S-->>AS: no commands + else normal play + S->>S: _choose_partition: pin committed slots, filter applicable() + S->>P: free robots, previous partition, available tactic ids + P-->>S: tactic id -> robots + S->>S: _validate_partition + loop each slot with robots + S->>T: tick(game, ctx, robot_ids, mem) + T->>SK: e.g. go_to_ball, go_to_point, move + SK-->>T: RobotCommand + T-->>S: commands, new mem + end + S->>T: pinned goalkeeper tick + S-->>AS: cmd_map + end + AS->>AS: robots missing from cmd_map get execute_default_action + AS->>RC: add_robot_commands, send_robot_commands +``` + ## Referee handling `CustomReferee` (`utama_core/custom_referee/`) runs in-process and identically across @@ -47,6 +95,51 @@ every outfield robot *before* any `Tactic` ticks, reusing the `*Step` classes in `custom_referee/actions.py`. Strategies can override a restart formation via `Strategy(referee_overrides=...)`. See `docs/custom_referee.md`. +To see where the override sends every robot for a restart, run `tools/restart_picture.py` +(`REPLAY.npz --t S`, or `--all-restarts` for one small panel per restart command in the replay). It +records each commanded target without changing the engine, and flags a target inside the 0.8 m keep-out +or a defence area it must avoid. Shown below: one match's restarts from our side (top) and from the other +team's side (bottom), and a free kick against us where the robot inside the keep-out is moved to its edge. + +![Referee override targets for each restart, our side](img/restart_picture/restarts_friendly.png) +![Referee override targets for each restart, the other team's side](img/restart_picture/restarts_enemy.png) +![A free kick against us: the robot inside the keep-out is cleared to its edge](img/restart_picture/free_kick_against_us.png) + +What each referee command makes our robots do, and when tactics take control again. "Ours" and +"theirs" are resolved against our team colour in `RefereeOverride._step_for`. + +```mermaid +flowchart TD + barrier{"entering a restart, or resuming from one?
(or FORCE_START out of a pause)
engine/referee_reset.py"} + wipe["barrier reset: every slot's mem and
commitments cleared (_barrier_reset)"] + sel{"referee command
engine/referee_override.py"} + halt["HALT: no commands from tactics
(execute_default_action stops robots)"] + stop["STOP, TIMEOUT_YELLOW / TIMEOUT_BLUE
StopStep: encroaching robots pushed out of keep-out, rest stop"] + ko_o["PREPARE_KICKOFF (ours)
PrepareKickoffOursStep: lowest-ID non-keeper to the ball,
others to own-half support spots"] + ko_t["PREPARE_KICKOFF (theirs)
PrepareKickoffTheirsStep: non-keepers to own half,
outside the centre circle"] + pe_o["PREPARE_PENALTY (ours)
PreparePenaltyOursStep: kicker to the penalty mark,
others on a line behind it"] + pe_t["PREPARE_PENALTY (theirs)
PreparePenaltyTheirsStep: keeper to our goal line centre,
others behind the penalty mark"] + fk_o["DIRECT_FREE or INDIRECT_FREE (ours)
DirectFreeOursStep: nearest robot becomes kicker,
others stop"] + fk_t["DIRECT_FREE or INDIRECT_FREE (theirs)
DirectFreeTheirsStep: clear robots out of keep-out"] + bp_o["BALL_PLACEMENT (ours)
BallPlacementOursStep: closest robot carries ball to the spot,
others clear"] + bp_t["BALL_PLACEMENT (theirs)
BallPlacementTheirsStep: clear away from ball and target"] + resume["NORMAL_START / FORCE_START
if their kickoff, penalty or free kick is still held
(ball moved less than 5 cm): its positioning continues;
otherwise tactics resume via _choose_partition"] + + barrier -- yes --> wipe --> sel + barrier -- no --> sel + sel --> halt + sel --> stop + sel --> ko_o + sel --> ko_t + sel --> pe_o + sel --> pe_t + sel --> fk_o + sel --> fk_t + sel --> bp_o + sel --> bp_t + sel --> resume +``` + ## Writing a Tactic Lessons from bugs that recurred (mostly `SwitchOfPlayTactic`, `tactics/switch_of_play.py`): @@ -71,10 +164,46 @@ A strategy is a combination of existing tactics plus a partitioner that decides each gets. Most new strategies need no new tactic. When one does, add it as a new module in `tactics/` (on a strategy branch, never by changing an existing tactic: see below). +How a strategy composes tactics, using `build_split_shape_kernel_strategy`: two tactic slots and +one hand-written partitioner that switches on which side has the ball. + +```mermaid +flowchart TD + tick["Strategy.tick, every frame
engine/strategy.py"] + committed{"any slot committed?
is_committed()"} + pinned["those slots keep their robots
(not offered to the picker)"] + free["free pool = outfield robots not pinned"] + avail{"both slots available?
(not pinned, applicable)"} + only["the one available slot
takes every free robot"] + subgraph picker["_possession_split_picker
strategy/split_shape.py"] + order["carrier_first(game, free)
strategy/pickers.py: ball carrier first"] + edge{"friendly closer to the ball
than the enemy?"} + hi["attack = (n+1)//2 + 1"] + lo["attack = n//2 - 1, clamped to 0..n"] + split["first attack robots to attack,
the rest to defense"] + end + validate["Strategy._validate_partition, then each slot ticks
(attack and defense slots below)"] + attack["attack: LeadAndSupportTactic
tactics/lead_and_support.py"] + defense["defense: ShadowAndMarkTactic
tactics/shadow_and_mark.py"] + + tick --> committed + committed -- yes --> pinned --> free + committed -- no --> free + free --> avail + avail -- no --> only + avail -- yes --> order --> edge + edge -- yes --> hi --> split + edge -- no --> lo --> split + validate --> attack + validate --> defense + only --> validate + split --> validate +``` + **Writing one.** Add `strategy/.py` with `build__kernel_strategy(outfield_robot_ids)`, returning a `Strategy(tactics={...}, partitioner=...)`, and the pickers only it uses; then import the factory in `strategy/kernel_strategy.py`. Every `build_*_kernel_strategy` that module -re-exports is discovered by name (`match`), so it joins round-robins and the bench as +re-exports is discovered by name (`match`), so it joins round-robins and `evaluate.py` as `` (a test fails if a factory is defined but not re-exported). Reuse the shared partitioner pieces in `strategy/pickers.py` rather than re-deriving them: - `friendly_closer_to_ball(game)` — the possession edge. True/False is a clear edge; None is a @@ -93,21 +222,16 @@ kernel invariants. Give its partitioner pure-function tests in `tests/strategy/t 2. **One saved match** against a few opponents: `round_robin.py --pair `. Saved, so stalls are recorded (`--no-save` cannot see them). A stall is a bug to fix before anything else. -3. **Matches:** a strict round-robin with it in (`round_robin.py --strict --reuse`), 0 stalls. - With `--reuse` a change to one strategy plays only its own matches, one per other config, so this is the - judgement, not a final confirmation. Record the result in `docs/strategies.md`. -4. **Bench A/B**, only when the round-robin can't answer: the change is too small to move a - row of mostly draws, or you need to know which kind of start it changed. Run it against the - nearest existing strategy, the one it differs from in a single idea, so the A/B tests that - idea: `tools/scenario_bench.py --load-bank --candidate --baseline - ` (see [The situation bench](#the-situation-bench)). Each start plays the opponent - it came from. +3. **Matches:** `tools/evaluation/evaluate.py ` (see [Evaluating a strategy](#evaluating-a-strategy)): + is it better than the pool's champion, worse, or not clearly either? Several candidates go in + one call. A strict round-robin (`round_robin.py --strict --reuse`) is the check for stalls + and the occasional health check of the whole catalog, no longer the way to judge one change. A change to `tactics/`, `skills/` or `pickers.py` is the other way round: reuse saves little, -because most strategies import the changed code and their matches all rerun. Screen it on the bench -first, batch such changes, and run one round-robin over the batch. +because most strategies import the changed code and their matches all rerun. Batch such +changes and run one round-robin over the batch. -**`--reuse`** (round-robin and bench) takes a match's or start's result from +**`--reuse`** takes a match's result from `replays/match_cache/` when nothing it runs has changed since it was stored, so after a change to one strategy only its matches play, one per other config (21 of 231 with 22 configs). What counts as "runs" is `utama_core/replay/fingerprint.py`'s: the factory's own module, every module that @@ -116,21 +240,90 @@ environment (planner, referee, runner, simulator build, packages, CPU). Editing reruns every config that imports it, which is most of them; changing shared code reruns everything, correctly. Each run replays 5% of what it would reuse (`--spot-check`) and compares; a difference evicts the records, says the fingerprint missed a dependency, and fails the run. -Use `--reuse` on every run: the bench's baseline side then costs -nothing while its code is unchanged. +Use `--reuse` on every run. -**What counts as better.** Results: goals and W-D-L in matches, and the bench's effect on goals per match. +**What counts as better.** The verdict is `evaluate.py`'s: goal difference per match, paired against the champion of the reference pool. A round-robin's W-D-L is a diagnostic. Everything in [Reading a tournament run](#reading-a-tournament-run) explains a result; none of it is a target. Don't tune a threshold until results move: a change needs a reason in game -terms, and a bench gain that matches don't confirm means distrust the bench, not that the -strategy got better. The situation bench on bank_v8 gets the size of a several-goal difference -right but did not rank six candidates the way a round-robin does (Spearman -0.09; its standard -error, about 2.3 goals a match, is wider than the spread between them: see -[How far to trust it](#how-far-to-trust-it)). So it screens changes; it doesn't rank strategies, -and a bench result alone never accepts a change. Two round-robins of near-identical code agree +terms. Two round-robins of near-identical code agree at Spearman +0.97, but that is not a ceiling: rsim is deterministic, so they agree largely because the code is near-identical. +### Evaluating a strategy + +`tools/evaluation/evaluate.py CANDIDATE [CANDIDATE ...]` answers one question per candidate: is +it stronger than the champion of a fixed reference pool, in goals, under the realistic sim noise? + +The flowchart contrasts it with the round-robin, which answers a different question. + +```mermaid +flowchart TD + subgraph rr["round_robin.py: is the whole catalog healthy?"] + r1["Every build_*_kernel_strategy config plays
every other once (one full match per pair)"] + r2["Stall and error check
--strict fails on a stall"] + r3["Standings in summary.json
not a verdict on any one strategy"] + r1 --> r2 --> r3 + end + + subgraph ev["evaluate.py: is this candidate stronger than the champion?"] + e1["Pool pool_v1.json: champion, six opponents,
three held-out opponents, sim noise"] + e2["Round k, noise seed k
candidate and champion each play every opponent"] + e7["Every match appended to the ledger
replays/eval/ledger.jsonl"] + e3{"P(candidate stronger), paired
with the champion's results"} + e4["better: P >= 0.99
then --held-out before promoting"] + e5["worse: P <= 0.2
drop it"] + e6["undecided after 6 rounds
drop it"] + e1 --> e2 --> e3 + e2 -.-> e7 + e3 -- "P >= 0.99" --> e4 + e3 -- "P <= 0.2" --> e5 + e3 -- "neither, rounds left" --> e2 + e3 -- "neither, no rounds left" --> e6 + end +``` + +- **The pool** (`tools/evaluation/pools/pool_v1.json`): a champion, six opponents chosen for + spread of styles, three held-out opponents and the sim noise. It changes only on purpose, as + a new version, so a result means the same thing next month; it does not follow the latest + standings. +- **Rounds.** In round k every candidate plays each opponent and the champion once, and the + champion plays each opponent, all with noise seed k and the side under test in the same colour + (the noise is drawn per colour and robot, so one seed can favour a colour by half a goal). A + candidate and the champion are compared on the same opponents under the same noise. +- **The decision** (`ratings.paired_difference`): the candidate's goal difference against each + opponent minus the champion's, same opponent, seed and colour, from rounds 1..k, plus their + head-to-head matches. Paired results face the same noise, so much of it cancels: all of it + for identical code (a byte copy of the champion gets 24 diffs of exactly 0 and stays + undecided), little for unrelated strategies. P(candidate stronger) >= 0.99 is **better**, + <= 0.2 **worse**, and after 6 rounds it is **undecided**: not clearly better, so drop it. + Simulated (match noise SD 1.9 goals): two equal strategies are called better at most 3% of the + time. An unrelated strategy one goal per match worse is dropped after ~2 rounds (~14 + matches), one goal better found 61% of the time, half a goal 20%. A small edit of the champion + (paired results correlated ~0.9): one goal better found every time in ~3 rounds, half a goal + 55%. Small gains of unrelated strategies are below what matches resolve at this cost. +- **Ratings** (`analysis/ratings.py`): a strength per strategy in goals per match with its SD, + fitted on every match in the ledger; printed at the end, and the match noise for the decision. +- **The ledger** (`replays/eval/ledger.jsonl`, `replay/eval_ledger.py`): one line per match from + evaluate.py and round-robins, keyed by the code that played it. A match is never played twice; + the champion's matches are played once per seed and reused by every candidate, and editing a + strategy starts its record afresh. Replays are not kept, except during a `--explain` run (rerun one with `round_robin.py --pair + A B --sim-noise` and the pool's noise with that seed). +- **Before promoting** a candidate called better, run `evaluate.py --held-out`: it must not + be clearly worse against opponents never used for selection. Then edit the pool's champion by + hand. Check its per-opponent table: a candidate far better against one opponent and worse + against the rest is a matchup, not a better strategy. +- **Pictures** (`evaluate.py --explain`, `tools/evaluation/explain.py`): for each candidate, + where both strategies' robots stand with and without the ball, their shots for and against, + their fouls by rule, and a `summary.json` of the numbers behind them, written to + `replays/eval/explain/_/`. The run's matches are kept until they are written. They + explain a verdict and never make one: the verdict is still goals, and a picture is not a ranking. + Matches reused from the ledger have no replay in the run, so the pictures cover only what was + played; the paired goal difference in `summary.json` is only as complete as that. Example, from a + round-robin (`explain.py replays/tournament_ split_shape clear_press_plus --out ...`): + ![Where two strategies' robots stand, with and without the ball](img/explain/positions.png) +- **Cost.** ~45 min of laptop per 66 matches on 8 workers. A clearly worse candidate costs ~14 + matches (~10 min), a close one up to 42. + ### Strategy branches A new or changed strategy, whether a person or an agent writes it, goes on its own branch and @@ -159,8 +352,7 @@ reaches `main` by pull request: than your own matches means the cache is stale (shared code changed since it was filled). Let it run anyway (a full round-robin of 600 s matches has taken from about 1 h 50 min, on 15 workers plugged in, to 4.7 h for `tournament_20261005_170958`; matches are now two halves of 300 s of playing time: about 710 s of sim time on average by the final run's stoppages, 785 s in two measured matches, so expect roughly 20-30% longer): the other strategies' matches run `main`'s code, so they refill the cache for everyone. -5. Open a draft pull request into `main` with the round-robin result, and the bench result if you - ran one. An agent never merges: a person reviews and merges. +4. Open a draft pull request into `main` with the round-robin result. An agent never merges: a person reviews and merges. Several branches can be worked on at once, each in its own worktree; they share `replays/match_cache/`. A round-robin uses one worker per core (`--max-workers N` to cap it), so @@ -180,21 +372,24 @@ whoever runs it. robot/ball trails over a window, optionally anchored on a `MatchLog` event. **Default to this over `load_frames_in_range()`**: a coordinate dump is expensive in context and easy to misread spatially. Use raw frames only for an exact number once the picture has localized the issue. +- **`tools/match_timeline.py`** — one match window as swimlanes on a shared time axis: the referee + command band, one lane per robot coloured by the tactic it was in (a thin tick at each switch, a + dark strip where it had the ball), each robot's distance to the ball, and goals, fouls and shots on + top. Use it for thrashing (a robot switching tactics every few ticks), a tactic stuck committed, or + everyone chasing the ball. Friendly side only has tactic lanes (the intentions log is config_a's). + Thrashing in `split_shape` against `tiki_taka_plus`: robots 2-4 flip between the attack and defence + slots about every 1.5 s, in pairs of rows at the same tick. It is not split_shape's alone: most + strategies that split attack and defence on possession take a tactic for under 1 s and revert about + 5 times a minute (the `tactic_thrash` finding, with the numbers per strategy in `match_timeline.py`): + ![split_shape lanes thrashing between attack and defence slots](img/match_timeline/split_shape_100-160.png) - **`render_clip()`** (`analysis/render_clip.py`) — MP4 of a window for humans, ball-following or full-pitch camera; sim ball teleports are never shown. Needs `ffmpeg`. Commands behind -<<<<<<< HEAD committed clips: `assets/clips/README.md`. - **`docs/strategies.md`** — every factory, whether a round-robin plays it or it is retired, and the latest results. - **`tools/evaluation/round_robin.py`** — round-robin runner (`--max-workers N`, `--both-sides`, -======= - committed clips: `assets/clips/README.md`. -- **`docs/strategies.md`** — every factory's status and the latest results. `baseline` - strategies aren't meant to win; don't tune them to. -- **`tools/evaluation/round_robin.py`** — round-robin runner (`--max-workers N`, `--both-sides`, ->>>>>>> repo-cleanup `--strict`, `--stop-at-first-stall`, `--fuzz-restarts SEED`, `--fuzz-interval LO HI`, - `--no-save`, `--pair A B` for one fixture with A as config_a — reruns a stalled match from a + `--sim-noise JSON`, `--no-save`, `--pair A B` for one fixture with A as config_a — reruns a stalled match from a round-robin; see the determinism caveat below). - **Ball losses** (`analysis/turnover_breakdown.py`) — runs after every saved tournament and writes `ball_losses.md`; see [Reading a tournament run](#reading-a-tournament-run). @@ -208,27 +403,136 @@ whoever runs it. - **Restart fuzzing** (`custom_referee/restart_fuzzer.py`) — injects legal restarts at seeded-random times to exercise auto-advance paths; same seed and interval reproduce the schedule. Details: `docs/custom_referee.md`. +- **Sim noise** (`rsoccer_simulator/src/Utils/sim_noise.py`, `--sim-noise JSON`) — seeded + command latency per robot, kick speed and direction noise (a per-robot offset plus + kick-to-kick spread), two-phase ball friction (slide after a kick, then roll), per-robot + speed, acceleration and braking limits applied to the commands the sim acts on (hardware caps, + below any planner), the physics substep count, and vision noise and dropped detections, all + off by default. Ball friction, kick direction and substeps need a robosim binary built from + `vendor/rSim` (`vendor/rSim/FORK_NOTES.md`); with the stock binary they raise. Draws depend on the seed, team + colour and robot id only, so every pairing in a run gets the same physics and A/B comparisons + stay paired; the same seed replays a match exactly. Settings and draws go into each replay + (`columnar_reader.load_sim_noise`) and `summary.json`, and into the match key only when set. + Ranges come from `docs/sim_realism.md` Phase 1; until then the knobs have no calibrated values. - **`tools/replay_trace.py REPLAY.npz T0 T1`** — text trace of a window: referee command and why it changed, ball, nearest robot per side. The first look at a stall or a voided restart. - **`tools/repro_from_replay.py`** — reload a replay's field state at time *t* into a fresh headless match with tracing on, instead of re-running the whole match (`--help` for flags). +### Debugging a foul or failure + +A foul or failure seen in a match is a symptom: the cause may be the robot, the shared referee +override, the planner, the referee itself, or the check that flagged it. Of the first six found +this way (table below), two were the referee or the check, not the robot. + +Start with `tools/triage.py RUN_DIR --strategy NAME`: open its `index.md` for the worst matches, the finding counts by kind +and the pictured findings. Filter `findings.jsonl` on the kind you care about, open that finding's picture to see what it +means, and confirm with the numbers in its message (or `replay_trace.py --events`) before concluding anything. Triage reads only the friendly side of +each match (the intentions log is written for it): a strategy's matches where it is config_a. In a round-robin each pair plays once, +so a strategy gets only the pairs where it comes first in the config order. + +The same steps as a flowchart; the moment branch is the optional step 5. + +```mermaid +flowchart TD + find["1. Find it
moments.py fouls RUN_DIR --by strategy,rule
(without --by: each foul, with its time and tactic)"] + look["2. Look at it
replay_trace.py REPLAY T0 T1 --events
repro_from_replay.py REPLAY --t S --summary IDS"] + q{"Restart or placement geometry?
a field state alone decides it"} + moment["Capture a moment
moments.py capture REPLAY --t S
play --seeds 0-4 must fail on 4 of 5"] + cause["3. Find the cause in the code
tactic or override, target, planner;
was the rule really broken?"] + fix["4. Fix the cause
with a unit test at the boundary"] + done["Done: the unit test is the protection
(a captured moment stays as a check)"] + + find --> look --> q + q -- "yes" --> moment --> cause + q -- "no: open play, rarely reproduces" --> cause + cause --> fix --> done +``` + +The picture tools (`match_timeline.py`, `restart_picture.py`, `plan_trace.py`, `pitch_maps.py`) write their flags as JSON with `--findings`: filter on that, open the picture to understand a flag, and never conclude from a picture alone. + +1. **Find it.** `pixi run python tools/moments.py fouls replays/tournament_ --by strategy,rule` + counts our side's fouls (any of strategy, rule, tactic, restart; `out_of_bounds`, the ball + leaving the field, is a stoppage and left out unless `--include-stoppages`); without `--by` it + lists each with its time, robot, tactic and the referee command then active + (`--skip-own-restart` drops fouls during our own restarts; `--json` writes either form as JSON). +2. **Look at it.** `tools/replay_trace.py REPLAY T0 T1` is the text timeline of the window: + referee command, ball, nearest robot per side; `--events` lists the kicks, possession, + referee changes and fouls instead; `--json` gives either as JSON. + `tools/repro_from_replay.py REPLAY --t S --summary 3` replays that state with the tactics' + `match_log` tracing on and prints each listed robot's tactic, position and target every + 0.25 s (trace keys name tactics, not strategies). In a parallel worktree, run + `tools/setup_worktree.sh` first. + When a robot does something odd near an obstacle or a keep-out zone (a carrot that swings round a robot, + a robot parked just inside a circle), `tools/plan_snapshot.py REPLAY --t S --robot ID --target X Y` + draws what the planner decided on that tick: the obstacles and clearance rings, the keep-out circle, + the target and the carrot it returned (`--side enemy` for the other strategy's robot; example: + ![kick-off robot parked inside the keep-out circle](img/plan_snapshot/kickoff_keep_out.png)). + `tools/plan_trace.py REPLAY --t0 S --t1 S --robot ID [--target X Y]` runs the same planner on every + tick of a window and draws the robot's path, each tick's carrot and the distances over time, so a + defect that unfolds over seconds shows as a trace (example: the kick-off robot of + ![a kick-off window](img/plan_trace/kickoff_keep_out.png)). The robot's positions are the replay's, + not re-simulated: this window was recorded before the push-out fix, so the robot stays 0.80 m from the + ball, while the current planner's carrot from those states is the push-out point 0.85 m from the ball. +3. **Find the cause in the code**, from what the trace shows: which tactic or override sent the + robot there, and the target, waypoint and obstacles it used. Ask whether the rule was really + broken (real rules, `docs/custom_referee_design_decisions.md`) and whether the check reads it + right. +4. **Fix the cause, with a unit test at the boundary** (CLAUDE.md, Testing), e.g. "a computed + target is off the field". That test is the protection, and it does not need the foul to + reproduce. +5. **Optionally keep a moment**, where a field state alone decides the behaviour (restart and + placement geometry under the referee override). `tools/moments.py capture REPLAY --t S --name + ... --check ... --note "..." [--out PATH]` saves the state; `play JSON --seeds 0-4` must fail on + at least 4 of 5 before the fix (`--against REF` plays the code at REF beside it; `--events` + lists referee changes, fouls, keep-out entries and kicks; `--strategy scratch.py:factory` + tries a strategy from a scratch file). Its note names the fix; if the fix is not in the same + change, set `"known_failure": ""` (a strict xfail). + +Open-play fouls rarely reproduce from a captured state: robots start from rest, tactics from +fresh memory, and the play diverges within a second. Of 21 fouls captured from +tournament_20261017_002337, none reproduced on 4 of 5 seeds, and one did with the opponents +steered along their recorded paths for 1-2 s (tried 2026-10-17, reverted in `9c224880`). Robot +velocities cannot be set either: every rsim teleport resets all robots (`SSLWorld::replace`). So +for open play, go from the trace to the code and a unit test. + +A moment's `checks` are `tools/moments.py`'s `CHECKS` (`keeps_clear`, `stop_speed`) or referee rule names; its `requires` (`restart_lasts`, +`robot_near_ball`) fail it with `did not arise` when the situation it tests never happened. A +restart moment plays one strategy, since the referee override moves every outfield robot the same +way whatever the strategy. `tools/moments.py new NAME --command CMD ...` writes a hand-made field +state to edit. Moments are not a gate on strategies: they pin shared-code bugs. + +| Symptom | Looked like | Cause | Fix | Test | +|---|---|---|---|---| +| Defender pushed off the pitch during their corner placement | robot leaving the field | override push-out target off the pitch in a corner | `14038121` | `tests/referee/test_referee_unit.py` | +| Defender stuck against their placer | robot ignoring the restart | planner found no path out for a robot inside another's clearance | `c64fc6f9` | `test_fpp_pressed_against_robot.py` | +| Defender boxed in a corner closes on the ball | robot driving in | planner detour led back toward the ball; the keep-out guard skipped robots already inside | `935da778` | `test_fpp_inside_ball_keep_out.py` | +| Same moment still failing after the fix | robot driving in | `keeps_clear` counted the ball placed onto a robot standing still | `20ca1974` | `test_moments.py` | +| keep_out foul on their free kick | robot driving in | referee: the kicker moved the ball 0.75 m in the preparation stage; it was in play | `12b46412` | `test_free_kick_taken.py` | +| Our placement times out (hand-made moment) | slow placer | placer drove through the ball toward a point behind it (planner ignores the ball in our placement) | `78685f79` (goes round it) | `test_referee_unit.py` (TestBallPlacementOursApproachAroundBall) | + +The last row matters on real robots only: in sim matches `StrategyRunner` teleports a placed ball +to its target (`docs/custom_referee_design_decisions.md`, Ball placement), so our placer never +runs in a round-robin. Its moment was dropped for that reason: check that a moment's behaviour +happens in sim matches at all before keeping it. The placer is still slow (about 5 s round the +ball, and the carry curves off line), a real-robot item. + **Frame convention trap:** rsim's own frame stores y negated relative to ours. Anything that writes into the sim (e.g. `teleport_robot`) must negate y *and* heading — `543741e` fixed a -teleported robot facing the mirrored heading, which had silently corrupted scenario benches. +teleported robot facing the mirrored heading, which had silently corrupted teleported scenarios. **Determinism:** an rsim match is reproducible: `--pair A B` replays a round-robin match exactly (13 of 13 sampled from `tournament_20260928_221404` at `1de1e18d`, every replay array and sidecar). One cause of "differs run to run" was state that outlived a match: `has_ball`'s and shielding's hysteresis were module dicts keyed by robot id, shared by both teams and carried from match to -match in a round-robin or bench worker process (`c27bfad7`). Module-level state in a tactic or +match in a round-robin worker process (`c27bfad7`). Module-level state in a tactic or skill must be keyed by team and cleared at match start (`StrategyRunner.__init__`). If a round-robin match and its `--pair` rerun still differ, suspect more of the same; compare the two replays' `ball_p` to find the first differing tick. **Fast and exact numerics:** the motion planner, refiners and Kalman filter use faster float code by default (about 19% less wall time per match under full load). `UTAMA_EXACT_MATH=1` restores the -original numpy paths, byte-identical to before (`8c191eea`); the round-robin, bench pools and rsim +original numpy paths, byte-identical to before (`8c191eea`); the round-robin pools and rsim all inherit it. The two modes agree to 1e-9 per call but matches diverge after the first differing tick, so a baseline recorded in one mode is only comparable with a candidate run in the same mode. @@ -242,8 +546,8 @@ it, a reference range from the latest round-robin and what an off value usually there before adding a metric: it probably exists. These are diagnostics, not objectives. Fewer losses is not better on its own: a strategy that -never passes or shoots loses the ball least. Rank strategies by results (goals, W-D-L), and use -the rest to explain why one wins or loses, and which shared primitive (reception, carrying, the +never passes or shoots loses the ball least. Rank strategies by results (goals, through +the ratings: `docs/signals.md`, "Ranking"), and use the rest to explain why one wins or loses, and which shared primitive (reception, carrying, the planner, the referee) is failing every strategy at once. ## Real matches @@ -285,205 +589,16 @@ What a converted match holds, and where it differs from a sim replay (the detail `NORMAL_START` and keeps `DIRECT_FREE_*` until the next stoppage. The converter switches it to `NORMAL_START` once the ball has moved 0.05 m, as our referee does. - `.source.json` holds the teams, the final score, the tracker used and the field size. +- Sidecars for measuring physics and vision (sim realism, [sim_realism.md](sim_realism.md)), + not read by any replay tool: `.kicks.json`, every kick the tracker reported (start, + kicker, start position, velocity estimates, stop); `.tracker.zip`, per-tick ball and + robot visibility and robot angular velocity; with `--raw-vision`, `.vision.zip`, every + raw camera detection (about another 35 MB a match). Kicks and visibility come from whichever + autoref's tracker sends them (in 2026 TIGERs did, ER-Force not). For a match converted + before these existed, `--backfill-extras` downloads the log again and adds them without + rewriting the replay. - Logs before 2020 have no tracker: the converter merges raw camera detections without filtering (ghost robots, ball jumps), and their referee also sends `GOAL_*` and `INDIRECT_FREE_*`. The corpus above starts in 2022. -## Play and watch one start - -A round-robin only tests a change in the situations its matches happen to reach, and some -pairings rarely reach the one a change is about (a defensive change, say, against an opponent -that seldom gets into our third). The bank's starts are real moments from a round-robin, so -`--play` puts a strategy into one directly and shows what it did: - - pixi run python tools/scenario_bench.py --load-bank utama_core/scenario_bench/banks/bank_v8.json \ - --play counter_flow_vs_split_shape_t426.3_own_ball_won_attacking --candidate high_press --render png - -It plays the start against the opponent it came from (`--opponent` to choose another) and -prints its outcome and signals and a timeline: the referee command, the ball, the robot of each -side nearest it every 0.25 s, and each change in the candidate's tactic assignments. The replay, -the candidate's match log, the timeline and the picture are kept in -`replays/scenario_play//` (`--render mp4` makes a clip instead). `--repeats N` plays it -from N slightly jittered positions, to see whether what happened holds up. A start is robot and -ball positions and the referee command only: both strategies begin it with no memory, and every -robot begins at rest (the ball keeps its speed). rsim has no way to place a moving robot: its -reset sends a robot's position and heading, and the physics fork stops every robot it places, -so adding one means changing code every cached match depends on. - -A restart start is the moment the referee awarded it: a free kick at the first tick the replay -shows `DIRECT_FREE_*`, a kickoff or penalty at its `PREPARE_*`. The referee then runs it as in -the match (`apply_scenario` puts it in that command from the first tick): a free kick goes live -once its kicker is at the ball and the defenders clear for 2 s, a penalty about 5 s in. A -ball-won start is the frame nearest the start of a goal-ledger possession spell, in live play. - -To find starts, tag them by situation with `--where` (names from -[pitch_zones.md](pitch_zones.md), all from the candidate's side): - - pixi run python tools/scenario_bench.py --load-bank utama_core/scenario_bench/banks/bank_v8.json \ - --list-scenarios --where third=defensive ball=theirs - -| Tag | Values | -|---|---| -| `third` | `defensive`, `middle`, `attacking`: where the ball is | -| `lane` | `left_wing`, `centre`, `right_wing` | -| `restart` | `ours`, `theirs` (whose kickoff, free kick, penalty or ball placement), `stop`, `live` | -| `ball` | `ours`, `theirs` (a robot within 0.2 m of it), `loose` | -| `near` | robots of each side within 1.5 m of the ball, e.g. `2v3` | - -`TAG=a,b` matches either value. `--where` also narrows a bench run or `--save-bank`. -`--list-scenarios` also prints each start's situation type. - -Bench results recorded before `b7ea54cf` are not comparable with later ones: until then every -start was played from the bench runner's own yellow kickoff. Nor are results recorded before -`82d6eb66`: until then every start began with phantom speeds (velocities were differenced -against the formation the runner was built in: a ball at 130 m/s, fouls in almost every -open-play start). `apply_scenario` now starts each side's history from the scenario. - -## The situation bench - -For a targeted A/B of one change to shared code (a tactic, the planner), which reruns most of a -round-robin even with `--reuse`, or of a change too small to move match results, use -`tools/scenario_bench.py` on the committed bank (`utama_core/scenario_bench/banks/`, newest -version). It estimates how the change moves goals per match from short paired replays of the -situations goals come from: - - effect = Σ over situation types (how often it happens per match) - × (candidate's P(goal) from it − baseline's) - -- **Situations** are the goal ledger's ([signals.md](signals.md#where-do-goals-come-from)), from - the candidate's view: own or opponent ball won or free kick in each third, own or opponent - kickoff or penalty, force start. Each start of the bank carries its type. -- **P(goal)** is the first goal within 10 s of the start (`--horizon`, the ledger's window): for, - against, or none. Both strategies play the same starts from config_a's side, so the change per - type is a mean of paired differences, with its standard error. -- **Opponent:** each start is played against its source match's config_b, so the bank's - opponents are the round-robin's. A start whose opponent is not on the branch is skipped and - counted. `--opponent` plays every start against one config instead. -- **How often** comes from a weights file of situation rates (`goal_ledger --rates`): per team - per live minute for each type, times live minutes per match. `--weights` picks it: - `banks/situation_rates_real.json` (default; the 118 published Division B matches 2022-2026, 7.4 - live minutes a match) or `banks/situation_rates_sim.json` (`tournament_20261011_230119`, 153 - matches, 9.0 live minutes). Real matches have fewer defensive-third regains and kickoffs, more - regains higher up, and far more free kicks and force starts per live minute than ours, so the - two weightings can disagree. - - # once, at the baseline commit - pixi run python tools/scenario_bench.py --load-bank utama_core/scenario_bench/banks/bank_vN.json \ - --candidate split_shape --workers 15 --output-dir bench_base - # per candidate commit - pixi run python tools/scenario_bench.py --load-bank utama_core/scenario_bench/banks/bank_vN.json \ - --candidate split_shape --workers 15 --output-dir bench_new \ - --against-results bench_base/scenario_bench_.json - -(or `--baseline ` to compare two strategies at one commit). The report has a row per -situation type: n, the candidate's and baseline's P(for) and P(against), the paired change with -its standard error, how often the type happens per match, and its contribution to goals scored, -conceded and goal difference per match. The last line printed is the total: change in goal -difference per match, ± its standard error (types independent; within a type, the standard -error of the mean paired difference). Under 2 standard errors is within chance. - -What the effect is and is not: - -- It sees only what a strategy does from each situation. The bank's starts are fixed, so a - strategy that changes how often situations happen (wins the ball higher up, concedes fewer - free kicks, or is forced into more of its own defensive-third regains) gets no credit or - blame for that. A round-robin does; it remains the confirmation. -- It over-counts goals. A goal counts for every situation that started in the 10 s before it, - and situations overlap: summed over every situation, P(goal within 10 s) × count is 1.21× the - goals actually scored under sim rates and 1.58× under real ones (measured with the ledger on - both corpora). Read the effect as a scaled estimate. The factor need not be the same for - every strategy, so it can reorder close candidates. -- It does not see goals from build-up. A goal more than 10 s after the latest situation began - ("sustained possession" in the ledger) is outside every start's horizon, and strategies - differ widely in how many they score: in `tournament_20261011_230119`, 3-7% of the split_* - family's goals, but 19-40% of overload_press's, high_press's and press_and_pass's, and 60-68% - of score_aware_counter_flow's and counter_flow's. The bench under-rates the slow scorers. - `--horizon 20` sees more of them, but the weights are for 10 s situations. The same horizon - hid passes aimed 10° off on bank_v5 (t = -7.6 at 20 s, +0.8 at 10 s; see the roadmap). - -rsim is deterministic, so the same code gives the same outcomes, and a baseline recorded once -serves every later candidate. A bench worker keeps its rsim subprocess between starts -(`enable_sim_reuse` in `robosim_wrapper.py`), asking it for a new native world each time instead -of paying about 0.4 s wall and 0.5 s CPU to start one: outcomes and every sim state are -bit-identical to a fresh process per start, in exact and fast numerics (checked on 106 starts in -two shuffled orders). Only the sim is kept: a `StrategyRunner` is still built per start, which -costs about 0.2 s. - -Each start's record also keeps the scorer's ordinal outcome (goal against -3 ... goal for +3, -`scenario_scorer.py`), its signals, fouls and stalls; the per-start table and `--stop-at-t` are -on that ordinal, not on the effect. `--stop-at-t 4` scores a shuffled sample 100 starts at a -time and stops once |t| of the mean ordinal delta reaches 4, or as futile once |mean| + 2.5 × -stderr < 0.15 (`FUTILE_BELOW`, `FUTILE_Z`); it was calibrated on bank_v5's 846 starts at a 20 s -horizon (passes aimed 10° off detected after about 300 starts; a null candidate stopped futile -after a fifth to two thirds of a pass) and has not been re-calibrated for the situation bank. - -### The bank - -`bank_v8` (from `tournament_20261011_230119`, 147 trustworthy matches of 153) holds 640 starts: -40 of each of the 15 other situation types and 20 of each kickoff type (every type had more). Each type -is sampled spread over its source matches (seeded, near-duplicates dropped), from config_a's -view, with its source match, time, configs and type in its provenance. Restarts are taken at -the first tick showing their command; ball-won starts at the start of the ledger's possession -spell. On this branch 293 of the 640 are playable: 347 come from matches against split_block, -split_outlet, split_outlet_wall, split_press, split_sticky or split_wall, which live only on -`strategy/search-20261011`. Per playable type that leaves 11-25 starts (kickoffs 6 and 9). - -A new bank from a new round-robin (keep its replays until this is done) takes a few minutes and -no simulation: - - pixi run python tools/scenario_bench.py --harvest-from replays/tournament_ --per-situation 40 \ - --workers 15 --save-bank utama_core/scenario_bench/banks/bank_vN+1.json --list-scenarios - -Restarts and ball-won moments both come from the goal ledger, so the bank and the weights use -one definition of a situation. Starts where a robot is past the field lines are dropped (the -sim can't place it there), and a start whose run errored on either side has no delta. A -harvest has no hand-authored starts: they have no type or opponent. There is no screen that -plays starts forward to pick "informative" ones: the one tried varied the opponent, not the -candidate, and threw away as many useful starts as it kept. A new bank needs a new baseline run. - -### How far to trust it - -Measured 2026-10-12 with `tools/bench_vs_standings.py`: split_shape as the baseline, six -candidates spread over the standings of `tournament_20261011_230119`, bank_v8's 293 playable -starts (those against the candidate or the baseline left out, so 222-247 each), one run each. -Round-robin is the candidate's mean goal difference per match minus split_shape's, against the -same opponents (10 each, one match per pair). - -| Candidate | Round-robin | Bench, sim weights | Bench, real weights | -|---|---:|---:|---:| -| overload_press | -2.22 | -6.21 ± 2.25 | -6.43 ± 2.54 | -| press_and_pass | -2.78 | -6.28 ± 2.28 | -7.18 ± 2.26 | -| counter_flow | -4.00 | -6.03 ± 2.26 | -6.42 ± 2.48 | -| clear_press_plus | -5.78 | -5.98 ± 2.48 | -6.49 ± 2.32 | -| high_press | -6.11 | -5.95 ± 2.29 | -7.03 ± 2.39 | -| decoy_and_overload | -7.89 | -7.24 ± 2.71 | -5.80 ± 2.71 | - -Over the six, sim weights: Pearson +0.49, Spearman -0.09; real weights (which describe other -matches, so are not expected to fit this round-robin): Pearson -0.49, Spearman -0.31. The bench -gets the level right: every candidate is worse than split_shape, by 6.3 a match on average, -which is 5.2 after the 1.21× over-count against 4.8 in the round-robin. It does not rank the -candidates: each estimate's standard error (about 2.3) is larger than the spread of the -round-robin differences (SD 2.2, of which about 1.3 is the round-robin's own noise at one -match per pair). With six candidates even an unbiased bench at this precision would be expected -to correlate at only about +0.5. So at this size the bench detects a large difference -(several goals a match) and says which situations it comes from; it cannot tell apart -candidates within 2-3 goals of each other. Use it for that, and rank with matches. - -Noise is not the only reason. The round-robin's gap between each candidate and split_shape, -split by the ledger into its parts (each candidate's situation counts and conversion against -split_shape's, over all its matches), is mostly conversion: -4.8 to -6.8 goals a match per -candidate. That is the part the bench measures, which is why it gets the level right. Between -the candidates, though, three parts differ by similar amounts: conversion (spread about 2 -goals), how often situations happen (decoy_and_overload -2.7, the others within 0.7) and goals -from build-up (+0.35 to +1.3 against split_shape). The bench sees only the first. Per second of -simulation it is no more precise than matches either: it used about a third of the play time -of the round-robin's matches for these candidates, for a standard error about √3 larger. - -Where the noise comes from: the frequent situations with rare goals. Defensive-third regains -happen about 16 times a match per side under sim rates, so one goal in 17 starts moves the -effect by about 1 goal a match; the two defensive-third ball-won types make up about 70% of the -variance. Kickoffs never led to a goal within 10 s in this run, for any strategy. Allocating -starts in proportion to (situations per match × spread of the paired difference) instead of -flat per type would give a standard error of about 1.45 with the same 293 starts, 1.0 with -about 600, and 0.5 with about 2400 (estimated from this run). Making the split_* variants -available here would more than double the playable starts of bank_v8. +The scenario bench (retired 2026-10-17) replayed 10 s starts harvested from replays; to look at one point in a replay use `tools/repro_from_replay.py --t `, and to pin a failure there add a regression moment ("Debugging a foul or failure", above), and `analysis/goal_ledger.py` lists a replay's situations and goals. diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 00000000..00c44c7d --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,182 @@ +# Architecture + +How the packages under `utama_core/` depend on each other, and how one tick flows through them. + +## One tick + +The flow of one tick of `StrategyRunner`, from vision to robot (module paths under `utama_core/`; the +same steps in words: [README](../README.md#system-design)). [pipeline_method.md](pipeline_method.md) +describes the same path in more detail. + +```mermaid +flowchart TD + subgraph input["Input"] + vis["Vision frames
data_processing/receivers/vision_receiver.py (real, grSim)
or rsim observations"] + refsrc["Referee data
custom_referee/custom_referee.py (rsim)
or data_processing/receivers/referee_receiver.py"] + end + + subgraph world["World model (data_processing/refiners/)"] + pos["PositionRefiner
refiners/position.py"] + vel["VelocityRefiner
refiners/velocity.py"] + rinfo["RobotInfoRefiner
refiners/robot_info.py"] + rref["RefereeRefiner
refiners/referee.py"] + game["Game / GameFrame
entities/game/game.py"] + end + + subgraph decision["Decision (engine/, strategy/)"] + strat["Strategy.tick
engine/strategy.py"] + restart{"Restart, STOP or TIMEOUT?
engine/referee_override.py"} + halt{"HALT?
engine/referee_reset.py"} + ovr["RefereeOverride.tick
engine/referee_override.py
custom_referee/actions.py"] + part["Partitioner (robots per tactic)
engine/strategy.py, strategy/*.py"] + tac["Tactic.tick
tactics/*.py"] + skill["Skills
skills/src/*.py"] + end + + subgraph motion["Motion (motion_planning/)"] + mc["MotionController.calculate
motion_planning/src/controllers/
fpp, dwa, trajsample"] + end + + subgraph output["Output (team_controller/)"] + ctrl["RobotController.send_robot_commands
team_controller/src/controllers/"] + rsim["rsim
rsoccer_simulator/ (sim/rsim_robot_controller.py)"] + grsim["grSim over UDP
sim/grsim_robot_controller.py"] + real["Real robots over radio
real/real_robot_controller.py"] + end + + vis --> pos + pos --> vel --> rinfo --> rref + refsrc --> rref + rref --> game + game --> strat + game -. "previous frame" .-> refsrc + strat --> restart + restart -- "yes" --> ovr + restart -- "no" --> halt + halt -- "yes: no tactic commands" --> ctrl + halt -- "no" --> part + part --> tac --> skill --> mc + ovr --> mc + mc --> ctrl + ctrl --> rsim + ctrl --> grsim + ctrl --> real +``` + +## Match data flow + +Every rsim match writes files under `replays/` (`REPLAY_BASE_PATH`), and the analyses, tools and +evaluation read them back. A match's file stem is `A_vs_B` (`round_robin.py`'s `_tag`); the names +below are from a real run directory. `--reuse` reads the match cache, and only matches whose +fingerprint changed are played again. + +![Match data flow: drivers, StrategyRunner, the per-match files, the stores and the readers](img/architecture/match_data_flow.png) + +Drawn by `docs/img/architecture/make_match_data_flow.py`; rerun it when a name in it changes. + +`evaluate.py` removes its own scratch run directory when it finishes, so its matches survive only +in the ledger. + +## Package dependencies + +Generated by `tools/package_graph.py` from the imports of every `.py` under `utama_core/` (tests +excluded as sources). Each edge is one first-level package importing another, labelled with its +number of imports; packages with no edges are left out. Regenerate and paste the output below: + + pixi run python tools/package_graph.py + +The only edge into `tests` is `run` (the runner imports `utama_core/tests/common`'s test manager). +The rule the script checks: `replay/` never imports `analysis/` (see [AGENTS.md](../AGENTS.md)). + +```mermaid +flowchart LR + p_analysis["analysis"] -->|11| p_config["config"] + p_analysis["analysis"] -->|3| p_engine["engine"] + p_analysis["analysis"] -->|11| p_entities["entities"] + p_analysis["analysis"] -->|8| p_replay["replay"] + p_analysis["analysis"] -->|1| p_rsoccer_simulator["rsoccer_simulator"] + p_config["config"] -->|1| p_global_utils["global_utils"] + p_custom_referee["custom_referee"] -->|20| p_config["config"] + p_custom_referee["custom_referee"] -->|48| p_entities["entities"] + p_custom_referee["custom_referee"] -->|2| p_global_utils["global_utils"] + p_custom_referee["custom_referee"] -->|1| p_shared["shared"] + p_custom_referee["custom_referee"] -->|1| p_skills["skills"] + p_dashboard["dashboard"] -->|1| p_analysis["analysis"] + p_dashboard["dashboard"] -->|3| p_config["config"] + p_dashboard["dashboard"] -->|3| p_custom_referee["custom_referee"] + p_dashboard["dashboard"] -->|1| p_engine["engine"] + p_dashboard["dashboard"] -->|2| p_entities["entities"] + p_dashboard["dashboard"] -->|2| p_replay["replay"] + p_data_processing["data_processing"] -->|6| p_config["config"] + p_data_processing["data_processing"] -->|26| p_entities["entities"] + p_data_processing["data_processing"] -->|2| p_global_utils["global_utils"] + p_data_processing["data_processing"] -->|4| p_team_controller["team_controller"] + p_engine["engine"] -->|3| p_config["config"] + p_engine["engine"] -->|2| p_custom_referee["custom_referee"] + p_engine["engine"] -->|17| p_entities["entities"] + p_engine["engine"] -->|1| p_global_utils["global_utils"] + p_engine["engine"] -->|3| p_motion_planning["motion_planning"] + p_engine["engine"] -->|1| p_rsoccer_simulator["rsoccer_simulator"] + p_engine["engine"] -->|1| p_skills["skills"] + p_engine["engine"] -->|1| p_tactics["tactics"] + p_engine["engine"] -->|1| p_team_controller["team_controller"] + p_entities["entities"] -->|1| p_config["config"] + p_global_utils["global_utils"] -->|1| p_config["config"] + p_global_utils["global_utils"] -->|2| p_entities["entities"] + p_motion_planning["motion_planning"] -->|23| p_config["config"] + p_motion_planning["motion_planning"] -->|23| p_entities["entities"] + p_motion_planning["motion_planning"] -->|3| p_global_utils["global_utils"] + p_motion_planning["motion_planning"] -->|8| p_rsoccer_simulator["rsoccer_simulator"] + p_replay["replay"] -->|6| p_config["config"] + p_replay["replay"] -->|1| p_custom_referee["custom_referee"] + p_replay["replay"] -->|1| p_data_processing["data_processing"] + p_replay["replay"] -->|20| p_entities["entities"] + p_replay["replay"] -->|2| p_global_utils["global_utils"] + p_replay["replay"] -->|5| p_rsoccer_simulator["rsoccer_simulator"] + p_replay["replay"] -->|3| p_team_controller["team_controller"] + p_rsoccer_simulator["rsoccer_simulator"] -->|4| p_config["config"] + p_rsoccer_simulator["rsoccer_simulator"] -->|3| p_entities["entities"] + p_rsoccer_simulator["rsoccer_simulator"] -->|2| p_global_utils["global_utils"] + p_run["run"] -->|8| p_config["config"] + p_run["run"] -->|4| p_custom_referee["custom_referee"] + p_run["run"] -->|4| p_data_processing["data_processing"] + p_run["run"] -->|4| p_engine["engine"] + p_run["run"] -->|12| p_entities["entities"] + p_run["run"] -->|2| p_global_utils["global_utils"] + p_run["run"] -->|2| p_motion_planning["motion_planning"] + p_run["run"] -->|2| p_replay["replay"] + p_run["run"] -->|6| p_rsoccer_simulator["rsoccer_simulator"] + p_run["run"] -->|1| p_shared["shared"] + p_run["run"] -->|1| p_skills["skills"] + p_run["run"] -->|1| p_team_controller["team_controller"] + p_run["run"] -->|1| p_tests["tests"] + p_shared["shared"] -->|3| p_config["config"] + p_shared["shared"] -->|4| p_entities["entities"] + p_shared["shared"] -->|1| p_motion_planning["motion_planning"] + p_shared["shared"] -->|1| p_skills["skills"] + p_skills["skills"] -->|6| p_config["config"] + p_skills["skills"] -->|2| p_data_processing["data_processing"] + p_skills["skills"] -->|1| p_engine["engine"] + p_skills["skills"] -->|26| p_entities["entities"] + p_skills["skills"] -->|2| p_global_utils["global_utils"] + p_skills["skills"] -->|8| p_motion_planning["motion_planning"] + p_skills["skills"] -->|3| p_shared["shared"] + p_strategy["strategy"] -->|65| p_engine["engine"] + p_strategy["strategy"] -->|22| p_entities["entities"] + p_strategy["strategy"] -->|23| p_motion_planning["motion_planning"] + p_strategy["strategy"] -->|63| p_tactics["tactics"] + p_tactics["tactics"] -->|6| p_config["config"] + p_tactics["tactics"] -->|25| p_engine["engine"] + p_tactics["tactics"] -->|40| p_entities["entities"] + p_tactics["tactics"] -->|1| p_motion_planning["motion_planning"] + p_tactics["tactics"] -->|15| p_shared["shared"] + p_tactics["tactics"] -->|36| p_skills["skills"] + p_team_controller["team_controller"] -->|10| p_config["config"] + p_team_controller["team_controller"] -->|7| p_entities["entities"] + p_team_controller["team_controller"] -->|1| p_global_utils["global_utils"] + p_team_controller["team_controller"] -->|2| p_rsoccer_simulator["rsoccer_simulator"] + p_team_controller["team_controller"] -->|1| p_skills["skills"] +``` + +Violations of the import rules (`replay/` never imports `analysis/`): +none diff --git a/docs/custom_referee.md b/docs/custom_referee.md index 906765c5..a1aac478 100644 --- a/docs/custom_referee.md +++ b/docs/custom_referee.md @@ -27,6 +27,13 @@ CustomReferee.step(game_frame, t) In grSim/Real, `StrategyRunner` raises if the first vision geometry packet disagrees with `full_field_dims`. +Each tick, the rule checks decide which violation (if any) reaches the state machine, and the +state machine's output reaches the strategy through `RefereeRefiner` and `RefereeOverride`: + +![CustomReferee tick: rule checks, game state machine, and the path to the strategy and simulator](img/custom_referee/architecture.png) + +Drawn by `docs/img/custom_referee/make_architecture.py`; rerun it when a name in it changes. + ## Rules Built in `_build_active_rules` (`custom_referee.py`), in this priority order; defaults live in @@ -39,9 +46,9 @@ Built in `_build_active_rules` (`custom_referee.py`), in this priority order; de | `BallSpeedRule` | live play | Ground speed > 6.5 m/s, edge-detected; same last-touch attribution. Non-stopping (§8.4.2). | | `DoubleTouchRule` | `NORMAL_START` after a restart | Only the restart kicker (first toucher after arming) is barred; disarms when any other robot touches. Open-play release-and-reacquire dribbling is legal. Keeps `_prev_command` across `reset()` because `reset()` runs on the very transition it must observe. | | `DefenseAreaRule` | live play | An extra defender (> `max_defenders`) touching the ball in its own area: penalty. An attacker touching the ball while at least partly in ours: non-stopping, 2 s re-raise (§8.4.2). | -| `KeepOutRule` | `DIRECT_FREE_*`, `PREPARE_KICKOFF_*` | Defending team within 0.5m for 30 consecutive frames: non-stopping, 2 s re-raise, restarts the free kick's 10 s clock (§8.4.3). Not during bare `STOP` or penalties. | +| `KeepOutRule` | `DIRECT_FREE_*`, `PREPARE_KICKOFF_*` | Defending team within 0.5m for 30 consecutive frames: non-stopping, 2 s re-raise, restarts the free kick's 10 s clock (§8.4.3). Not during bare `STOP` or penalties, nor once a free kick is taken (stage 3 of the state machine: the ball moved 0.05m, so it is live). | | `PenaltyTimeLimitRule` | `NORMAL_START`/`FORCE_START` after `PREPARE_PENALTY_*` | Penalty still in play 10 s after its normal start: goal kick for the defenders (§5.3.5). | -| `NoProgressRule` | live play | Ball hasn't moved 0.05 m in 10 s: `STOP` then `FORCE_START` where it lies (§8.1). The clock waits for the ball to be in play (a `NORMAL_START` restart: 0.05 m of movement or the 10 s auto-in-play) and is held while the ball is inside either defense area. Last in the list, so a real foul on the same tick wins. | +| `NoProgressRule` | live play | Ball hasn't moved 0.05 m in 10 s: `STOP` then `FORCE_START` where it lies, moved out of the opponent's defense area if it is inside it (`legal_restart_position`, §8.1). The clock waits for the ball to be in play (a `NORMAL_START` restart: 0.05 m of movement or the 10 s auto-in-play) and is held while the ball is inside either defense area. Last in the list, so a real foul on the same tick wins. | | `PushingRule`, `KeeperHeldBallRule`, `ExcessiveDribblingRule`, `RobotStopSpeedRule`, `CrashingRule`, `DefenseAreaStoppageRule`, `BallPlacementInterferenceRule` | see each rule file | SSL §8.3/8.4 audit (`434ab29`). Rationale per rule: `custom_referee_design_decisions.md`. | ## State machine and auto-advance @@ -53,12 +60,19 @@ Built in `_build_active_rules` (`custom_referee.py`), in this priority order; de | # | Transition | Trigger | |---|---|---| | 1 | `STOP` → queued restart | all robots ≥0.5m from ball (15s clear timeout) | -| 2 | `PREPARE_KICKOFF_*`/`PREPARE_PENALTY_*` → `NORMAL_START` | prepare timer + kicker in position (kick-off: and every robot in its own half, waited for at most 10 s), held 2s | -| 3 | `DIRECT_FREE_*` → `NORMAL_START` | kicker ≤0.3m from ball, defenders ≥0.5m, held 2s | +| 2 | `PREPARE_KICKOFF_*`/`PREPARE_PENALTY_*` → `NORMAL_START` | prepare timer + kicker in position (kick-off: and every robot in its own half, waited for at most 10 s past the prepare timer, `_KICKOFF_POSITIONING_CAP_S`), held 2s | +| 3 | `DIRECT_FREE_*` → `NORMAL_START` | the free kick is taken: the ball has moved ≥0.05m from where it was when the command began (at once, from any kick during the preparation); or kicker ≤0.3m from ball, defenders ≥0.5m, held 2s | | 4 | `BALL_PLACEMENT_*` → next command | ball ≤0.15m from target, held 2s (10s placement timeout) | | 5 | `NORMAL_START` → `FORCE_START` | `kickoff_timeout_seconds` elapsed and ball unmoved | | 6 | end of a half → second half, then `POST_GAME` | the half's playing time (`half_duration_seconds`) used up | +The same transitions as a state diagram, with the command each one leaves and enters (team-specific +commands are shown as `*`; the restart team is the one named in the command): + +![Referee commands and the transitions between them, with the trigger or timer on each](img/custom_referee/state_machine.png) + +Drawn by `docs/img/custom_referee/make_state_machine.py`; rerun it when a name in it changes. + Nothing leaves `HALT` on its own: a person or the game controller resumes it. In the sim, `StrategyRunner` resumes after 5 s with `resume_from_halt`, which goes to `STOP` and keeps the queued restart, so play continues into it (transition 1). A `HALT` that interrupts a restart in @@ -108,9 +122,10 @@ relaxed profile). Copy `simulation.yaml`/`human.yaml` to start a new profile. Pass `StrategyRunner(..., referee=CustomReferee(...))`. Then the UDP `RefereeMessageReceiver` is not started; `referee.step()` runs each tick and feeds `ref_buffer` -(→ `RefereeRefiner` → `game.referee` → `RefereeOverride`); and on the edge into `STOP` with a -`designated_position`, the ball is teleported there when a `sim_controller` exists (skipped in -Real). `StrategyRunner` only checks `isinstance(referee, CustomReferee)`, so subclasses work +(→ `RefereeRefiner` → `game.referee` → `RefereeOverride`); and when a `sim_controller` exists (skipped in +Real) the ball is teleported to the restart's `designated_position`: on entry to `BALL_PLACEMENT_*` +for a restart with a spot (fouls, goals, half time; the restart itself follows), or on the edge into +a `STOP` whose next command is `FORCE_START` (no progress), which then force-starts. `StrategyRunner` only checks `isinstance(referee, CustomReferee)`, so subclasses work unchanged. `CustomReferee.reset()` restores score/stage/command/timers for a new RL episode (`BaseRule.reset_for_new_episode()` also clears `GoalRule`'s cooldown); call `seed_clock()` again afterwards. diff --git a/docs/custom_referee_design_decisions.md b/docs/custom_referee_design_decisions.md index 2466e8ba..e1245330 100644 --- a/docs/custom_referee_design_decisions.md +++ b/docs/custom_referee_design_decisions.md @@ -22,6 +22,36 @@ Decisions from the audit of `CustomReferee` against the SSL rulebook. Numbers ar (y = ±3.0m) behind the mark. Fully off-field placement is deferred until the simulator supports it. Penalties stay disabled in the built-in profiles. +## Foul to restart + +Which violation gives which restart, and to whom, as the code does it (`_handle_goal` and +`_handle_foul` in `state_machine.py`). A stopping violation always goes through STOP first; when it +carries a spot, the team that gets the restart places the ball there before the restart. + +```mermaid +flowchart LR + goal["Goal
rules/goal_rule.py"] + ft["Free-kick fouls
out_of_bounds_rule.py (incl. aimless kick), double_touch_rule.py,
keeper_held_ball_rule.py, excessive_dribbling_rule.py,
pushing_rule.py (one side at fault), penalty_time_limit_rule.py"] + pen["Extra defender touches ball in own box
defense_area_rule.py"] + fs["Pushing with similar force, no progress
pushing_rule.py, no_progress_rule.py"] + stop2["2nd Defense Area Stoppage foul
defense_area_stoppage_rule.py"] + nons["Non-stopping: ball speed, crashing, keep-out, attacker in box,
robot stop speed, placement interference, 1st stoppage foul"] + o1["Kick-off for the team that conceded
BALL_PLACEMENT at centre, then PREPARE_KICKOFF"] + o2["Free kick for the other team
BALL_PLACEMENT at spot, then DIRECT_FREE
out of bounds with no known last touch: STOP only"] + o3["Penalty for the attacking team, no foul counter
BALL_PLACEMENT at penalty mark, then PREPARE_PENALTY"] + o4["FORCE_START where the ball lies
no placement, sim teleports the ball"] + o5["HALT; the interrupted restart is queued again on resume"] + o6["Foul counter and cards only
command unchanged"] + + goal -- "goal" --> o1 + goal -- "scorer fouled in last 2 s: invalid_goal" --> o2 + ft --> o2 + pen --> o3 + fs --> o4 + stop2 --> o5 + nons --> o6 +``` + ## Rulebook audit (2026-09-28, Division B) Checked against the [SSL rulebook](https://robocup-ssl.github.io/ssl-rules/sslrules.html). @@ -41,8 +71,8 @@ Checked against the [SSL rulebook](https://robocup-ssl.github.io/ssl-rules/sslru | Free-kick position (§5.3.3) | ≥ 0.2 m from all lines and ≥ 1 m from either defense area, else the closest valid spot | 0.65 m from a defense area (planner margin) | Fixed: 1 m. Sim: the spot is pushed out along x only (a square corner), not to the Euclidean closest point, and lines aren't re-clamped for in-field positions | | Throw-in, goal kick, corner kick (§6.1.1, §6.2.1–2) | Throw-in 0.2 m in from the touch line; goal kick 1 m / 0.2 m; corner kick 0.2 m / 0.2 m, in the corner | Ball placed where it crossed, 0.25 m infield, 0.5 m on both axes near a corner, then kept 1 m off the box | Fixed: over a goal line, a corner kick 0.5 m from both lines or a goal kick 1 m from the goal line, 0.5 m from the touch line, the same spot as every other goal kick (`RefereeGeometry.goal_kick_position`; a disallowed goal or a penalty out of time put it 0.2 m from the touch line) (placing it where it crossed made a free kick 2 m in front of goal: 521 kicks, 150 goals in one round-robin). Sim: 0.5 m, not 0.2 m, from the touch line, and a throw-in stays 0.25 m in; 0.2 m from two lines at once let a drifting ball go straight back out (the corner-loop fix in `OutOfBoundsRule`) | | Aimless kick (§6.2.3, Div B) | A ball touched by a robot that then crosses the halfway line and the opponent's goal line outside the goal, touching no other robot: free kick for the opponent from where it was kicked | Not implemented: a goal kick in the opponent's corner, so a blind clearance cost nothing (28% of goal kicks led to one at the other end within seconds, tournament_20261005_170958) | Fixed: `OutOfBoundsRule` keeps where the ball was last touched in this spell of play (`last_touch.touching_team`), and restarts from there when the touch was on the far side of the halfway line, strictly (a kick-off from the line itself is not aimless); the spot is kept 0.25 m inside the lines and 1 m off the defense areas like any free kick. `out_of_bounds.aimless_kick`, on in both profiles: we play Division B | -| Free kick stages (§5.3.3) | One free-kick command; the kick puts the ball in play | `DIRECT_FREE_*` preparation, then `NORMAL_START` once the kicker is ready | Sim: kept. The two stages give the auto-advance a readiness check with no human referee | -| Ball placement (§5.2) | The team places the ball | With a simulator controller, `StrategyRunner` teleports the ball to `designated_position` and force-starts | Sim: kept, so matches don't wait on physical placement. Real mode still places | +| Free kick stages (§5.3.3) | One free-kick command; the kick puts the ball in play | `DIRECT_FREE_*` preparation, then `NORMAL_START` once the kicker is ready | Sim: kept. The two stages give the auto-advance a readiness check with no human referee. A kick during the preparation stage ends it too: the ball moved 0.05 m from where it was when the command began, so `NORMAL_START` (live play, as after a ready kick) and `KeepOutRule` stops. Before, the referee missed the kick and charged a defender the ball rolled onto with keep_out (`free_kick_robot_drives_in_at_start`). Auto-advance profiles only (`direct_free_to_normal`); a human-run referee issues `NORMAL_START` itself | +| Ball placement (§5.2) | The team places the ball | With a simulator controller, `StrategyRunner` teleports the ball to `designated_position` on entry to `BALL_PLACEMENT_*`, then the restart itself follows (only a `STOP` queued for `FORCE_START`, no progress, force-starts) | Sim: kept, so matches don't wait on physical placement. Real mode still places | | Multiple Defenders (§8.4.1) | A non-keeper entirely inside its own area touching the ball: penalty | More than `max_defenders` (1) with centres inside, one touching | Fixed: the goalkeeper's own touch is exempt. The keeper id comes from the referee data on the frame (`GameFrame.referee`, the team info's `goalkeeper`); with no referee data attached every touch counts. The occupancy proxy (`max_defenders`) is kept | | Double Touch (§8.2) | Kicker may not touch again before another robot; free kick from the ball position | Same | Already correct | | Crashing | §8.4.2 | Any contact with closing speeds within 0.3 m/s of each other was a "both at fault" crash, including robots at rest against each other (6694 fouls in 231 matches; 30 of 97 goals then voided by the goal-validity rule) | Fixed: a crash needs > 1.5 m/s projected on the line between the robots; the 0.3 m/s band then compares the robots' own speeds, as TIGERs AutoReferee's `BotCollisionDetector` does. Its 0.1 s brake lookahead is modelled too (each robot's speed less 0.4 m/s, floored at 0, where TIGERs reverses it): without it a round-robin had 226 crashes to TIGERs' 8 over the same frames | diff --git a/docs/img/architecture/make_match_data_flow.py b/docs/img/architecture/make_match_data_flow.py new file mode 100644 index 00000000..5628daf8 --- /dev/null +++ b/docs/img/architecture/make_match_data_flow.py @@ -0,0 +1,129 @@ +"""Draws match_data_flow.png for docs/architecture.md: one rsim match's files under replays/, the stores that +index them, and the analyses, tools and ratings that read them back. + +pixi run python docs/img/architecture/make_match_data_flow.py +""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from diagram_style import Diagram # noqa: E402 + +# Every name a box shows; utama_core/tests/tools/test_diagram_names.py checks they still exist. +NAMES = [ + "StrategyRunner", + "match_key", + "tools/evaluation/round_robin.py", + "tools/evaluation/evaluate.py", + "tools/convert_ssl_log.py", + "utama_core/run/strategy_runner.py", + "utama_core/replay/ssl_log.py", + "utama_core/replay/columnar_writer.py", + "utama_core/replay/columnar_reader.py", + "utama_core/engine/match_log.py", + "utama_core/engine/match_stats.py", + "utama_core/replay/match_cache.py", + "utama_core/replay/fingerprint.py", + "utama_core/replay/eval_ledger.py", + "utama_core/analysis/goal_ledger.py", + "utama_core/analysis/ratings.py", + "utama_core/analysis/chances.py", + "utama_core/analysis/turnover_breakdown.py", + "utama_core/analysis/restart_outcomes.py", + "utama_core/analysis/stuck_detector.py", + "tools/signal_report.py", + "tools/moments.py", + "tools/replay_trace.py", + "tools/repro_from_replay.py", +] + +d = Diagram( + 184, + 124, + "Match data flow", + "Every rsim match writes its files under replays/. The stores index them; the analyses, tools and ratings read " + "them back.", + figsize=(15, 10.8), +) + +# --- drivers and sources -------------------------------------------------------------------------- +d.box( + 22, + 80, + 36, + 20, + "Drivers", + "tools/evaluation/round_robin.py\ntools/evaluation/evaluate.py\nmatch_key (replay/fingerprint.py)\npicks which matches to play", + kind="decision", +) +d.box(22, 104, 38, 12, "Official SSL logs", "*.log.gz\nconvert_ssl_log.py, replay/ssl_log.py", kind="source") +d.box(72, 80, 32, 16, "StrategyRunner", "run/strategy_runner.py\nplays each match", kind="decision") +d.arrow((40, 80), (56, 80)) +d.arrow((40, 104), (98, 100), "converted logs: same types", lx=1.0, ly=2.0) + +# --- files written per match ---------------------------------------------------------------------- +d.box( + 120, + 98, + 44, + 14, + "A_vs_B.npz", + "+ A_vs_B.sparse_referee.pkl\nreplay/columnar_writer.py", + kind="state", +) +d.box( + 120, + 80, + 44, + 16, + "A_vs_B.intentions.jsonl", + "+ A_vs_B.stats.json\nengine/match_log.py\nengine/match_stats.py", + kind="state", +) +d.arrow((88, 84), (98, 96)) +d.arrow((88, 78), (98, 80)) + +# --- stores --------------------------------------------------------------------------------------- +d.box(22, 44, 34, 12, "Match cache", "replays/match_cache/\nreplay/match_cache.py", kind="state") +d.arrow((14, 70), (14, 50), "store", lx=1.0) +d.arrow((30, 50), (30, 70), "reuse", lx=1.0) +d.box(72, 26, 32, 12, "Evaluation ledger", "replays/eval/ledger.jsonl\nreplay/eval_ledger.py", kind="state") +d.arrow((40, 71), (56, 30), "append", lx=-1.0, ha="right") +d.box(118, 60, 36, 12, "summary.json", "+ ball_losses.md, one per run", kind="state") +d.arrow((40, 73), (100, 62), "round_robin.py", lx=0.0, ly=2.0) + +# --- readers -------------------------------------------------------------------------------------- +d.box(165, 92, 34, 12, "goal_ledger.py", "goal_ledger.json", kind="output") +d.box( + 165, + 70, + 34, + 16, + "analysis/", + "chances, turnover_breakdown,\nrestart_outcomes, stuck_detector", + kind="output", +) +d.box(165, 48, 34, 18, "tools/", "signal_report.py, moments.py,\nreplay_trace.py,\nrepro_from_replay.py", kind="output") +d.box(165, 26, 34, 12, "ratings.py", "strengths and verdicts", kind="output") +d.arrow((142, 98), (148, 92)) +d.arrow((142, 94), (148, 72)) +d.arrow((142, 80), (148, 70)) +d.arrow((142, 76), (148, 48)) +d.arrow((88, 26), (148, 26), "every ledger match", lx=0.0, ly=2.0) + +d.note( + 4, 10, "evaluate.py deletes its scratch run directory when it finishes;\nits matches survive only in the ledger." +) + +d.key( + 104, + 48, + [ + ("source", "outside the process: logs"), + ("decision", "drivers and the strategy runner"), + ("state", "files and stores read back later"), + ("output", "analyses, tools, ratings"), + ], +) +print(d.save(__file__, "match_data_flow.png")) diff --git a/docs/img/architecture/match_data_flow.png b/docs/img/architecture/match_data_flow.png new file mode 100644 index 00000000..c054e877 Binary files /dev/null and b/docs/img/architecture/match_data_flow.png differ diff --git a/docs/img/custom_referee/architecture.png b/docs/img/custom_referee/architecture.png new file mode 100644 index 00000000..5b5a0b3d Binary files /dev/null and b/docs/img/custom_referee/architecture.png differ diff --git a/docs/img/custom_referee/make_architecture.py b/docs/img/custom_referee/make_architecture.py new file mode 100644 index 00000000..10084048 --- /dev/null +++ b/docs/img/custom_referee/make_architecture.py @@ -0,0 +1,176 @@ +"""Draws architecture.png for docs/custom_referee.md ("## Architecture"): one tick of CustomReferee, from the +GameFrame through the rule checks and the game state machine to the strategy layer and the simulator. + +pixi run python docs/img/custom_referee/make_architecture.py +""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from diagram_style import INK_2, LOOP, Diagram # noqa: E402 +from matplotlib.patches import FancyBboxPatch # noqa: E402 + +# Every name a box shows; utama_core/tests/tools/test_diagram_names.py checks they still exist. +NAMES = [ + "CustomReferee", + "GameFrame", + "GameStateMachine", + "RefereeData", + "RefereeRefiner", + "RefereeOverride", + "RefereeMessageReceiver", + "StrategyRunner", + "Strategy", + "MatchStats", + "_disallowed_goal", + "_TRANSITION_COOLDOWN", + "utama_core/custom_referee/custom_referee.py", + "utama_core/custom_referee/state_machine.py", + "utama_core/custom_referee/rules", + "utama_core/entities/game/game_frame.py", + "utama_core/engine/strategy.py", + "utama_core/tactics", +] + +d = Diagram( + 150, + 128, + "CustomReferee, one tick", + "custom_referee/custom_referee.py steps on each GameFrame. The result reaches the strategy through the " + "same buffer as a real referee.", + figsize=(14, 12), +) + +# --- rule checks (a dashed group) ---------------------------------------------------------------- +d.ax.add_patch( + FancyBboxPatch( + (2, 55), + 68, + 44, + boxstyle="round,pad=0,rounding_size=1.2", + facecolor="none", + edgecolor=INK_2, + linewidth=1.0, + linestyle="--", + zorder=1, + ) +) +d.note(4, 97.3, "CustomReferee.step: rule checks", fontsize=9.5, weight="bold", va="center") + +d.box( + 36, + 109, + 50, + 9, + "GameFrame, one tick", + "entities/game/game_frame.py", + kind="state", +) +d.box( + 36, + 89, + 62, + 13, + "Stopping rules: the first violation wins", + "custom_referee/rules/, in priority order: goal, out of bounds, double touch,\n" + "defense area, pushing, keeper held ball, excessive dribbling, no progress,\n" + "penalty time limit, defense area stoppage (2nd foul: HALT)", + kind="decision", + size=10, +) +d.box( + 36, + 71, + 62, + 15, + "Non-stopping rules: the first one is kept", + "they only feed the foul counter; a stopping rule later still wins\n" + "ball speed, keep out, crashing, robot stop speed,\n" + "ball placement interference, defense area (attacker touch),\n" + "defense area stoppage (1st foul)", + kind="decision", + size=10, +) +d.note( + 4, + 59.2, + "Scorer fouled in the last 2 s: the goal is invalid_goal (_disallowed_goal)", + va="center", +) + +# --- state machine and the referee's data -------------------------------------------------------- +d.box( + 36, + 41, + 62, + 13, + "GameStateMachine.step", + "custom_referee/state_machine.py\n" + "0.3 s cooldown (_TRANSITION_COOLDOWN), then the violation\n" + "(a stopping foul or goal changes the command), then auto-advance", + kind="decision", +) +d.box( + 36, + 24, + 50, + 10, + "Referee buffer", + "deque(maxlen=1): only the latest\nStrategyRunner (real: RefereeMessageReceiver)", + "buffer", +) +d.box( + 36, + 8, + 50, + 10, + "RefereeRefiner", + "sets GameFrame.referee: command, stage,\nscores, designated position", + "refiner", +) + +d.main((36, 104.5), (36, 99)) +d.main((36, 55), (36, 47.5)) +d.main((36, 34.5), (36, 29)) +d.main((36, 19), (36, 13)) +d.note(37.5, 51.2, "one violation, or none", colour=INK_2, style="italic", va="center") +d.note(37.5, 31.8, "RefereeData", colour=INK_2, style="italic", va="center") + +# --- strategy and simulator ---------------------------------------------------------------------- +d.box( + 116, + 8, + 60, + 13, + "Strategy.step", + "restart, STOP or TIMEOUT: RefereeOverride places the robots\n" + "otherwise tactics and the partitioner run as usual\n" + "engine/strategy.py, tactics/", + kind="decision", +) +d.box( + 116, + 24, + 60, + 12, + "Simulator", + "teleports the ball to designated_position;\nMatchStats records last_violation", + kind="source", +) +d.main((61, 8), (86, 8)) +d.note(73.5, 11.4, "game.referee", colour=INK_2, style="italic", ha="center", va="center") +d.arrow((61, 24), (86, 24), colour=INK_2, ls="--") + +d.key( + 86, + 62, + [ + ("source", "outside the process"), + ("state", "game state"), + ("decision", "referee or strategy decision"), + ("buffer", "holds data between stages"), + ("refiner", "turns data into game state"), + ], +) +print(d.save(__file__, "architecture.png")) diff --git a/docs/img/custom_referee/make_state_machine.py b/docs/img/custom_referee/make_state_machine.py new file mode 100644 index 00000000..3fc5fbe5 --- /dev/null +++ b/docs/img/custom_referee/make_state_machine.py @@ -0,0 +1,90 @@ +"""Draws state_machine.png for docs/custom_referee.md ("## State machine and auto-advance"): the referee +commands GameStateMachine moves between, with the trigger or timer on each transition. The blue line is the +live-play cycle: STOP, a free kick, NORMAL_START, and a stopping foul or goal back to STOP. + +pixi run python docs/img/custom_referee/make_state_machine.py +""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from diagram_style import INK_2, SURFACE, Diagram # noqa: E402 + +LABEL_BOX = {"facecolor": SURFACE, "edgecolor": "none", "pad": 1.0} + +# Every name a box or note shows; utama_core/tests/tools/test_diagram_names.py checks they still exist. +NAMES = [ + "GameStateMachine", + "RefereeCommand", + "utama_core/custom_referee/state_machine.py", +] + +d = Diagram( + 200, + 138, + "Referee commands and their transitions", + "custom_referee/state_machine.py, GameStateMachine. A box is a RefereeCommand (* = the team); each arrow is " + "its trigger or timer.", + figsize=(16, 11.5), +) + +# --- states -------------------------------------------------------------------------------------- +d.box(14, 100, 24, 10, "HALT", "starts here", kind="output") +d.box(50, 76, 30, 12, "STOP", "robots clear the ball", kind="decision") +d.box(102, 114, 34, 10, "BALL_PLACEMENT_*", kind="decision", size=10) +d.box(102, 94, 34, 10, "PREPARE_KICKOFF_*", kind="decision", size=10) +d.box(102, 74, 34, 10, "PREPARE_PENALTY_*", kind="decision", size=10) +d.box(102, 54, 34, 10, "DIRECT_FREE_*", kind="decision", size=10) +d.box(102, 34, 34, 10, "FORCE_START", kind="decision", size=10) +d.box(176, 74, 30, 12, "NORMAL_START", "the ball is in play", kind="decision", size=10) +d.box(50, 24, 30, 10, "POST_GAME", "stage, with STOP", kind="output") + +# --- the live-play cycle (blue): STOP -> free kick -> NORMAL_START -> STOP -------------------------- +d.main((63, 72), (85, 57)) +d.main((119, 56), (161, 70)) +d.loop([(176, 80), (176, 124), (50, 124), (50, 82)]) +d.note(112, 126.5, "a stopping foul or goal, or the half's time up", colour=INK_2, style="italic", ha="center") + +# --- queued restarts out of STOP ----------------------------------------------------------------- +d.arrow((60, 82), (85, 112), "placement", lx=-1.5, ly=2.0, ha="right") +d.arrow((64, 80), (85, 94), "kick-off", lx=-1.0, ly=-3.6, ha="right") +d.arrow((65, 76), (85, 74), "penalty", lx=1.0, ly=2.4) +d.arrow((60, 70), (85, 36), "FORCE_START", lx=-2.5, ly=0.0, ha="right") +d.arrow((50, 70), (50, 29), "second half's time used up", lx=-2.0, ly=-6.0, ha="right") +d.arrow((24, 95), (40, 82), "resumed after\n5 s (sim)", lx=-1.5, ly=-2.5, ha="right") + +# --- back into the live-play path and between restarts ------------------------------------------- +d.arrow((102, 109), (102, 99), "placed, then the queued restart", lx=1.5, ly=0.0, ha="left") +d.arrow((102, 49), (102, 39), "no kick in 10 s", lx=1.5, ly=0.0, ha="left") +d.arrow((119, 92), (161, 78), "3 s prepared, held 2 s", lx=0.0, ly=4.0, ha="left") +d.arrow((119, 74), (161, 74), "penalty ready, held 2 s", lx=-19.0, ly=3.0, ha="left") +d.note(142.0, 51.5, "ball unmoved 10 s", colour=INK_2, style="italic", ha="center", va="center", bbox=LABEL_BOX) +d.note( + 134.0, 68.0, "ball moved, or kicker ready", colour=INK_2, style="italic", ha="center", va="center", bbox=LABEL_BOX +) +d.arrow((165, 68), (119, 35)) + +# --- notes --------------------------------------------------------------------------------------- +d.note( + 4, + 13, + "A queued restart leaves STOP once every robot is 0.5 m from the ball, or after 15 s.\n" + "Any stopping foul or goal sends play to STOP; a second defense area stoppage foul sends any state to HALT.\n" + "Non-stopping fouls change no state. A placement that is not made in 10 s counts as a failure.", + colour=INK_2, + va="top", + linespacing=1.5, +) + +d.key( + 160, + 30, + [ + ("output", "halted or ended"), + ("decision", "referee command"), + ], +) +d.note(160, 19, "blue: the live-play cycle", colour=INK_2, style="italic") + +print(d.save(__file__, "state_machine.png")) diff --git a/docs/img/custom_referee/state_machine.png b/docs/img/custom_referee/state_machine.png new file mode 100644 index 00000000..8ce11e7c Binary files /dev/null and b/docs/img/custom_referee/state_machine.png differ diff --git a/docs/img/diagram_style.py b/docs/img/diagram_style.py new file mode 100644 index 00000000..2bdbbfb2 --- /dev/null +++ b/docs/img/diagram_style.py @@ -0,0 +1,133 @@ +"""The shared style of the drawn architecture pictures (docs/img/*/make_*.py): boxes by kind, receivers, +arrows, the blue main-loop line and a key, on one canvas in made-up units. + +Each picture script also lists, in a module-level `NAMES`, every class, function, constant or path its +boxes name; `utama_core/tests/tools/test_diagram_names.py` checks they still exist, so a rename fails +`pixi run test` instead of leaving a picture that is quietly wrong. +""" + +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +from matplotlib.patches import FancyArrowPatch, FancyBboxPatch, Polygon # noqa: E402 + +INK, INK_2, SURFACE = "#1f1f1f", "#5c5c5c", "#fcfcfb" +LOOP = "#2a78d6" # the main data path: thick and blue +KINDS = { # fill, edge. A kind means the same thing in every picture. + "source": ("#ececec", "#8a8a8a"), # outside the process: network, simulator, robots + "receiver": ("#e6dcf3", "#7c5aa6"), # pulls data in (drawn as a parallelogram) + "buffer": ("#ffffff", "#8a8a8a"), # holds data between stages + "refiner": ("#fde6cc", "#dd8a25"), # turns data into game state + "state": ("#dcebfa", "#2a78d6"), # game state, files on disk, anything read back later + "decision": ("#d8f0e5", "#1b9b6c"), # the strategy layer and the referee's decisions + "motion": ("#fff1c9", "#c99400"), # motion planning and control + "output": ("#f4f4f4", "#5c5c5c"), # where commands or results leave +} + + +class Diagram: + """One picture: a canvas `w` by `h` units, title and subtitle at the top left.""" + + def __init__(self, w: float, h: float, title: str, subtitle: str = "", figsize=(14, 11.5)): + self.fig, self.ax = plt.subplots(figsize=figsize) + self.fig.patch.set_facecolor(SURFACE) + self.ax.set_facecolor(SURFACE) + self.ax.set_xlim(0, w) + self.ax.set_ylim(0, h) + self.ax.axis("off") + self.ax.text(2, h - 3, title, fontsize=16, weight="bold", color=INK) + if subtitle: + self.ax.text(2, h - 6.4, subtitle, fontsize=9.5, color=INK_2) + + def box(self, x, y, w, h, title, sub="", kind="buffer", size=10.5): + """A rounded box centred on (x, y): bold title, smaller detail lines under it.""" + fill, edge = KINDS[kind] + self.ax.add_patch( + FancyBboxPatch( + (x - w / 2, y - h / 2), + w, + h, + boxstyle="round,pad=0.25,rounding_size=1.2", + facecolor=fill, + edgecolor=edge, + linewidth=1.4, + zorder=3, + ) + ) + ax = self.ax + if sub: + ax.text(x, y + h / 2 - 1.6, title, ha="center", va="top", fontsize=size, weight="bold", color=INK, zorder=4) + ax.text( + x, y + h / 2 - 4.6, sub, ha="center", va="top", fontsize=8.6, color=INK_2, zorder=4, linespacing=1.35 + ) + else: + ax.text(x, y, title, ha="center", va="center", fontsize=size, weight="bold", color=INK, zorder=4) + + def receiver(self, x, y, w, h, title, sub): + """A parallelogram: a thread or call that pulls data in.""" + fill, edge = KINDS["receiver"] + s = 1.6 + corners = [ + (x - w / 2 + s, y + h / 2), + (x + w / 2 + s, y + h / 2), + (x + w / 2 - s, y - h / 2), + (x - w / 2 - s, y - h / 2), + ] + self.ax.add_patch(Polygon(corners, closed=True, facecolor=fill, edgecolor=edge, linewidth=1.4, zorder=3)) + self.ax.text(x, y + 1.3, title, ha="center", va="center", fontsize=10.5, weight="bold", color=INK, zorder=4) + self.ax.text(x, y - 1.9, sub, ha="center", va="center", fontsize=8.6, color=INK_2, zorder=4) + + def arrow(self, a, b, label="", colour=INK_2, lw=1.3, ls="-", rad=0.0, lx=0.0, ly=0.0, ha="left"): + """An arrow from `a` to `b`; `label` in italics beside its midpoint, moved by (lx, ly).""" + self.ax.add_patch( + FancyArrowPatch( + a, + b, + arrowstyle="-|>", + mutation_scale=13, + color=colour, + linewidth=lw, + linestyle=ls, + connectionstyle=f"arc3,rad={rad}", + zorder=2, + shrinkA=0, + shrinkB=0, + ) + ) + if label: + mx, my = (a[0] + b[0]) / 2 + lx, (a[1] + b[1]) / 2 + ly + self.ax.text(mx, my, label, ha=ha, va="center", fontsize=8.6, color=INK_2, style="italic", zorder=4) + + def loop(self, points): + """The thick blue main-path line through `points`, arrowhead at the end.""" + xs, ys = zip(*points) + self.ax.plot(xs[:-1], ys[:-1], color=LOOP, linewidth=3.2, solid_capstyle="round", zorder=1) + self.arrow(points[-2], points[-1], colour=LOOP, lw=3.2) + + def main(self, a, b): + """One straight segment of the main path.""" + self.arrow(a, b, colour=LOOP, lw=3.2) + + def note(self, x, y, text, colour=INK_2, **kw): + """Free text, 8.6 pt unless `fontsize` says otherwise.""" + self.ax.text(x, y, text, **{"fontsize": 8.6, "color": colour, **kw}) + + def key(self, x, y, entries, title="Key"): + """A key at (x, y) (its top left): one swatch per (kind, label). A second column passes title="".""" + self.ax.text(x, y, title, fontsize=9.5, weight="bold", color=INK, va="top") + for i, (kind, label) in enumerate(entries): + fill, edge = KINDS[kind] + yy = y - 3.2 - i * 2.8 + self.ax.add_patch( + FancyBboxPatch((x, yy - 0.9), 4, 1.8, boxstyle="round,pad=0.1", facecolor=fill, edgecolor=edge) + ) + self.ax.text(x + 5.5, yy, label, fontsize=8.6, color=INK_2, va="center") + + def save(self, script: str, name: str) -> Path: + """Write `name` beside the calling script (pass `__file__`).""" + out = Path(script).with_name(name) + self.fig.savefig(out, dpi=110, bbox_inches="tight", facecolor=SURFACE) + return out diff --git a/docs/img/explain/fouls.png b/docs/img/explain/fouls.png new file mode 100644 index 00000000..df570c49 Binary files /dev/null and b/docs/img/explain/fouls.png differ diff --git a/docs/img/explain/positions.png b/docs/img/explain/positions.png new file mode 100644 index 00000000..2668bdc5 Binary files /dev/null and b/docs/img/explain/positions.png differ diff --git a/docs/img/explain/shots.png b/docs/img/explain/shots.png new file mode 100644 index 00000000..617068ce Binary files /dev/null and b/docs/img/explain/shots.png differ diff --git a/docs/img/explain/summary.json b/docs/img/explain/summary.json new file mode 100644 index 00000000..2b89258c --- /dev/null +++ b/docs/img/explain/summary.json @@ -0,0 +1,112 @@ +{ + "run_dir": "/home/isaac/dev/ssl/Utama-Core/replays/tournament_20261017_151052", + "candidate": "split_shape", + "champion": "clear_press_plus", + "replays_read": 21, + "sides": { + "split_shape": { + "matches": 11, + "goals_for_per_match": 6.0, + "goals_against_per_match": 1.2727272727272727, + "shots_for_per_match": 21.90909090909091, + "shots_against_per_match": 9.0, + "in_possession_attacking_half": 0.3653179190751445, + "in_possession_mean_abs_y": 0.9595364315577324, + "out_possession_own_half": 0.683714969241285, + "shots_from_attacking_third": 0.7219917012448133, + "fouls_in_defensive_third": 0.3, + "fouls_per_match": { + "ball_speed": 0.455, + "crashing": 2.636, + "defense_area": 0.455, + "defense_area_stoppage": 4.636, + "double_touch": 0.455, + "excessive_dribbling": 1.273, + "invalid_goal": 0.091 + } + }, + "clear_press_plus": { + "matches": 11, + "goals_for_per_match": 1.2727272727272727, + "goals_against_per_match": 0.8181818181818182, + "shots_for_per_match": 6.2727272727272725, + "shots_against_per_match": 6.454545454545454, + "in_possession_attacking_half": 0.6478642596428924, + "in_possession_mean_abs_y": 1.4162411967222082, + "out_possession_own_half": 0.5518248175182482, + "shots_from_attacking_third": 0.7536231884057971, + "fouls_in_defensive_third": 0.11538461538461539, + "fouls_per_match": { + "crashing": 1.182, + "defense_area": 0.455, + "defense_area_stoppage": 1.091, + "double_touch": 0.455, + "excessive_dribbling": 1.545 + } + } + }, + "paired": { + "clear_danger": { + "candidate": 10, + "champion": -3, + "matches": 1 + }, + "counter_flow": { + "candidate": 0, + "champion": 0, + "matches": 0 + }, + "decoy_and_overload": { + "candidate": 0, + "champion": 0, + "matches": 0 + }, + "give_and_go_solo": { + "candidate": 0, + "champion": 0, + "matches": 0 + }, + "high_press": { + "candidate": 0, + "champion": 0, + "matches": 0 + }, + "overload_press": { + "candidate": 0, + "champion": 0, + "matches": 0 + }, + "press_and_pass": { + "candidate": 0, + "champion": 0, + "matches": 0 + }, + "press_trigger_flow": { + "candidate": 0, + "champion": 0, + "matches": 0 + }, + "score_aware_counter_flow": { + "candidate": 0, + "champion": 0, + "matches": 0 + }, + "tiki_taka_plus": { + "candidate": 5, + "champion": 0, + "matches": 1 + }, + "head-to-head": { + "candidate": 0, + "champion": 0, + "matches": 1 + } + }, + "fouls_rules_shown": [ + "defense_area_stoppage", + "crashing", + "excessive_dribbling", + "double_touch" + ], + "notes": "Matches are those in the run dir; fixtures reused from the ledger have no replay here. Positions and fouls exclude the goalkeeper and out-of-bounds (as pitch_maps.py)." +} \ No newline at end of file diff --git a/docs/img/match_timeline/decoy_and_overload_110-170.png b/docs/img/match_timeline/decoy_and_overload_110-170.png new file mode 100644 index 00000000..27aaf45c Binary files /dev/null and b/docs/img/match_timeline/decoy_and_overload_110-170.png differ diff --git a/docs/img/match_timeline/split_shape_100-160.png b/docs/img/match_timeline/split_shape_100-160.png new file mode 100644 index 00000000..0e6979e2 Binary files /dev/null and b/docs/img/match_timeline/split_shape_100-160.png differ diff --git a/docs/img/match_timeline/stall_split_shape_773-796.png b/docs/img/match_timeline/stall_split_shape_773-796.png new file mode 100644 index 00000000..950842a9 Binary files /dev/null and b/docs/img/match_timeline/stall_split_shape_773-796.png differ diff --git a/docs/img/motion_planning/control_schemes.png b/docs/img/motion_planning/control_schemes.png new file mode 100644 index 00000000..7fa092d2 Binary files /dev/null and b/docs/img/motion_planning/control_schemes.png differ diff --git a/docs/img/motion_planning/make_control_schemes.py b/docs/img/motion_planning/make_control_schemes.py new file mode 100644 index 00000000..57593355 --- /dev/null +++ b/docs/img/motion_planning/make_control_schemes.py @@ -0,0 +1,130 @@ +"""Draws control_schemes.png for docs/motion_planning_comparison.md: how move() reaches the three control +schemes (fpp, dwa, trajsample) through get_control_scheme, and what each one hands back. + +pixi run python docs/img/motion_planning/make_control_schemes.py +""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from diagram_style import Diagram # noqa: E402 + +# Every name a box shows; utama_core/tests/tools/test_diagram_names.py checks they still exist. +NAMES = [ + "move", + "get_control_scheme", + "CONTROL_SCHEME_MAP", + "MotionController", + "FastPathPlanningController", + "DWAController", + "DWATranslationController", + "DynamicWindowPlanner", + "TrajectorySamplingController", + "TrajectorySamplingPlanner", + "TwoDPID", + "get_pids", + "_path_to", + "RobotCommand", + "StrategyRunner", + "utama_core/skills/src/utils/move_utils.py", + "utama_core/motion_planning/src/common/control_schemes.py", + "utama_core/motion_planning/src/controllers/fastpathplanning.py", + "utama_core/motion_planning/src/controllers/dwa_controller.py", + "utama_core/motion_planning/src/controllers/trajsampling.py", + "utama_core/motion_planning/src/pid/pid.py", +] + +d = Diagram( + 180, + 104, + "Control schemes: how move() reaches the planners", + "move() calls the scheme's calculate once per robot per tick. pid is also registered but not drawn: it steers " + "straight at the target, with no obstacle planning.", + figsize=(14, 8.8), +) + +# --- entry and registry --------------------------------------------------------------------------- +d.box( + 18, + 54, + 34, + 28, + "move()", + "skills/src/utils/move_utils.py\ncalls calculate(game, robot_id,\ntarget, orientation) once,\nthen rotates the velocity\ninto the robot frame", + kind="decision", +) +d.box( + 56, + 54, + 34, + 28, + "get_control_scheme", + "common/control_schemes.py\nname -> class, in\nCONTROL_SCHEME_MAP\npicked per side by\nStrategyRunner (fpp)", + kind="buffer", +) +d.arrow((35, 54), (39, 54)) + +# --- the three planners --------------------------------------------------------------------------- +d.box( + 108, + 82, + 56, + 22, + "fpp: FastPathPlanningController", + "controllers/fastpathplanning.py\nlookahead carrot from _path_to,\nTwoDPID steers to the carrot;\nthe braking cap uses the true target", + kind="motion", +) +d.box( + 108, + 54, + 56, + 22, + "dwa: DWAController", + "controllers/dwa_controller.py\nDWATranslationController, through\nDynamicWindowPlanner, picks the velocity", + kind="motion", +) +d.box( + 108, + 28, + 56, + 22, + "trajsample: TrajectorySamplingController", + "controllers/trajsampling.py\nTrajectorySamplingPlanner.plan: bang-bang\ncandidates, the first collision-free one;\n" + "braked if the closing speed is too high", + kind="motion", + size=9.5, +) +d.arrow((73, 54), (80, 82)) +d.arrow((73, 54), (80, 54)) +d.arrow((73, 54), (80, 28)) + +# --- shared orientation --------------------------------------------------------------------------- +d.box(108, 6, 56, 10, "Orientation, all three", "pid/pid.py: get_pids, a PID to target_oren", kind="motion") + +# --- output --------------------------------------------------------------------------------------- +d.box( + 160, + 52, + 34, + 28, + "RobotCommand", + "global velocity from calculate,\nrotated into the robot frame;\nangular velocity as it is", + kind="output", +) +d.arrow((136, 82), (143, 62)) +d.arrow((136, 54), (143, 52)) +d.arrow((136, 28), (143, 42)) +d.arrow((136, 6), (160, 38)) + +d.key( + 4, + 36, + [ + ("decision", "skills: the caller of move()"), + ("buffer", "the registry"), + ("motion", "motion planning and control"), + ("output", "where the command leaves"), + ], +) +print(d.save(__file__, "control_schemes.png")) diff --git a/docs/img/motion_planning/summary.png b/docs/img/motion_planning/summary.png new file mode 100644 index 00000000..6f87f1d1 Binary files /dev/null and b/docs/img/motion_planning/summary.png differ diff --git a/docs/img/motion_planning/trajectories_crossing.png b/docs/img/motion_planning/trajectories_crossing.png new file mode 100644 index 00000000..b8b68190 Binary files /dev/null and b/docs/img/motion_planning/trajectories_crossing.png differ diff --git a/docs/img/motion_planning/trajectories_direct.png b/docs/img/motion_planning/trajectories_direct.png new file mode 100644 index 00000000..f142ad63 Binary files /dev/null and b/docs/img/motion_planning/trajectories_direct.png differ diff --git a/docs/img/motion_planning/trajectories_static_slalom.png b/docs/img/motion_planning/trajectories_static_slalom.png new file mode 100644 index 00000000..c357d4e2 Binary files /dev/null and b/docs/img/motion_planning/trajectories_static_slalom.png differ diff --git a/docs/img/pipeline/make_one_tick.py b/docs/img/pipeline/make_one_tick.py new file mode 100644 index 00000000..5ad5486a --- /dev/null +++ b/docs/img/pipeline/make_one_tick.py @@ -0,0 +1,152 @@ +"""Draws one_tick.png for docs/pipeline_method.md and the README: one tick of StrategyRunner, from the data +sources to the robots, with the class or module behind each box. + +pixi run python docs/img/pipeline/make_one_tick.py +""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from diagram_style import LOOP, Diagram # noqa: E402 + +# Every name a box shows; utama_core/tests/tools/test_diagram_names.py checks they still exist. +NAMES = [ + "StrategyRunner", + "CONTROL_FREQUENCY", + "TIMESTEP", + "CustomReferee", + "VisionReceiver", + "RefereeMessageReceiver", + "get_robots_responses", + "RawVisionData", + "RefereeData", + "RobotResponse", + "PositionRefiner", + "CameraCombiner", + "VelocityRefiner", + "RobotInfoRefiner", + "RefereeRefiner", + "GameFrame", + "GameHistory", + "Game", + "RefereeOverride", + "MatchStats", + "RobotCommand", + "utama_core/run/strategy_runner.py", + "utama_core/data_processing/receivers", + "utama_core/entities/game", + "utama_core/engine/strategy.py", + "utama_core/motion_planning", + "utama_core/team_controller/src/controllers", +] + +d = Diagram( + 140, + 115, + "One tick of StrategyRunner", + "utama_core/run/strategy_runner.py, 60 times a second (CONTROL_FREQUENCY). In rsim both teams run in the " + "same runner: each side refines and steps in turn.", +) + +# --- sources ------------------------------------------------------------------------------------- +d.box(24, 99, 36, 10, "Vision", "SSL Vision over UDP (real, grSim)\nor the rsim observation", kind="source") +d.box(70, 99, 36, 10, "Robots", "radio feedback (real)\nor the simulator's responses", kind="source") +d.box(116, 99, 36, 10, "Referee", "GameController over UDP, or\nCustomReferee stepped in process", kind="source") + +# --- receivers ----------------------------------------------------------------------------------- +d.receiver(24, 84, 34, 7, "VisionReceiver", "data_processing/receivers/, own thread") +d.receiver(70, 84, 34, 7, "get_robots_responses()", "team_controller/ RobotController") +d.receiver(116, 84, 34, 7, "RefereeMessageReceiver", "or CustomReferee.step (rsim)") +for x in (24, 70, 116): + d.arrow((x, 93.6), (x, 88)) + +# --- buffers ------------------------------------------------------------------------------------- +d.box(24, 71, 34, 6.5, "Vision buffers", "deque(maxlen=1) per camera", kind="buffer", size=10) +d.box(116, 71, 34, 6.5, "Referee buffer", "deque(maxlen=1): only the latest", kind="buffer", size=10) +d.arrow((24, 80.4), (24, 74.6), "RawVisionData", lx=1.2) +d.arrow((116, 80.4), (116, 74.6), "RefereeData", lx=1.2) + +# --- refiners on the main loop ------------------------------------------------------------------- +Y = 50 +d.box( + 24, + Y, + 30, + 15, + "PositionRefiner", + "CameraCombiner merges the\ncameras, then a Kalman\nfilter per robot and ball", + "refiner", +) +d.box(58, Y, 22, 15, "VelocityRefiner", "velocities and\naccelerations,\nfrom GameHistory", kind="refiner") +d.box( + 87, + Y, + 24, + 15, + "RobotInfoRefiner", + "has-ball from our IR,\nor inferred from positions;\nthe enemy's in rsim", + "refiner", +) +d.box(116, Y, 22, 15, "RefereeRefiner", "command, stage,\nscores, designated\nposition", kind="refiner") +d.arrow((24, 67.6), (24, 57.6), "latest frame of each camera", lx=1.2) +d.arrow((70, 80.4), (87, 57.6), "RobotResponse\n(IR has-ball)", lx=1.5) +d.arrow((116, 67.6), (116, 57.6), "RefereeData", lx=1.2) +for a, b in ((39.2, 46.8), (69.2, 74.8), (99.2, 104.8)): + d.main((a, Y), (b, Y)) +d.note(135.2, 40, "GameFrame", colour=LOOP, weight="bold", ha="left", va="center", rotation=90, fontsize=9) + +# --- state --------------------------------------------------------------------------------------- +d.box(116, 30, 28, 11, "Game", "latest GameFrame + GameHistory\n(entities/game/), immutable", kind="state") +d.loop([(127.2, Y), (134, Y), (134, 30), (130.6, 30)]) +d.box(116, 13, 26, 9, "Recorded", "replay writer, MatchStats,\ndashboard vision stream", kind="output", size=10) +d.arrow((116, 24.4), (116, 17.6), ls="--") + +# --- decision ------------------------------------------------------------------------------------ +d.box( + 77, + 30, + 38, + 16, + "Strategy.step", + "engine/strategy.py. Restart, STOP or timeout:\nRefereeOverride places the robots.\nOtherwise the partitioner " + "splits\nthe robots between tactics (tactics/),\nwhich call skills (skills/)", + kind="decision", +) +d.main((101.4, 30), (96.6, 30)) +d.box( + 34, + 30, + 26, + 16, + "MotionController", + "motion_planning/: fpp (default),\ndwa or trajsample turn each\ntarget into a velocity,\navoiding robots and the\n" + "ball's keep-out", + kind="motion", +) +d.arrow((57.4, 30), (47.6, 30)) +d.note(52.5, 27.6, "targets", style="italic", ha="center", va="top") + +# --- output -------------------------------------------------------------------------------------- +d.box(34, 13, 28, 8, "RobotController", "team_controller/src/controllers/", kind="output", size=10) +d.arrow((34, 21.8), (34, 17.2), "RobotCommand per robot", lx=1.2) +for x, w, name in ((12, 13, "rsim"), (27, 13, "grSim"), (54, 16, "Real robots")): + d.box(x, 3.5, w, 4.5, name, kind="output", size=9.5) +d.arrow((26, 8.8), (12, 5.9)) +d.arrow((30, 8.8), (27, 5.9)) +d.arrow((42, 8.8), (54, 5.9)) +d.loop([(19.8, 13), (1.5, 13), (1.5, Y), (8.8, Y)]) +d.note(2.6, 31, "next tick:\noff rsim, sleep\nto TIMESTEP", colour=LOOP, ha="left", va="center") + +d.key( + 75, + 17.6, + [ + ("receiver", "receiver"), + ("refiner", "refiner"), + ("state", "game state"), + ("decision", "strategy layer"), + ("motion", "motion"), + ], +) +print(d.save(__file__, "one_tick.png")) diff --git a/docs/img/pipeline/one_tick.png b/docs/img/pipeline/one_tick.png new file mode 100644 index 00000000..6853808b Binary files /dev/null and b/docs/img/pipeline/one_tick.png differ diff --git a/docs/img/plan_snapshot/kickoff_keep_out.png b/docs/img/plan_snapshot/kickoff_keep_out.png new file mode 100644 index 00000000..c602dbec Binary files /dev/null and b/docs/img/plan_snapshot/kickoff_keep_out.png differ diff --git a/docs/img/plan_trace/kickoff_keep_out.png b/docs/img/plan_trace/kickoff_keep_out.png new file mode 100644 index 00000000..f45e3b1f Binary files /dev/null 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a/docs/img/sim_realism/make_rsim_tick.py b/docs/img/sim_realism/make_rsim_tick.py new file mode 100644 index 00000000..81824232 --- /dev/null +++ b/docs/img/sim_realism/make_rsim_tick.py @@ -0,0 +1,127 @@ +"""Draws rsim_tick.png for docs/sim_realism.md: one tick of rsim, where the seeded command-side noise and the +seeded vision noise sit between the strategy layer and the C++ physics. + +pixi run python docs/img/sim_realism/make_rsim_tick.py +""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from diagram_style import Diagram # noqa: E402 + +# Every name a box shows; utama_core/tests/tools/test_diagram_names.py checks they still exist. +NAMES = [ + "StrategyRunner", + "CustomReferee", + "RSimController", + "SSLStandardEnv", + "SimNoise", + "CommandDelay", + "KickSpeedNoise", + "KickAngleNoise", + "MotionLimits", + "RSimSSL", + "RSimSubprocessWrapper", + "set_physics_params", + "_get_commands", + "_frame_to_observations", + "vision_rng", + "utama_core/run/strategy_runner.py", + "utama_core/custom_referee/custom_referee.py", + "utama_core/team_controller/src/controllers/sim/rsim_controller.py", + "utama_core/rsoccer_simulator/src/ssl/envs/standard_ssl.py", + "utama_core/rsoccer_simulator/src/Utils/sim_noise.py", + "utama_core/rsoccer_simulator/src/Simulators/rsim.py", + "utama_core/rsoccer_simulator/src/Simulators/robosim/robosim_wrapper.py", + "utama_core/rsoccer_simulator/src/Simulators/robosim/robosim_subprocess.py", + "vendor/rSim/src/robosim", +] + +d = Diagram( + 160, + 110, + "One rsim tick: where the seeded noise goes", + "Commands pass the command-side noise on the way in; observations pass the vision noise on the way out. " + "The physics is the fork in vendor/rSim.", + figsize=(14, 10), +) + +# --- strategy layer (left) ------------------------------------------------------------------------ +d.box( + 24, + 62, + 36, + 34, + "StrategyRunner", + "run/strategy_runner.py: _run_step\nstrategies, tactics, MotionController\n(strategy/, tactics/, motion_planning/)\n" + "one RobotCommand per robot,\nsent through RSimController", + kind="decision", +) +d.box( + 24, 22, 36, 14, "CustomReferee", "custom_referee/custom_referee.py\nstepped in process each tick", kind="decision" +) +d.arrow((18, 45), (18, 29), "frames", lx=-1.0, ha="right") +d.arrow((30, 29), (30, 45), "referee data", lx=1.0) + +# --- command side (top) --------------------------------------------------------------------------- +d.box( + 74, + 84, + 50, + 22, + "Command-side noise", + "SSLStandardEnv, per tick, in order:\nCommandDelay: latency in whole ticks\n_get_commands: KickSpeedNoise, KickAngleNoise\n" + "MotionLimits: top speed, acceleration, braking", + kind="refiner", +) +d.arrow((42, 76), (49, 86)) + +# --- physics (top right) -------------------------------------------------------------------------- +d.box( + 126, + 84, + 40, + 22, + "rsim physics (C++)", + "RSimSSL, RSimSubprocessWrapper\nrobosim_subprocess.py child process\nvendor/rSim/src/robosim", + kind="source", +) +d.arrow((99, 86), (106, 86)) + +# --- vision side (right) -------------------------------------------------------------------------- +d.box( + 126, + 36, + 40, + 24, + "Vision-side noise", + "_frame_to_observations, per tick:\ngaussian offsets on every detection\nvanishing: detections dropped\n" + "SimNoise.vision_rng: a fresh seeded stream", + kind="refiner", +) +d.arrow((126, 73), (126, 48), "frame", lx=1.0) +d.arrow((106, 36), (42, 52), "observations", lx=0.0, ly=2.0) + +# --- per match, set at reset (bottom middle) ------------------------------------------------------ +d.box( + 74, + 18, + 56, + 18, + "Drawn once per match", + "SimNoise.draw: each robot's latency and its kick\nspeed and angle offsets\n" + "ball friction and substeps: set_physics_params,\nonce, when SSLStandardEnv is built", + kind="refiner", +) + +d.key( + 112, + 21, + [ + ("source", "outside the process: the physics"), + ("refiner", "noise on commands or observations"), + ("decision", "strategy layer and referee"), + ], +) +print(d.save(__file__, "rsim_tick.png")) diff --git a/docs/img/sim_realism/rsim_tick.png b/docs/img/sim_realism/rsim_tick.png new file mode 100644 index 00000000..5980cc2d Binary files /dev/null and b/docs/img/sim_realism/rsim_tick.png differ diff --git a/docs/img/strategy/make_tactic_kernel.py b/docs/img/strategy/make_tactic_kernel.py new file mode 100644 index 00000000..d64c1472 --- /dev/null +++ b/docs/img/strategy/make_tactic_kernel.py @@ -0,0 +1,180 @@ +"""Draws tactic_kernel.png for docs/STRATEGY_DEVELOPMENT.md ("The tactic-kernel model"): the classes that make up +one team's Strategy, who holds or calls whom, and the path a tick takes from StrategyRunner down to the skills. + +pixi run python docs/img/strategy/make_tactic_kernel.py +""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from diagram_style import LOOP, Diagram # noqa: E402 + +# Every name a box shows; utama_core/tests/tools/test_diagram_names.py checks they still exist. +NAMES = [ + "StrategyRunner", + "AbstractStrategy", + "Strategy", + "RefereeOverride", + "_TacticSlot", + "Partitioner", + "TickContext", + "MatchLog", + "Tactic", + "BaseTactic", + "AbstractRobotController", + "DEFAULT_COMMITMENT_DEADLINE_S", + "utama_core/run/strategy_runner.py", + "utama_core/engine/abstract_strategy.py", + "utama_core/engine/strategy.py", + "utama_core/engine/referee_override.py", + "utama_core/engine/referee_reset.py", + "utama_core/engine/tactic.py", + "utama_core/engine/context.py", + "utama_core/engine/match_log.py", + "utama_core/strategy/split_shape.py", + "utama_core/tactics", + "utama_core/skills/src", +] + +d = Diagram( + 150, + 112, + "The tactic-kernel model: one team's Strategy", + "Each tick the Strategy re-partitions the outfield robots across its tactics. The blue line is the path " + "one tick takes.", + figsize=(14, 10.5), +) + +L, M, R = 22, 80, 130 + +# --- top: the driver and the wrapper ------------------------------------------------------------- +d.box(L, 98, 38, 10, "StrategyRunner", "run/strategy_runner.py,\nonce per side per tick", kind="source") +d.box( + M, + 98, + 46, + 13, + "AbstractStrategy", + "engine/abstract_strategy.py: wraps the Strategy\n" + "a factory built; robots missing from\nthe command map get execute_default_action", + kind="decision", +) +d.main((41, 98), (57, 98)) +d.note(49, 100.8, "step()", colour=LOOP, ha="center", style="italic") + +# --- the scheduler ------------------------------------------------------------------------------- +d.box( + M, + 76, + 46, + 17, + "Strategy.tick(game)", + "engine/strategy.py. Barrier reset at a restart,\npins committed slots, filters applicable(),\n" + "validates the partition, ticks each slot", + kind="decision", +) +d.main((M, 91.5), (M, 84.5)) +d.box( + L, + 76, + 38, + 15, + "_TacticSlot, one per tactic id", + "tactic, mem, assigned_robots,\ncommitted_ticks. Cleared at each\nrestart barrier. Deadline:\n" + "DEFAULT_COMMITMENT_DEADLINE_S (15 s)", + kind="state", +) +d.arrow((M - 23, 76), (L + 19, 76), colour="#5c5c5c", ls="--") +d.box( + R, + 76, + 36, + 13, + "RefereeOverride", + "engine/referee_override.py:\nrestart or STOP, robots\nplaced before tactics tick", + kind="decision", +) +d.arrow((M + 23, 76), (R - 18, 76)) + +# --- the partition and the context --------------------------------------------------------------- +d.box( + M, + 54, + 46, + 12, + "Partitioner", + "strategy/split_shape.py and similar: splits\nthe free robots over the applicable tactics", + kind="decision", +) +d.main((M, 67.5), (M, 60)) +d.note(M + 2, 63.8, "free robots", colour=LOOP, ha="left", va="center", style="italic") +d.box( + R, + 54, + 36, + 12, + "TickContext", + "motion_controller, match_log:\npassed to every tactic tick", + kind="buffer", +) + +# --- tactics ------------------------------------------------------------------------------------- +d.box( + M, + 32, + 46, + 14, + "Tactic (tactics/)", + "tick(game, ctx, robot_ids, mem)\n-> commands, new mem. BaseTactic\ngives the defaults. tag: colour only", + kind="decision", +) +d.main((M, 47.5), (M, 39)) +d.arrow((R - 18, 48), (M + 23, 36), colour="#5c5c5c", ls="--") +d.box(R, 30, 36, 12, "MatchLog", "intention, trace, referee events;\nto_jsonl after the match", kind="state") +d.arrow((R, 48), (R, 36), "match_log", lx=1.4, ly=0, ha="left", colour="#5c5c5c", ls="--") + +# --- skills and output --------------------------------------------------------------------------- +d.box( + M, + 10, + 46, + 12, + "Skills (skills/src/)", + "go_to_ball, go_to_point, ... each\nreturns a RobotCommand", + kind="motion", +) +d.main((M, 25), (M, 16)) +d.box( + L, + 10, + 38, + 12, + "AbstractRobotController", + "team_controller/: add_robot_commands,\nthen send_robot_commands", + kind="output", +) +d.arrow((M - 23, 10), (L + 19, 10), "RobotCommand", lx=0, ly=2.6) + +d.note( + 3, + 60, + "One writer per robot: the partition is set\nonce per tick, before any tactic runs. A restart\n" + "barrier clears every slot's mem and commitment.", + ha="left", + va="top", +) + +d.key( + 108, + 22, + [ + ("source", "driver (run/)"), + ("decision", "strategy layer"), + ("buffer", "per-tick context"), + ("state", "slot and log state"), + ("motion", "skills"), + ("output", "robot controller"), + ], +) +print(d.save(__file__, "tactic_kernel.png")) diff --git a/docs/img/strategy/tactic_kernel.png b/docs/img/strategy/tactic_kernel.png new file mode 100644 index 00000000..7eb1cf7d Binary files /dev/null and b/docs/img/strategy/tactic_kernel.png differ diff --git a/docs/img/team_controller/make_real_robot_path.py b/docs/img/team_controller/make_real_robot_path.py new file mode 100644 index 00000000..3cd4f6ed --- /dev/null +++ b/docs/img/team_controller/make_real_robot_path.py @@ -0,0 +1,144 @@ +"""Draws real_robot_path.png for utama_core/team_controller/README.md ("Real robot path"): one tick of a real +match, from the SSL-Vision and referee multicast into the refiners, the Strategy and MotionController, and out to +the robots over the serial radio, with the feedback back into RobotInfoRefiner. + +pixi run python docs/img/team_controller/make_real_robot_path.py +""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from diagram_style import Diagram # noqa: E402 + +# Every name a box shows; utama_core/tests/tools/test_diagram_names.py checks they still exist. +NAMES = [ + "VisionReceiver", + "RefereeMessageReceiver", + "PositionRefiner", + "CameraCombiner", + "VelocityRefiner", + "RobotInfoRefiner", + "RefereeRefiner", + "GameFrame", + "Strategy", + "MotionController", + "RealRobotController", + "RobotResponse", + "VISION_PORT", + "REFEREE_PORT", + "PORT", + "BAUD_RATE", + "utama_core/config/settings.py", + "utama_core/data_processing/receivers/vision_receiver.py", + "utama_core/data_processing/receivers/referee_receiver.py", + "utama_core/data_processing/refiners/position.py", + "utama_core/data_processing/refiners/filters/kalman.py", + "utama_core/data_processing/refiners/velocity.py", + "utama_core/data_processing/refiners/robot_info.py", + "utama_core/data_processing/refiners/referee.py", + "utama_core/entities/game/game_frame.py", + "utama_core/engine/strategy.py", + "utama_core/motion_planning", + "utama_core/team_controller/src/controllers/real/real_robot_controller.py", +] + +d = Diagram( + 150, + 112, + "Real robot path: one tick of a real match", + "StrategyRunner wires it: receivers fill one-slot buffers, refiners build the GameFrame, and the real " + "controller sends commands over the serial radio.", + figsize=(14, 10.5), +) + +VX, RX = 26, 124 # the vision column and the referee column + +# --- network and receivers ----------------------------------------------------------------------- +d.box(VX, 100, 46, 9, "SSL-Vision (multicast)", "224.5.23.2:10006 (VISION_PORT)", kind="source") +d.box(RX, 100, 46, 9, "Referee, GameController", "224.5.23.1:10003 (REFEREE_PORT)", kind="source") +d.receiver(VX, 88, 40, 7, "VisionReceiver", "data_processing/receivers/") +d.receiver(RX, 88, 40, 7, "RefereeMessageReceiver", "data_processing/receivers/") +d.arrow((VX, 95.5), (VX, 91.5)) +d.arrow((RX, 95.5), (RX, 91.5)) + +# --- buffers ------------------------------------------------------------------------------------- +d.box(VX, 76, 36, 6.5, "Vision buffers", "deque(maxlen=1) per camera", kind="buffer", size=10) +d.box(RX, 76, 36, 6.5, "Referee buffer", "deque(maxlen=1): only the latest", kind="buffer", size=10) +d.arrow((VX, 84.5), (VX, 79.3), "RawVisionData", lx=1.4) +d.arrow((RX, 84.5), (RX, 79.3), "RefereeData", lx=1.4) + +# --- refiners: the main path runs along this row ------------------------------------------------- +Y = 58 +d.box( + VX, + Y, + 32, + 16, + "PositionRefiner", + "CameraCombiner merges the\ncameras; a Kalman filter\nper robot and ball", + "refiner", +) +d.box(62, Y, 20, 16, "VelocityRefiner", "velocities and\naccelerations,\nfrom GameHistory", kind="refiner") +d.box( + 92, + Y, + 22, + 16, + "RobotInfoRefiner", + "has-ball from the IR\nfeedback; from vision for\nuntrusted robots", + kind="refiner", +) +d.box(RX, Y, 28, 16, "RefereeRefiner", "command, stage,\nscores, designated\nposition", kind="refiner") +d.arrow((VX, 72.8), (VX, Y + 8.4), "latest frame of each camera", lx=1.4) +d.arrow((RX, 72.8), (RX, Y + 8.4)) +for a, b in ((42, 52), (72, 81), (103, RX - 14)): + d.main((a, Y), (b, Y)) + +# --- game frame, strategy, motion ---------------------------------------------------------------- +d.box(RX, 40, 30, 9, "GameFrame", "entities/game/game_frame.py", kind="state") +d.main((RX, Y - 8), (RX, 44.5)) +d.box(RX, 24, 30, 12, "Strategy", "engine/strategy.py: the tactics\npick a target per robot", kind="decision") +d.main((RX, 35.5), (RX, 30.4)) +d.box(82, 24, 26, 12, "MotionController", "motion_planning/: a target\nbecomes a velocity", kind="motion") +d.arrow((RX - 15, 24), (82 + 13, 24), "targets", lx=0, ly=2.4, ha="center") + +# --- output and feedback ------------------------------------------------------------------------- +d.box( + 30, + 24, + 40, + 12, + "RealRobotController", + "team_controller/src/controllers/real/\nserial radio on settings.PORT, 115200 baud", + kind="output", + size=10, +) +d.arrow((82 - 13, 24), (30 + 20, 24), "RobotCommand", lx=0, ly=2.4, ha="center") +d.arrow((30 + 10, 30.4), (92 - 8, Y - 8.4), ls="--") +d.note(60, 42, "RobotResponse (has ball)", ha="right", style="italic") +d.box(30, 7, 40, 7, "Robots", "over the serial radio", kind="source") +d.arrow((30, 18.4), (30, 10.9)) + +d.key( + 101, + 17, + [ + ("source", "outside the process"), + ("receiver", "receiver"), + ("buffer", "one-slot buffer"), + ("refiner", "refiner"), + ], +) +d.key( + 126, + 17, + [ + ("state", "game state"), + ("decision", "strategy layer"), + ("motion", "motion planning"), + ("output", "robot controller"), + ], + title="", +) +print(d.save(__file__, "real_robot_path.png")) diff --git a/docs/img/team_controller/real_robot_path.png b/docs/img/team_controller/real_robot_path.png new file mode 100644 index 00000000..68dc8630 Binary files /dev/null and b/docs/img/team_controller/real_robot_path.png differ diff --git a/docs/motion_planning_comparison.md b/docs/motion_planning_comparison.md index 7840e294..b2a654e6 100644 --- a/docs/motion_planning_comparison.md +++ b/docs/motion_planning_comparison.md @@ -11,6 +11,7 @@ pixi run python tools/motion_planning_benchmark.py --scenarios direct static_sla pixi run python tools/motion_planning_benchmark.py --repeats 5 # expose instability pixi run python tools/motion_planning_benchmark.py --schemes fpp trajsample --scenarios mirror_swap --repeats 3 pixi run python tools/motion_planning_benchmark.py --list-scenarios +pixi run python tools/motion_planning_benchmark.py --scenarios direct --figures docs/img/motion_planning # PNG figures too ``` Runs are sequential (concurrent cells would contend for CPU and corrupt latency numbers). @@ -18,6 +19,17 @@ Geometry, colours/sides, rsim noise and the sampler seed are fixed; `--repeats` deterministic setup stays repeatable. Exits non-zero if any cell fails (after writing reports) unless `--allow-failures`; `--output-dir PATH` redirects output. +## Control schemes + +Every scheme is a `MotionController`, and `move()` calls its `calculate` once per robot per tick. The +scheme name picks the class through `CONTROL_SCHEME_MAP`; `StrategyRunner` resolves it per side from +`strategy.control_scheme` (default `fpp`, and `pid` is also registered). Each `calculate` returns a +global velocity and an angular velocity, which `move()` rotates into the robot frame. + +![Control schemes: move() calls get_control_scheme, which picks fpp, dwa or trajsample; each returns a velocity to RobotCommand](img/motion_planning/control_schemes.png) + +Drawn by `docs/img/motion_planning/make_control_schemes.py`; rerun it when a name in it changes. + ## Scenarios | Name | Exercises | Timeout | Tolerance | @@ -59,6 +71,54 @@ The four crowd scenarios (`ball_scrum` to `kickoff_reset`) and the six match-der (`box_clearing_scrum` to `trailing_intercept`) are built on where real match congestion and tactical restarts occur. +### FastPathPlanner (`fpp`) decisions per tick + +Each tick, `FastPathPlanner._path_to` turns the robot's target into a carrot point for the PID. The +flowchart is that call's decision order. `tools/plan_snapshot.py` draws the carrot and the planner's +inputs for one logged tick. `tools/plan_trace.py` runs the same `_path_to` on every tick of a window and +draws how the carrot moved against the robot's path, which a single tick cannot show. Robot positions are the replay's, not re-simulated, so it shows what the current planner would +do from each recorded state; the kick-off example is in [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md), +"Debugging a foul or failure". + +```mermaid +flowchart TD + start["_path_to(game, robot_id, target)
fastpathplanning/planner.py"] + obs["Obstacles: moving robots within LOOK_AHEAD_RANGE as ghost segments, plus static walls
_get_obstacles, _refresh_obstacle_cache"] + filt["Drop boundary walls if the target is off the field;
drop defense-area walls if _enemy_defense_area_retrieval_exempt"] + tgt["Target: project out of enemy box unless exempt, then sanitize_target
ball-adjacent walls exempt when the target is at the ball"] + lv["Routing obstacles: drop each robot the robot is inside the clearance of
and moving away from (_leaving), so it can drive straight out"] + line{"Straight run to target collides?
check_segment, collides"} + det["Detour via subgoal on either side, with side hysteresis (DETOUR_SWITCH_MARGIN)
recompute-skip reuses the last detour if target and robot barely moved"] + carrot["Carrot: smooth_path, PROJECTION_DISTANCE along the first segment,
clamped to clearance, averaged with the next segment"] + box{"Carrot inside enemy box and not exempt?"} + ball{"STOP, or the other team's restart?
_keeps_off_ball"} + out{"Robot outside BALL_KEEP_OUT_DISTANCE?"} + circ["detour_around_circle round the ball (0.8 m)"] + app{"Robot inside keep-out and carrot closer to ball?
_approaches"} + push["Push out along the ray from the ball to BALL_KEEP_OUT_DISTANCE + _PUSH_OUT_MARGIN"] + done["Carrot returned to FastPathPlanningController, then TwoDPID"] + + start --> obs + obs --> filt + filt --> tgt + tgt --> lv + lv --> line + line -- "yes" --> det + line -- "no" --> carrot + det --> carrot + carrot --> box + box -- "yes, project out" --> ball + box -- "no" --> ball + ball -- "no" --> done + ball -- "yes" --> out + out -- "yes" --> circ + out -- "no" --> app + app -- "yes" --> push + app -- "no" --> done + circ --> done + push --> done +``` + ### Trajectory Sampling (`trajsample`) Architecture & Optimizations Following TIGERs Mannheim's approach (Sumatra), `trajsample` plans directly in continuous trajectory space @@ -99,6 +159,34 @@ fraction (robot-seconds below 0.05 m/s while unreached); mean/p95/max `MotionController.calculate()` latency (first 5 calls excluded for Numba warm-up, `--timing-warmup-calls`); max speed and acceleration. +## Figures + +`--figures DIR` also writes `trajectories_.png` (the pitch with each scheme's paths, then speed +and clearance over time) and `summary.png` (one dot per scheme and scenario for each metric). It is off +by default; turning it on records per-tick robot state during the runs and changes nothing else. Figures +show the first repeat of each cell. Three scenarios from one run are committed in `docs/img/motion_planning/`: + +```bash +pixi run python tools/motion_planning_benchmark.py --scenarios direct static_slalom crossing --allow-failures --figures docs/img/motion_planning +``` + +![Summary: one dot per scheme and scenario](img/motion_planning/summary.png) + +![static_slalom trajectories, speed and clearance](img/motion_planning/trajectories_static_slalom.png) + +![crossing trajectories, speed and clearance](img/motion_planning/trajectories_crossing.png) + +In this run (all three schemes, one repeat): in `static_slalom`, `dwa`'s path swerves round the first two +obstacles and ends touching the lower one (clearance reaches 0 m: its failed cell), while `fpp` and +`trajsample` keep a straight line with clearance of about 0.12 m. In `crossing`, `dwa` ends at 0 m clearance +from the crossing opponent, `trajsample` keeps about 0.9 m or more but bends wide and finishes in about 5 s, +and `fpp` passes at about 0.2 m and finishes in about 3.3 s. In `direct`, `fpp` and `trajsample` both reach the +2 m/s speed limit, while `dwa` accelerates more slowly and never reaches it. Where two schemes' paths coincide +(`direct`, and the straight part of `static_slalom`), the later scheme is drawn on top and hides the earlier one. + +The three-scenario command took about 8 s of wall time, including writing the figures. Its numbers are +not the table in the results section above, which was made at an earlier revision. + ## Reading it Pass rate first — a fast planner that collides or doesn't converge isn't a winner. Then: lower diff --git a/docs/pipeline_method.md b/docs/pipeline_method.md index 7649e7e4..be05697c 100644 --- a/docs/pipeline_method.md +++ b/docs/pipeline_method.md @@ -1,6 +1,9 @@ # Pipeline method -![Dataflow Diagram](../assets/images/pipeline_new.drawio.png) +![One tick of StrategyRunner](img/pipeline/one_tick.png) + +Drawn by `docs/img/pipeline/make_one_tick.py`; rerun it when a box's name changes. The same tick as a +Mermaid diagram with every module path: [architecture.md](architecture.md#one-tick). How vision, robot and referee data become one `Game` state, and how one tick turns that state into robot commands. Everything below runs inside `StrategyRunner` @@ -33,6 +36,30 @@ Friendly and enemy robots expose the same fields, estimated from different sourc The uniform `GameTimeline`/`Predictions` interface (pluggable per-property predictors) from earlier drafts was never built; predictions are ad hoc per call site. +### Inside PositionRefiner + +Camera combining and the Kalman filters (`data_processing/refiners/position.py`, +`data_processing/refiners/filters/kalman.py`). Smoothing and vanished-robot imputation are +separate switches; both run on the same per-robot filters. + +```mermaid +flowchart TD + cams["Raw vision, one RawVisionData per camera"] + comb["CameraCombiner.combine_cameras
_avg_robots per robot id, _combine_balls_by_proximity,
_bounds_check against VisionBounds"] + vd["VisionData
combined robots and balls"] + sw{"smooth_positions?"} + kf["KalmanFilter per robot, KalmanFilterBall
filters/kalman.py"] + imp{"impute_vanished?"} + vanish["_include_vanished_robots
Kalman predict for robots missing from vision"] + game["Game, GameFrame
entities/game/game.py"] + + cams --> comb --> vd --> sw + sw -->|yes| kf --> imp + sw -->|no| imp + imp -->|yes| vanish --> game + imp -->|no| game +``` + ## One tick `CustomReferee` steps first (when in-process), then each side refines its frame, the replay diff --git a/docs/pitch_zones.md b/docs/pitch_zones.md index bd6de627..7266d611 100644 --- a/docs/pitch_zones.md +++ b/docs/pitch_zones.md @@ -36,8 +36,7 @@ The centre is |y| ≤ 1 m (the box's half width). Left and right turn with the t attacking toward +x, its left wing is y > 1 m; for a team attacking toward −x (`my_team_is_right`), its left wing is y < −1 m. So the two teams' left wings are on opposite touch lines. -The scenario bench tags each start with its third and lane (`start.start_tags`, `--where`); -no tactic uses the lanes yet, and one that needs them should use these boundaries. +No tactic uses the lanes yet; one that needs them should use these boundaries. **Ball side / far side**: the wing the ball is on, and the wing opposite it. When the ball is in the centre, neither applies. **Weak side** is not the same thing: in `SwitchOfPlayTactic` diff --git a/docs/roadmap.md b/docs/roadmap.md index f909e2f2..051bdeb2 100644 --- a/docs/roadmap.md +++ b/docs/roadmap.md @@ -49,7 +49,7 @@ resolved, replace it with a one-line pointer under "Done". `StallEvent`s across N seeded fuzzed matches. **Built:** stall watchdog + `--strict`, commitment deadline, restart fuzzer, pure `Game` builder. **Not built:** CI job running the strict seeded stall gate. - - *Outer loop, fast half (scenario bench, numbers vs a committed baseline, non-blocking):* + - *Outer loop, fast half (scenario bench, numbers vs a committed baseline, non-blocking; **retired 2026-10-17**, code and banks deleted, history kept):* seeded start state + 15-30s horizon, scored as paired differentials by calibrated proxy metrics. **Built (v1):** `start.py`, 4 hand-authored anchors, `scenario_harvester.py` (restart transitions, trust gate `stall_events == []`), @@ -58,7 +58,7 @@ resolved, replace it with a one-line pointer under "Done". flag; `--repeats K` jittered starts give the seed noise rsim's determinism hid (the screen's default pool was the champion itself, so every live scenario came out DETERMINED); `--against-results` compares against an earlier run's JSON, for A/B - across commits. Banks in `utama_core/scenario_bench/banks/` are committed (v3 onward; v1/v2 predate + across commits. Banks in `utama_core/scenario_bench/banks/` were committed (v3 onward; v1/v2 predate the rulebook referee fixes and stay local). **Bank v4** (173 scenarios) = v3 (from RR `tournament_20260928_132051`) + RR `tournament_20260928_125725`, each harvested, screened (informative + noisy kept), then merged dropping near-duplicate starts (same @@ -91,7 +91,8 @@ resolved, replace it with a one-line pointer under "Done". lost-play weakness subset, event-triggered open-play harvesting, bank versioning/lifecycle. - *Outer loop, slow half (ladder, acceptance gate):* candidate vs a frozen reference pool of 4-5 strategies, both colours, K fuzz seeds, Elo anchored to the pool; full 600s - matches only vs the top of the pool. **Not built.** + matches only vs the top of the pool. **Ladder built (2026-10-09) and retired 2026-10-17 in + favour of `tools/evaluation/evaluate.py`** (fixed pool `pool_v1`, paired sequential test). - *Metrics — derive, don't invent:* `MatchStats` has both-sided turnovers, completed passes, attacking-third entries, possession-under-pressure, restart-to-entry. The first correlation study (`tools/metric_correlation.py`, `git show 24ef2c3f:benchmark_results/ @@ -100,14 +101,15 @@ resolved, replace it with a one-line pointer under "Done". Caveat: raw `turnovers` is mostly nearest-robot flicker on contested balls (1990 raw vs 1090 real losses over 231 matches, `git show 24ef2c3f:benchmark_results/turnover_breakdown_20260923_211717.md`); prefer `summary.json["ball_losses"]["real_losses"]`. - Goodhart guard: if the bench improves and the ladder doesn't, retire the proxy. + Goodhart guard: if a screen improves and evaluate.py doesn't, retire the proxy. - *Compute discipline:* paired comparison on common seeds, sequential stopping, short sampled horizons over long matches. - - **Not built:** one `evaluate --budget` entry point (contracts + bench at low - budget, ladder at high) that also writes `docs/strategies.md`. - - Build order: calibration tournament → bench on harvested states → ladder. + - **Partly done (2026-10-17):** one entry point, `tools/evaluation/evaluate.py `, + without `--budget` (it stops by itself). Not built: contracts, and writing + `docs/strategies.md`. + - Build order: calibration tournament → bench on harvested states (retired 2026-10-17) → ladder (since retired; see evaluate.py). -2a. **Strategy evaluation v2 (direction agreed 2026-10-09; ladder built).** Builds the ladder +2a. **Strategy evaluation v2 (direction agreed 2026-10-09; ladder built, then retired 2026-10-17 for `evaluate.py`).** Builds the ladder above and settles how matches vary. - *Ladder:* a candidate plays a fixed reference pool (the current top 4-5) in several sampled worlds, each world played twice with the teams swapped so luck cancels, both @@ -115,7 +117,8 @@ resolved, replace it with a one-line pointer under "Done". full round-robin stays as the occasional full refresh after shared-code changes. Pure Elo matchmaking over the whole league is not the plan: strategies counter each other, and the round-robin's results table is what shows that. - **Built (2026-10-09):** `tools/evaluation/ladder.py`. Until worlds vary, a pairing has 8 + **Built (2026-10-09), retired 2026-10-17:** `tools/evaluation/ladder.py` (replaced by + `tools/evaluation/evaluate.py`). Until worlds vary, a pairing has 8 distinct matches (4 side/kickoff settings, each mirrored); it stops at 4 or more once wins minus losses reaches 3 either way or can no longer change sign. Not built: Elo anchoring (points per match against the pool is the read for now) and spot-checks of reused records. @@ -130,8 +133,8 @@ resolved, replace it with a one-line pointer under "Done". stall-testing tool, not an evaluation input. - *Names:* one match runner with the schedule (round-robin, ladder) and match settings (sides, kickoffs, world seed) as options, replacing round-robin / full-match tournament / - Elo as separate tools. `full_match_tournament.py` (both kickoffs) and `tools/elo.py`, - `plot_elo.py` are retired only once the ladder covers them. + Elo as separate tools. `full_match_tournament.py`, `tools/elo.py` and `plot_elo.py` are + removed; the ladder, then evaluate.py, cover them. - *Match cache:* a match's key (`replay/fingerprint.py` `match_key`) hashes the exact bytes and paths of everything `tools/evaluation/match.py` imports, data files next to them (not `.md`), both strategies' modules, and the match settings. New tools that call @@ -185,6 +188,12 @@ resolved, replace it with a one-line pointer under "Done". already computes `{marker: opponent}` each tick and discards it. Tactic side is small via `MatchLog.trace_if_changed()`; `field_canvas.js` has no line/arrow primitive yet. +9b. **Match timeline in the dashboard.** `tools/match_timeline.py` draws one window as swimlanes + (referee band, each robot's tactic, possession, distance to the ball, events); as a dashboard + panel under the Replay tab's field view it would follow the replay cursor, and clicking a lane + would seek to that tick. Data is all in the replay and its `intentions.jsonl` already + (`load_window` computes it without drawing). Not started (user idea, 2026-10-17). + 10. **More tactics from real football vocabulary** (formations, set plays, pressing schemes) rather than variations on existing ones. @@ -272,7 +281,15 @@ resolved, replace it with a one-line pointer under "Done". and compare velocity jitter and lag against a constant-velocity filter, before changing anything. -11. **Deferred, revisit only when forced** (minimalism): +11. **Sim realism (plan, 2026-10-14):** [`sim_realism.md`](sim_realism.md). Real 2025–2026 + Division B logs against the baseline round-robin: goals per live minute match, but sim + keepers save about twice as many good chances and sim shots almost never go wide. Plan: a + realism scorecard, parameter ranges from the official logs, seeded per-match physics draws + (latency, kick noise, ball deceleration, per-robot limits, vision noise), a sensitivity + sweep, then adoption in the round-robin. P0 ahead of a real-data value model (P1) and + structured strategy search scored by it (P2). + +12. **Deferred, revisit only when forced** (minimalism): - Shared `Sticky`/hysteresis helper beyond `shared/tolerance.py` — existing instances differ in shape. - `@pytest.mark.engine` marker for kernel tests — wait for real CI bottleneck data. diff --git a/docs/signal_report.md b/docs/signal_report.md index ede92c4f..6e78b37e 100644 --- a/docs/signal_report.md +++ b/docs/signal_report.md @@ -3,87 +3,151 @@ What each strategy does well and badly, from the signals in [`signals.md`](signals.md), drawn from one round-robin. Standings are in [`strategies.md`](strategies.md); this shows why. -**Run:** `tournament_20261010_090819` (strategy-guard at `a17e2791`, the 12 non-retired configs, -66 matches of 600 s). Regenerate the figures after a new full round-robin, then rewrite the notes: +**Run:** `tournament_20261017_151052` (sim-realism-20261016 at `423f63a0`, the 12 non-retired +configs, 66 matches, sim noise `v3_seed2`). Regenerate the figures after a new full round-robin, then rewrite the notes: pixi run python tools/signal_report.py replays/tournament_ Signals explain results; none is a target. A red cell is a place to look, not a thing to tune away. -## Every strategy at a glance +## Why each strategy gets its results -![Signals per strategy](img/signals/signal_heatmap.png) +![Goals, shots and goals per shot, for and against](img/signals/results.png) -Rows by points per match (the number after each name). Each column is coloured by rank among the -12 strategies, green where the value usually helps; passes are blue because more is neither -better nor worse. The printed number is the real value: per match, or a share. +Goals per match are shots per match times goals per shot, so the three panels say whether a +strategy wins on how many shots it gets or on how good they are. Blue is the strategy's own, +orange the opponent's. -- **`split_shape` is green across attack**: most shots (14.8) and entries (22.5), a shot after - 20% of its regains (1.9 s on average) and after 59% of its free kicks, 1.74 m gained per pass - from the fewest passes (18). Its weak side is defense: 133 s a match of danger conceded, second - most, and the most real ball losses (63). -- **`counter_flow` and `score_aware_counter_flow` win by conceding little**: the fewest goals - against (2.2) and the least danger (103 and 87 s), with few shots of their own. -- **The pressers shoot a lot and still lose.** `high_press`, `give_and_go_solo` and - `overload_press` make 9–11 shots a match, but concede 4.3–4.6 goals: the shots they face are - open (63–66%) and they save half. -- **`press_and_pass` passes for nothing**: 50 passes, 0.34 m gained each, 13% forward. -- **`decoy_and_overload` gains ground passing (1.07 m, 36% forward) and concedes the most**: - 5.0 goals and 156 s of danger a match. -- **`clear_danger` is last with no red flag in defense**: it shoots least (6.0), from furthest - (2.7 m), at the least open goal (52%). It defends adequately and doesn't create. +- **`split_shape` wins on both**: 21.9 shots to 9.0 faced, and 27% of its shots score against + 14% of the shots it faces. +- **`clear_press_plus` wins on quality alone**: as many shots as it faces (6.3 and 6.5), but + only 13% of the shots against it score, the fewest of all. +- **`counter_flow` loses on quality**: it outshoots its opponents (8.9 to 7.4) but scores with + 17% of its shots, the worst, and concedes with 26%. +- **`decoy_and_overload` faces the most shots** (11.5 a match) and is outshot by 5. +- **`high_press` plays the most open games**: 32% of its shots score and 29% of those it faces. +- **`score_aware_counter_flow` is last on defense**: 30% of the shots it faces score. -## Attack against defense +## The signals behind it -![Attack vs defense](img/signals/attack_defense.png) +![Signals per strategy](img/signals/signals.png) -Left: shots created against seconds the enemy holds the ball in the strategy's defensive third. -Only `split_shape` creates far more than the rest; it does so while conceding more danger than -most. `overload_press` creates 10.8 shots while conceding the second least danger (91 s), yet -finishes ninth: its losses are in the right panel. +Each panel is one signal on its own scale; every strategy is a dot, in the same order as above, +and the dashed line is the median. Only the lowest and highest values are printed: all of them +are in the table at the end. -Right: how open the shots a strategy faces are, against its save rate. Every strategy fields the -same keeper, so this is the defense in front of it. The pressers (`high_press`, `overload_press`, -`give_and_go_solo`, `press_and_pass`) are bottom right: they leave the lane open (63–66%) and -save about half. `split_shape`, `counter_flow` and `score_aware_counter_flow` are top left: the -lane is blocked (47–51%) and the keeper saves 65–68%. +- `split_shape` is far ahead in attack: 26.5 entries to the attacking third, a shot after 23% of + its regains and 55% of its free kicks, 1.28 m gained per pass from the fewest passes (17). +- `clear_press_plus` and `split_shape` face the least open shots (48–49% of the goal mouth) and + save 86–87%: the defense in front of the keeper blocks the lane. `overload_press` concedes + the least danger (74 s) but faces the most open shots (67%). +- `clear_press_plus`, `overload_press` and `tiki_taka_plus` pass the most (100–118 a match) + for the least ground (0.13–0.20 m a pass). ## What makes a shot score ![Goals per shot by distance and open goal](img/signals/shot_quality.png) -All 1117 shots of the run. Inside 1.5 m with three quarters of the goal open, 89% score; with -under a quarter open, at most 18% do at any distance, and past 3.5 m at most 14%. So shot distance -and open goal mouth measure chance quality, and a strategy's conversion follows from where it -shoots, not from luck. Every shot here is already on target (`MatchStats`' detector), which is -why the rates are high. +All 1122 shots of the run; cells under 15 shots are grey, with no rate. With three quarters of +the goal open, 90% score inside 1.5 m and 13% past 3 m; with under a quarter open, at most 12% +do. So shot distance and open goal mouth measure chance quality, and a strategy's conversion +follows from where it shoots, not from luck. Every shot here is already on target +(`MatchStats`' detector), which is why the rates are high. ## How each strategy loses the ball ![Ball losses by kind](img/signals/ball_losses.png) -Real ball losses per match (not won back within 1 s), measured as config_a only: `n` is how many -matches that is. In a round-robin config_a is the earlier name, so `split_shape` (1), -`score_aware_counter_flow` (2) and `press_trigger_flow` (3) rest on very little, and -`tiki_taka_plus` has none. +Real ball losses per match (not won back within 1 s). They are measured for config_a only, so +strategies with fewer than 5 matches as config_a are left out; in a round-robin config_a is the +earlier name, which leaves out `split_shape`, `tiki_taka_plus`, `press_and_pass`, +`press_trigger_flow` and `score_aware_counter_flow`. -- **Being tackled is everyone's largest loss**, 14–25 a match: carrying into pressure. -- **`clear_press_plus` kicks the ball out most** (about 11 a match), its second largest loss; it - also commits the most out-of-bounds fouls below. -- Shots saved or blocked are a loss too, so the strategies that shoot most (`split_shape`, - `give_and_go_solo`, `overload_press`) lose more that way; that one is a cost of attacking. +- Being tackled (15–19 a match) and kicking the ball out (10–13) are the largest losses for + everyone shown. +- `decoy_and_overload` loses the most to fouls (about 6 a match), the excessive dribbling and + double touches below. ## Fouls and stalls ![Fouls by rule](img/signals/fouls.png) -Fouls per match by the offending strategy, colour scaled per column, and the stalled matches it -played in (each stall counts for both teams). No match stalled in this run. +Fouls per match by the offending strategy, by rule; the ball going out of play is not a foul and +has its own panel. Each stall counts for both teams in the match. -- Out of bounds is the most common foul everywhere; `clear_press_plus` (11.5) and `clear_danger` - (9.3), the two with a clearance valve, are the outliers. -- `overload_press` (3.2) and `decoy_and_overload` (2.2) dribble too far: both run - `DecoyOverloadTactic`. -- `counter_flow` (2.0) and `score_aware_counter_flow` (1.7) enter the defense area most, and - `split_shape` crashes into robots most (1.4). +- `split_shape` commits the most (about 10 a match), half of them stopping play in the defense + area (4.6). +- `decoy_and_overload` dribbles too far most (3.7); it and `overload_press` run + `DecoyOverloadTactic` and double-touch most (1.6 and 1.7): the lure lets the ball go and + touches it again. +- The ball goes out 15–22 times a match for every strategy; most for `clear_press_plus` and + `clear_danger`, the two with a clearance valve. + +## On the pitch + +Drawn by `tools/pitch_maps.py` from the same run (`pixi run python tools/pitch_maps.py replays/tournament_`, +about 3 minutes). Every panel attacks to the right: the strategy's own goal is on the left, and a +team that attacks left is turned 180 degrees, so its left wing is the top of its panel too. +Live play only. "With the ball" means the team's nearest robot is within reach of the ball and the +ball is slow, the same radius `MatchStats` uses for possession. + +![Where the outfield robots stand with the ball](img/signals/positions_attack.png) + +Share of the live samples per cell, one scale for every panel; the five dots are each robot's mean +position (the goalkeeper is left out). + +- `overload_press` is the most forward. 83% of its samples with the ball are in the attacking half, + and only 17% of its samples without it are in its own half. Its robots stack about 2 m into the + attacking half (the dark block in its panel). +- `split_shape` builds from deep: 37% of its samples with the ball are in the attacking half, yet + 72% of its shots are struck from the attacking third (below). + +![Where the outfield robots stand without the ball](img/signals/positions_defence.png) + +The same robots while the opponent has the ball. + +- `split_shape` and `score_aware_counter_flow` sit deepest: 68% and 69% of their samples without the + ball are in their own half, against 17% for `overload_press`. +- In most panels a vertical stripe stands about 2.7 m in front of the strategy's own goal, in both + figures: robots that hold one line in their own half whether or not they have the ball. It is + mostly `BlockShapeTactic`'s fixed-depth screen; the breakdown by tactic is in + [visual_guide.md](visual_guide.md#where-its-robots-stand-the-position-map). + +![Where each strategy shoots from, and where it concedes](img/signals/shots.png) + +Every shot at the spot it was struck from: filled if it scored, hollow if not. Conceded shots are at +the other end, faded. The counts are shots and goals per strategy over the run. + +- `split_shape` takes the most shots (241, 22 a match, 65 goals). Its own shots are spread across the + attacking half, and its conceded shots sit on the left. +- `decoy_and_overload` faces the most shots (127, 11.5 a match) and `clear_press_plus` the fewest (71, + 6.5 a match); the faded conceded shots of `decoy_and_overload` fill the left half of its panel. + +![Where each strategy commits its fouls, by rule](img/signals/fouls_map.png) + +Each foul at the ball's spot. The four most common rules in the run have their own colours and the +rest are "other rules"; ball out of play is not a foul. + +- `score_aware_counter_flow` commits the largest share in its own defensive third (41% of its fouls), + and `clear_press_plus` the smallest (12%); the script prints these shares. +- Excessive dribbling (blue) and crashing (green) are spread across the pitch in most panels. + +## The numbers + +Every value in the figures, one row per strategy, by points per match. + +| strategy | points / match | GF | GA | shots | shots faced | goals / shot | conceded / shot faced | attacking-third entries / match | regains followed by a shot | free kicks followed by a shot | shot distance (m) | goal mouth open, own shots | completed passes / match | metres gained per pass | danger conceded (s / match) | goal mouth open, shots faced | save rate | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| `split_shape` | 2.82 | 6.0 | 1.3 | 21.9 | 9.0 | 27% | 14% | 26.5 | 23% | 55% | 3.1 | 52% | 17 | 1.28 | 96 | 49% | 86% | +| `clear_press_plus` | 1.82 | 1.3 | 0.8 | 6.3 | 6.5 | 20% | 13% | 15.5 | 11% | 14% | 3.1 | 56% | 114 | 0.13 | 84 | 48% | 87% | +| `overload_press` | 1.82 | 2.0 | 1.6 | 7.8 | 8.6 | 26% | 19% | 19.8 | 14% | 19% | 2.7 | 57% | 118 | 0.20 | 74 | 67% | 81% | +| `clear_danger` | 1.55 | 1.8 | 2.2 | 8.1 | 8.0 | 22% | 27% | 18.6 | 15% | 14% | 3.1 | 57% | 37 | 0.36 | 93 | 53% | 74% | +| `tiki_taka_plus` | 1.45 | 2.1 | 1.9 | 7.3 | 7.4 | 29% | 26% | 13.5 | 12% | 15% | 3.2 | 58% | 103 | 0.15 | 79 | 53% | 74% | +| `high_press` | 1.36 | 2.2 | 2.5 | 6.8 | 8.4 | 32% | 29% | 16.6 | 16% | 11% | 3.0 | 57% | 73 | 0.23 | 82 | 64% | 72% | +| `press_trigger_flow` | 1.18 | 1.2 | 1.7 | 6.6 | 7.8 | 18% | 22% | 14.2 | 10% | 13% | 3.2 | 49% | 37 | 0.43 | 84 | 57% | 78% | +| `give_and_go_solo` | 1.09 | 1.8 | 2.7 | 7.9 | 9.4 | 23% | 29% | 18.3 | 17% | 15% | 3.1 | 60% | 39 | 0.45 | 82 | 65% | 74% | +| `press_and_pass` | 1.09 | 1.3 | 2.5 | 6.3 | 9.8 | 20% | 26% | 16.2 | 12% | 9% | 3.3 | 61% | 79 | 0.20 | 87 | 60% | 74% | +| `decoy_and_overload` | 1.00 | 1.5 | 2.5 | 6.5 | 11.5 | 23% | 21% | 17.5 | 12% | 12% | 3.2 | 60% | 45 | 0.47 | 93 | 49% | 80% | +| `counter_flow` | 0.91 | 1.5 | 1.9 | 8.9 | 7.4 | 17% | 26% | 15.8 | 14% | 19% | 3.1 | 58% | 40 | 0.40 | 80 | 51% | 74% | +| `score_aware_counter_flow` | 0.64 | 1.5 | 2.5 | 7.6 | 8.3 | 19% | 30% | 13.2 | 13% | 14% | 2.9 | 58% | 38 | 0.52 | 102 | 56% | 70% | diff --git a/docs/signals.md b/docs/signals.md index 8bc3a5cc..5a35f2ce 100644 --- a/docs/signals.md +++ b/docs/signals.md @@ -4,10 +4,48 @@ Every signal we record about how a strategy plays, grouped by the question it an when designing or debugging a strategy: find the question, read the signal, compare with the reference range, and follow "if it's off". Add a signal here when you add one anywhere else. -Signals are diagnostics, not objectives. Rank strategies by results (W-D-L, goals); use these to -explain a result, to find what a strategy does badly, and to notice a shared primitive -(reception, carrying, the planner, the referee) failing every strategy at once. A strategy that -never passes or shoots loses the ball least, so no signal is "better" on its own. +Signals are diagnostics, not objectives: use them to explain a result, to find what a strategy +does badly, and to notice a shared primitive (reception, carrying, the planner, the referee) +failing every strategy at once. A strategy that never passes or shoots loses the ball least, so +no signal is "better" on its own. Ranking uses goals alone: see [Ranking](#ranking). + +The sections below group the signals by the question each one answers; this map shows the groups. + +```mermaid +mindmap + root((Signals)) + ["Is something broken?"] + ["Does it create chances?"] + ["Does it keep and move the ball?"] + ["Does it defend?"] + ["Restarts"] + ["Where do goals come from?"] + ["Ranking"] +``` + +## Ranking + +Strategies are ranked by goals only, through ratings fitted on match margins +(`tools/evaluation/evaluate.py`; `docs/STRATEGY_DEVELOPMENT.md`, "Evaluating a strategy"). +Signals explain a result; none of them ranks or is a search objective. + +Tested and rejected as rankers (2026-10-17, two noisy round-robins of the same 12 strategies with +different seeds, `tournament_20261016_201213` and `tournament_20261017_002337`; scripts in +`~/dev/ssl/eval-infra/` and `~/dev/ssl/xg-analysis/metrics_quick/`). Those scripts were one-off +analysis scripts kept outside the repo; the data they used are the two round-robins above and the +evaluation ledger (`replays/eval/ledger.jsonl`). + +- **Shots on target.** Within one run, strengths from shots predicted held-out matches about as + well as goals; across runs, goal strengths from run 1 predicted run 2's goal strengths better + (rank correlation 0.57) than shot strengths did (0.37), leaving out split_shape, whose margin + swung from +1.5 to +4.9 between seeds and dominates any correlation that includes it. +- **xG** (real-data model, `analysis/realism/xg.py`): sim keepers save twice as often as real + ones, so it misjudges sim strategies by how they shoot. +- **Possession, completed passes, possession under pressure**: consistent between runs but + predict *losing* in this pool. Territory and attacking-third entries predict nothing. +- **Weighted combinations** of match stats: the weights depend on the strategy pool they are + fitted on, so a new strategy's score would depend on unrelated strategies. +- **A value model** of the game state (`docs/sim_realism.md`): no more reliable than goals. ## Where they come from @@ -19,6 +57,7 @@ Every saved `tools/evaluation/round_robin.py` run prints its sections and writes | Standings, STRATEGIES | `standings`, `strategies[*]` | `MatchStats` (live, every match) | | LOSS KINDS, BALL LOSSES, `passes:` line | `strategies[*].real_loss_kinds_as_a`, `ball_losses` (tables in `ball_losses.md`) | `analysis/turnover_breakdown.py` | | CHANCES | `strategies[*].chances`, `pass_progress_m`, `forward_pass_share` | `analysis/chances.py` | +| None (in `summary.json` only) | `ratings` | `analysis/ratings.py` ([Ranking](#ranking)) | | FOULS | `fouls` | `MatchStats` | | STALLS | `stalled_match_count`, `stall_incidents`, per-match `stats.stall_events` | `MatchStats` stall watchdog | | RESTARTS | `restarts` | `analysis/restart_outcomes.py` | @@ -111,9 +150,35 @@ sum over situations of situations per match × P(goal), for goals scored and con result splits into which situations a strategy creates or allows, and how well it converts or defends them. It reads only the replay and its referee file, so it runs on converted official logs too. The rules are in the module's docstring. `--rates PATH` writes how often each -situation happens per team per live minute, whoever owns it: the weights of the -[situation bench](STRATEGY_DEVELOPMENT.md#the-situation-bench), whose P(goal) also counts a goal -that comes after a later situation started, unlike the table below. +situation happens per team per live minute, whoever owns it (the scenario bench, retired +2026-10-17, used these as its weights). + +How a goal is credited (`analysis/goal_ledger.py`; `WINDOW_S` = 10 s): + +```mermaid +flowchart TD + subgraph starts["A situation starts"] + rs["Restart goes live
find_restarts: kickoff, free kick, penalty
owned by the kicking side"] + fs["Force start
owned by no one"] + bw["Side wins the ball in open play
possession_spells: owned by that side"] + end + + types["Situation type, from the owner's frame
own / opponent: ball won or free kick, third;
kickoff, penalty; force start"] + goal["Goal, at the tick play stops"] + near{"Latest start less than 10 s
before the goal?"} + sus["sustained possession"] + cred["Credited to that situation
p_scored / p_conceded"] + sum["goals per match = sum over situations of
situations per match x P(goal)"] + + rs --> types + fs --> types + bw --> types + goal --> near + near -->|yes| cred + near -->|no| sus + cred --> sum + sus --> sum +``` Reference: `split_shape` in `tournament_20261011_230119` (17 matches; 6.76 scored, 4.41 conceded per match). diff --git a/docs/sim_realism.md b/docs/sim_realism.md new file mode 100644 index 00000000..e351e715 --- /dev/null +++ b/docs/sim_realism.md @@ -0,0 +1,515 @@ +# Sim realism: closing the gap between rsim and real matches + +Status: plan, 2026-10-14 (priorities added 2026-10-16). Nothing below is built yet unless it +says so. + +## Where this leads + +The end goal is **search over the structured strategy space**: tactics, their parameters, and +the scheduler (partitioner) that combines them. Candidates are scored with a **dense value +signal** instead of goals alone. Three pieces, in priority order: + +| Priority | Work | Why this order | +|---|---|---| +| P0 | Sim realism, Phases 0–3 below: scorecard, league ranges, seeded per-match physics draws, calibration | Search finds whatever wins in the sim. If the sim rewards an unrealistic detail (a perfect keeper, kicks that never miss), stronger search exploits it harder. The seeded draws are also the controlled-noise framework every later evaluation uses | +| P1 | [Value model fitted on real logs](#p1-value-model-on-real-data) | A model trained on sim outcomes learns the sim's errors. Fitted on real matches, it values situations the way real matches do. Safe to apply to sim strategies only once the sim varies over realistic ranges | +| P2 | [Structured search](#p2-structured-strategy-search) scored by the value model in the randomized sim | Needs both of the above | + +A "novel" winner from search looks exactly like a sim exploit until checked. Every one gets +the per-strategy scorecard: an edge from parts the sim gets right (positioning, chance +creation) is kept; an edge from parts it gets wrong is suspect. + +## Why + +Strategies are developed and ranked in rsim. A ranking is only useful if a strategy that wins +in the sim would also win on a real field. That needs two things: + +- **Sim physics in the right range.** The sim must not reward behaviour that only works + because of an unrealistic detail, such as a perfect keeper or a perfectly accurate kick. +- **Robustness.** Real parameters drift (floor friction, kick speed as the capacitor drains, + dribbler wear, one robot slower than another). A strategy must win across that drift, not + at one tuned value. + +The goal is therefore not a perfect replica of one real robot. It is a sim whose behaviour +matches real matches on the measures that decide goals, and that varies its physics over the +ranges real robots vary over. + +We assume no measurements of our own robots for the next month. Ranges come from the official +match logs of other teams (see [Official logs](#what-the-official-logs-can-and-cannot-give)), +and are replaced by our own measurements once hardware tests are possible. + +## What we found (2026-10-14) + +Real: 54 official Division B matches from 2025–2026 (`tools/convert_ssl_log.py`). Sim: the +baseline round-robin `tournament_20261014_144456` (66 matches). An expected-goals (xG) model +was fitted on real shots. On held-out matches it ranks shots with AUC 0.82 and its predicted +rates agree with observed ones. + +Goal attempts per live minute, their real xG and finishing (goals ÷ xG) each sit inside the +real 95% interval (pooled sim ÷ real: ×1.09, ×1.12, ×1.11). That agreement hides two +differences that cancel: + +| Shots of equal quality (xG band) | Real | Sim | +|---|---|---| +| Saved by the keeper, all shots | 17% | 39% | +| Saved by the keeper, xG > 0.35 | 20% | 52% | +| Wide or off the post | 22% | 6% | +| Goal rate, xG < 0.05 | 2% | 14% | +| Goal rate, xG > 0.35 | 53% | 35% | + +Sim keepers save far more good chances; sim shooters almost never miss. + +Other differences: + +- **Ball slow-down:** the sim ball keeps 57% of its speed by 1 m from goal; real balls keep 67%. +- **Kick speed:** every sim kick is 4.75 m/s; real kicks range from 3.9 to 5.5 m/s (interquartile). +- **Keeper position:** the sim keeper always stands on the goal line, 0.09 m deep; real keepers stand about 0.2 m out. +- **Shot count:** with passes and clearances counted, sim teams make 1.8× more goalward kicks than real teams. +- **Live time:** real matches have about 7.4 live minutes; the sim has about 9.1. Together with the slightly higher per-minute rates above, this is why sim matches have more goals per match. + +Lesson: **never judge realism by goal totals.** They can match for the wrong reasons. +Compare the components, as in the [scorecard](#the-realism-scorecard). + +## How one rsim tick is built + +Where the sim noise is applied in one tick: commands go down through the seeded command-side +noise, and the observations come back through the seeded vision noise. The C++ physics is the +fork in `vendor/rSim` ([FORK_NOTES.md](../vendor/rSim/FORK_NOTES.md) covers the build). + +![One rsim tick: the command-side noise, the C++ physics and the vision-side noise between the strategy layer and the physics](img/sim_realism/rsim_tick.png) + +Drawn by `docs/img/sim_realism/make_rsim_tick.py`; rerun it when a name in it changes. + +## Parameters + +Ordered by how much each one is likely to change match outcomes. + +**Between** means the value differs per robot or per match (one robot's top speed, a field's +friction). **Within** means kick-to-kick noise for the same robot. + +| Parameter | Why it matters | From official logs? | rsim today | +|---|---|---|---| +| Sensing-to-action latency (vision, decision, radio, motor response) | Likeliest cause of the keeper gap | Only as other teams' total reaction time: from a kick to their keeper accelerating | None: commands act on the next tick | +| Ball rolling deceleration (and the switch from sliding to rolling) | Pass and shot timing; keeper reaction time | Yes: fit free-rolling segments, per match (venue carpet) | Fixed in the C++ physics (`vendor/rSim`) | +| Kick speed: between and within | How much time the keeper has; pass weight | Yes: peak ball speed after each kick, per team | Fixed | +| Kick direction error | Wide rate (6% vs 22%) | Partly: shot miss distance, pass error relative to the receiver | None: exact | +| Robot top speed, acceleration, braking, turn rate | Who reaches the ball first | Yes: from the tracker, per robot | One limit for all robots (2 m/s for evaluation) | +| Vision noise and dropped frames (ball especially, also when blocked) | Every decision; keeper tracking | Yes from raw vision packets in the logs; the converter currently reads the filtered tracker | Yes: `SimNoise` (`--sim-noise`, the pool's `sim_noise`) in `run_match`, the round-robin and `evaluate.py`; `rsim_noise` and `rsim_vanishing` in `StrategyRunner` are its older form | +| Dribbler: hold strength, ball loss when turning or touched, release speed | Possession, passing | Weak: only possession-loss rates as a proxy | Fixed, with the fork's dribbler fixes (`vendor/rSim/FORK_NOTES.md`) | +| Ball bounce off robots and walls | Rebounds, deflections | Partly: speed before and after contact | Fixed | +| Robots fielded per team | Defenders in the way of each shot (real 4.5 on average, sim always 6) | Yes: robots on the field in live play, per match | Always 6 | +| Robot breakdowns and lost commands | Gaps in defence and support | Partly: robots that stop mid-play. Lost radio commands only from our hardware | None | +| Stoppages: how often, and how long (ball placement, human-confirmed goals) | Game flow: set pieces vs continuous pressing | Yes: restarts and dead time per match (real 41.6 restarts, sim 25.4) | Our referee's rules and timings | + +### What the official logs can and cannot give + +- **They describe the league, not our robots.** That's still the right range for opponents, and + a reasonable starting range for our own robots until we measure them. A strategy robust + across the league's range is unlikely to break on ours. +- **Robots and ball come from an autoref's tracker**, which filters positions. Peaks + (kick speed, acceleration) are smoothed, so tracker-based estimates are lower bounds. Raw + camera detections are also in the logs, for vision noise. +- **Latency cannot be separated from decision time** for other teams. We get their total + reaction time, which is what matters for keeper saves anyway. +- **Kick direction error needs an intended target**, which the logs don't record. Proxies are a + shot's miss distance, and a pass's distance from the nearest teammate when it arrives. + +## How varying parameters are handled in strategy development + +**Domain randomization:** each parameter gets a range, and the sim draws from it. + +1. **Between-values are drawn per match and per robot; within-noise per kick.** For example: + - friction once per match; + - a top speed and kick-speed mean per robot; + - kick speed and direction noise on every kick. +2. **Draws come from the match seed.** They join the seeded vision noise already planned for + the round-robin. When strategy A and strategy B each play the baseline, + they see the same draws, so the comparison is paired and the extra randomness costs few + extra matches. +3. **Ranges are measured, not guessed.** Use the spread of team-level medians for between- + values, and each team's own spread for within-noise. Too narrow overfits to the sim; too wide + rewards overcautious play. +4. **Randomize only what changes standings.** A sensitivity sweep decides which parameters + are randomized; the rest stay fixed at the league median. +5. **Edge cases are a separate diagnostic, not part of the ranking.** Run candidates at the + edges (slow robot, weak kick, worn dribbler) and report how far they fall. +6. **Prefer strategies that measure parameters over ones that assume them.** For example, a + pass speed chosen from the kick speeds actually observed. The sim then checks that the code + copes with the whole range. + +## The realism scorecard + +One report that compares sim with real on the components that decide goals. Each row is a +real value with a 95% interval, the sim value, and a pass mark (sim inside the real +interval): + +- shots per team per live minute (passes and kicks from beyond 6 m excluded); +- real xG per shot; +- finishing (goals ÷ real xG), overall and in each xG band; +- shot outcome shares: keeper, defender, wide or post, goal; +- ball speed kept to 1 m from the goal; +- kick speed: median and spread; +- keeper depth and keeper reaction time; +- live minutes per match and restarts per match (referee-driven, so they are checked but not targeted). + +Real values are recomputed when new logs are converted. The sim side reruns on any +round-robin. + +## Plan + +Each phase ends with a checkpoint: results reported, decision taken, then the next phase +starts. Evaluation changes and strategy changes go in separate commits. + +### Phase 0: Bring the analysis into the repo (2–3 days) — done 2026-10-16 + +`pixi run python tools/realism_scorecard.py` (see `docs/tools.md`); package +`utama_core/analysis/realism/`, real reference `real_2025_2026.json`. On the baseline it +reproduces the outcome numbers above. Rows failing on the baseline: goal rate in every xG +band, keeper and defender share, wide or post, ball speed kept, kick speed spread, keeper +depth. Keeper reaction time is not a row yet (Phase 1). + +The xG and shot-outcome analysis currently lives outside the repo (`~/dev/ssl/xg-analysis/`). + +- Move it into `utama_core/analysis/`: + - `shots.py`: the kick-based shot detector. Real goals are linked through the game + controller's GOAL events, because real goals are confirmed by a human operator seconds to + minutes late. + - `xg.py`: the model fit and the decomposition. + - `shot_outcomes.py`: what happened to each shot. +- Add a `tools/realism_scorecard.py` entry point, and unit tests on the shot detector and goal + linking using small synthetic frames. +- Commit the fitted real xG coefficients as data, so the sim side doesn't need the real logs. +- Done when: the scorecard reproduces the numbers above for the baseline. + +### Phase 1: League ranges from the official logs (1 week, analysis only) + +One script per parameter, each giving a between-range and a within-spread with intervals: + +1. **Ball deceleration:** free-rolling segments (no robot within 0.3 m, speed below the + sliding threshold), fitted per match. +2. **Kick speed:** peak speed after each detected kick. Spread across teams, and within each team. +3. **Robot motion:** per robot, the 95th-percentile speed and acceleration, and braking. +4. **Reaction time:** from a shot's kick to the keeper's acceleration onset, and to its first + movement toward the shot line. The same for the nearest defender. +5. **Kick direction error, by proxy:** miss distance of shots, and pass arrival error relative + to the nearest teammate. +6. **Vision noise and dropped frames:** from raw camera detections of a ball at rest, and the + share of frames missing each object. This needs the converter to keep raw detections (a + small converter option). + +Done when: a table with each range and its caveats has been added to this doc. + +#### Results so far (2026-10-16, `python -m utama_core.analysis.physics_ranges`) + +54 real 2025–2026 Division B matches against the 66-match sim baseline. Deceleration in m/s², +speed in m/s, turn rate in rad/s; brackets are 95% bootstrap intervals over matches. + +| Measure | Real | Sim | +|---|---|---| +| Ball rolling deceleration (1 s or more after a kick) | 0.29 [0.28, 0.30]; per-match IQR 0.29–0.33; within-match IQR 0.09 | 0.65 at every speed, no spread | +| Ball sliding deceleration, 0.1–0.2 / 0.2–0.3 / 0.3–0.5 s after a kick | 3.7 / 2.7 / 1.9 | 0.9 / 0.65 / 0.65 | +| Robot speed, 95th percentile (team medians) | 1.37 (0.65–2.04) | 1.66 (1.32–1.92) | +| Acceleration, 95th percentile (team medians) | 2.56 (1.54–3.49) | 2.85 (2.80–2.89) | +| Braking, 95th percentile (team medians) | 4.09 (1.77–5.79) | 2.96 (2.92–3.07) | +| Turn rate, 95th percentile (team medians) | 3.02 (1.01–6.43) | 2.56 (2.32–2.78) | +| Ball unseen in live play (tracker output) | 1% per match; 4.9 gaps per live minute, median 0.05 s, p90 0.2 s | none | + +- **The sim ball has the wrong shape, not just the wrong level.** A real ball slides for about + 0.5 s after a kick, losing 2–4 m/s², then rolls at about 0.3. The rsim ball loses a constant + 0.65. It keeps too much speed early and loses twice too much while rolling. Phase 2 needs + two-phase ball friction in the C++ fork, not one damping value. +- **Robot motion measures driving, not just hardware.** The baseline ran the FPP planner on + both sides with a 4 m/s² configured acceleration limit, so the sim's 2.85 / 2.96 m/s² are how + FPP drives. Real values mix each team's planner (often bang-bang or trajectory sampling) with + its hardware. Per-robot sim limits are hardware caps applied below any planner; take their + range from real peaks (a lower bound, because of tracker smoothing), not typical driving. +- **Sim teams are near-identical:** acceleration 2.80–2.89 across teams against 1.5–3.5 real; + real top speeds differ about 3× between teams. Per-team and per-robot draws are justified. +- Caveats: the tracker smooths peaks (robot values and sliding deceleration are lower bounds; + the first 0.1 s after a kick is unusable). Tracker dropouts understate vision dropouts, + because the tracker bridges short occlusions. Exclude 2026-07-02 TurtleRabbit vs NAMeC + (broken tracker: ball unseen 57%). Robots moving with a 95th-percentile speed under + 0.5 m/s are dropped, which also drops keepers. +- Next data: the converter discards raw camera detections and the tracker's `KickedBall` + message (kick time, kicker, velocity) and per-object `visibility`. Keeping them gives + vision noise and a direct kick-speed source. + +#### More measurements (2026-10-16, from the raw-vision and kick sidecars) + +| Measure | Real | Source | +|---|---|---| +| Raw robot position noise per detection | 0.4–1.0 mm | 2022 `.vision.zip`, 2nd difference / √6, 4 matches, one camera each | +| Raw robot heading noise | 0.2–0.3° | same | +| A robot missing for one camera frame | 0.1–0.9% | same | +| Kick speed spread at full power, within one team | IQR 0.7–1.1 m/s, SD about 0.45 | 2022 `.kicks.json`: kicks at or above 0.8 × the team's 90th percentile | +| Full-power kick speed, between teams | 4.1–6.3 m/s (team medians) | same | + +- **Kick speed spread across all kicks (IQR 1.66) is mostly teams choosing a speed**, not + kicker noise. Our kicker is fixed power, so the within-team full-power spread is the range to + draw from; the scorecard's all-kicks IQR row should not be expected to match. +- **Vision noise is small and must be set from the measurement.** A provisional 5 mm / 1° made + the recorded sim positions jitter 7 mm frame to frame (real tracker output: 0.6–0.8 mm), and + that jitter read as robots reacting: keeper "velocity change within 0.4 s" went from 68% to + 98%. Our replay records the positions after our own Kalman filter, as the strategy sees them; + real logs are another team's tracker output. Rows built on velocities are only comparable when + the sim's vision noise is realistic. + +#### First calibration samples (2026-10-16) + +Six matches each (split_shape, tiki_taka_plus, press_trigger_flow, score_aware_counter_flow, +full length, `--sim-noise ~/dev/ssl/xg-analysis/calib/v{1,2}.json`), against the 53-match real +reference. Common to both: latency 0.05–0.10 s, kick speed SD 0.45 (per robot 0.2), kick +direction SD 3° (per robot 1.5°), ball slide 2.5–4.0 then roll 0.27–0.33 m/s², acceleration cap +1.5–3.5 m/s². v1 vision noise 5 mm / 1° / 2% dropped (provisional); v2 0.7 mm / 0.3° / 0.5% +(measured). Six matches give wide intervals: read directions, not passes. + +| Row | Real | Baseline (knobs off) | v1 | v2 | +|---|---|---|---|---| +| Goals per match | about 3 | 6.2 | 3.7 | 5.2 | +| Finishing (goals / xG) | 1.00 | 1.10 | 0.89 | 1.01 | +| Keeper saves | 0.17 | 0.39 | 0.35 | 0.38 | +| Keeper velocity change, median (s) | 0.18 | 0.12 | 0.12 | 0.19 | +| Keeper moves toward the line, median (s) | 0.22 | 0.12 | 0.18 | 0.20 | +| Wide | 0.11 | 0.05 | 0.13 | 0.10 | +| Aim outside the posts | 0.20 | 0.11 | 0.21 | 0.20 | +| Ball speed kept 1 m from goal | 0.67 | 0.57 | 0.66 | 0.65 | +| Pass line offset, median (m) | 0.26 | 0.09 | 0.19 | 0.10 | +| Pass completion | 0.50 | 0.70 | 0.52 | 0.63 | +| Live minutes per match | 7.1 | 9.1 | 7.2 | 7.4 | +| Restarts per match | 42 | 25 | 44 | 48 | + +- With measured vision noise, **keeper reaction times match real** (0.19 s vs 0.18) with + latency 0.05–0.10 s, and so does the nearest defender's. The latency range is consistent with + real reactions, though it is still a stand-in for our own hardware's (Phase 5). +- **Keepers still save about twice as often as real ones**, at every xG band, with reaction + times now matching. Not explained yet. Candidates: our keeper's positioning (0.09 m off the + line vs 0.21 real), contact physics (a real ball off a keeper deflects unpredictably; rsim's + bounces cleanly), and real shots the scorecard counts as on target that real keepers cannot + reach. Look before adding a knob. +- **v1's better passing rows came from vision noise, not kick noise.** With realistic vision, + pass offset falls back to 0.10 m against 0.26 real. Real pass error includes other teams' + decisions and receivers moving, so it is not purely kick error; do not raise kick direction + noise to close it without a hardware measurement of our kicker. +- **Real "crossed, no goal" was mostly the ball bouncing inside the goal after a goal**, before a + human confirmed it (hand check: 10 of 12 cases, 8 followed by the score rising). Those kicks + no longer count as shots; the reference dropped from 1,055 to 959 attempts and real crossed- + no-goal from 7.7% to 2.8%. Against the rebuilt reference v2 reads: attempts per team per live + minute 1.85 vs 1.27 real, finishing 0.87 vs 1.00, keeper saves 0.38 vs 0.18, wide 0.10 vs + 0.10. The table above uses the old reference. +- **Keeper gap, first look** (`~/dev/ssl/xg-analysis/keeper/`): keeper depth, distance to the + shot line and the speed needed to reach it have the same distributions real and sim, and at + matched values the sim keeper still saves about twice as often (shot line within 0.1 m of the + keeper: real 0.29, sim 0.56). Lead to check when the 2025–26 kick sidecars exist: about 10% of + real kicks leave the ground (tracker `KickedBall` vertical speed, 2022–24), and a chip beats a + keeper standing on the line; our strategies rarely chip. + **Checked 2026-10-16, ruled out:** joined to the 2025–26 tracker kicks, only 21 of 1,180 real + shots (1.8%) leave the ground (vertical speed > 0.5 m/s; 2% of all 2025–26 kicks, against + ~10% in 2022–24). Flat shots alone are saved 0.17 of the time, the same as all shots, so the + sim's 0.38 is not about chips. Next suspects: what happens when the ball reaches the keeper + (contact and rebound, sim keeper body), and shot speed at the keeper. +- **First sensitivity sweep** (v2 with one change each, 6 matches per run, same 4 strategies; + `~/dev/ssl/xg-analysis/sens/summary_all.json`). Changing only the seed moves goals per match by + 0.5 and pass completion by 0.06, and reshuffles every place below the top: split_shape + wins every run, the other three swap places by chance alone. Latency 0–0.05 / 0.10–0.15 s: + goals per match 4.0 / 3.8 against 5.2, finishing 0.80 / 0.73. Kick direction 1° / 6°: goals + per match 6.0 / 3.5, pass completion 0.80 / 0.39 (real 0.50), keeper saves 0.34 / 0.27. + Kick direction moves the most and needs measuring on our kicker (Phase 5). No knob closes + the keeper gap without overshooting another row (at 6°, saves 0.27 but completion 0.39). +- **v3, 28 matches** (v2 with latency widened to 0.03–0.15 s, 8 strategies, 3 workers; + `replays/tournament_20261016_185615`, `~/dev/ssl/xg-analysis/calib/v3_compare.json`): + 3.2 goals per match (real ~3). Matching real: attempts per team per live minute 1.23 vs 1.27, + open-play and set-piece attempts, attempt distance, xG per attempt, aim outside the posts, kick + speed median, keeper reaction times. Still off: keeper saves 0.33 vs 0.18, so finishing 0.75 + vs 1.00; defender blocks 0.22 vs 0.35; wide 0.17 vs 0.10; pass completion 0.66 vs 0.50 and + pass line offset 0.11 vs 0.26 m; keeper depth 0.09 vs 0.21 m (strategy). With 8 strategies + instead of 4, attempt volume matches; the 4-strategy samples' excess was those strategies. + The knobs cost about 1% of match time (120 s match: 28.0 s off, 28.3 s on). +- **New baseline, 2026-10-16** (`replays/tournament_20261016_201213`, all 12 strategies, 66 + matches, v3 noise, 8 workers, ~70 min): 3.6 goals per match (old deterministic baseline + `tournament_20261014_144456`: 6.7). split_shape still first (26 points, GD +18; was +58). + Below it the order barely survives: rank correlation with the old baseline 0.17 + (clear_danger 10th → 2nd, decoy_and_overload 12th → 4th, press_and_pass 2nd → 10th). With + one match per pairing and per-match GD SD ≈ 3, those middle places are not resolved either + way; separating them needs repeated seeds. +- No stalls in either sample; a dribbled ball stays at the kicker with two-phase friction + (`test_dribble_with_friction.py`). + +**Checkpoint:** review which ranges are trustworthy. + +### Phase 2: Knobs in the sim (1–2 weeks) + +Each knob is off by default, so current results are unchanged, and each comes with a test that +pins its effect: + +- **Latency:** a delay buffer between the strategy's command and rsim (Python). This is the cheapest and probably biggest. +- **Vision noise and dropped frames:** done. `SimNoise` (`--sim-noise`, the pool's `sim_noise`) goes through `run_match`, the round-robin and `evaluate.py`. +- **Kick speed noise:** perturb the commanded kick speed (Python). +- **Kick direction noise:** perturb the direction inside the C++ kicker (fork change, recorded in `FORK_NOTES.md`). +- **Ball deceleration:** expose the ball's damping or friction in the C++ fork. +- **Per-robot motion limits:** speed and acceleration caps per robot in the sim controller. +- **One seeded draw** per match, and per robot, from the ranges, recorded in the replay so any match can be reproduced. + +Done when: a match reproduces exactly from its seed, and every knob at zero reproduces today's results. + +**Physics substeps (gated, not assumed).** rsim runs 5 substeps per tick, hardcoded in +`vendor/rSim/src/robosim/sslworld.cpp` (`SSLWorld::step`); making it a parameter is a fork change. Fewer would be +faster, but substep error is systematic (every kick, contact and dribble wrong the same way), +so it does not average out like vision noise. A fast ball at 6.5 m/s moves about 22 mm per +substep at 5 and 36 mm at 3, against a 43 mm ball, and the fork's kicker and dribbler fixes +were tuned at 5. Physics was 46% of match time (2026-10-10), so 3 substeps saves at most about +15–20% of a round-robin. Steps: + +1. Measure match time at 3, 4 and 5 substeps. +2. Measure kick speed and direction, ball slow-down, dribbler hold and release, and collisions + at 3, 4, 5 and a 10-substep reference. +3. Adopt fewer substeps only if the error against the reference sits well inside the Phase 1 + real ranges, and the scorecard and standings don't move beyond match noise. Adoption + reruns every cached match, so it goes in with the Phase 4 baseline. + +### Phase 3: Calibrate and find what matters (1 week) + +- **Sensitivity sweep:** for each knob, rerun a small fixed set of matches at the low, median + and high end of its range. Measure the change in the scorecard and in standings between a few + strategy pairs. +- **Calibrate:** set each kept knob to its league range and check the scorecard moves into the + real intervals, keeper saves and the wide rate above all. Where a knob at the league range + overshoots or does nothing, record it. That usually means a modelling gap, not a tuning problem. +- **Held-out check:** calibrate on 2025–2026, then check the scorecard on 2022–2024 (rules + changed in 2022, so compare per year). A sim that passes only on the years it was calibrated on + has fitted the scorecard, not reality. +- Done when: the scorecard passes on its main rows, or the remaining gaps are named with a cause. + +**Checkpoint:** decide which knobs become the evaluation default. + +### Phase 4: Adopt in evaluation (3–5 days) + +- The round-robin uses randomized physics by default, with several seeds per pairing (number + set by the per-match noise: goal difference varies by about 3 goals per match) and paired seeds for A/B comparisons. +- Run a new baseline round-robin. Every cached match reruns: a cache invalidation, so batch it + with any pending renames. +- Update `docs/STRATEGY_DEVELOPMENT.md` (evaluation), `docs/signals.md` (scorecard rows that + become signals) and `docs/strategies.md` (new results). +- Done when: the strategy catalog has been re-ranked under the new evaluation, and the + changes in ranking are explained. + +### Phase 5: Our own robots (when hardware tests are possible) + +Replace league ranges with measurements of our robots. Tests, each short and repeatable: + +| Test | Measures | +|---|---| +| Step command, timed from the vision log | End-to-end latency | +| Repeated kicks at a target, at several charge levels | Kick speed mean and spread, direction error, drop with charge | +| Rolling and kicked balls on the competition carpet (and our lab floor) | Deceleration, sliding-to-rolling switch | +| Speed and acceleration ramps, per robot | Motion limits, spread across robots | +| Dribble while turning or being touched; release | Hold strength, loss rate, release speed | +| Ball at rest under the cameras | Vision noise and dropped frames for our setup | +| A long run with radio logging, through a full battery | Lost commands, robot dropouts, speed and kick drop as the battery drains | + +Ranges for our robots then replace the league ranges for our side only. Opponents keep the +league ranges. + +## P1: Value model on real data + +The xG model is already a small value model fitted on reality, for shots only. The extension +values every situation: V = P(we score within 10 s) − P(they score within 10 s), from a dozen +interpretable features (ball position and zone, who has the ball, defenders goal-side, +attackers ahead of the ball, keeper position, the restart it came from). Football analytics +calls these expected threat and VAEP. + +- **Fit and check** on real logs, held out by match, like the xG model: calibration and AUC. +- **Score sim strategies** by crediting each action (pass, dribble, interception, shot) with + the change in V it caused. Reported next to standings in the round-robin summary. +- **Validate** that ranking by it agrees with match results once the sim is randomized. +- **Limits:** + - 54 real matches (118 with 2022–2024) give about 1,300 shots and 5 live hours: enough for + a compact model, not for a learned world model or policy. + - Where sim strategies reach situations the real data rarely contains, the model is + guessing. Measure the overlap, and fall back to match results there. + - V assumes league-average play from each situation on, so it cannot credit a tactic whose + payoff comes after the situation it scores. +- The 2026-10-12 sim-trained value model failed as a cheaper outcome partly because it was + trained on sim outcomes; this one is not. + +**First results, 2026-10-16** (`~/dev/ssl/xg-analysis/value/`, report2.md and +estimators/report.md). Fitted on the 118 real matches only (486 goals): two boosted-tree +classifiers, "we score next within H s" and "they do", V their difference. On held-out years at +H = 10 s, AUC 0.82 and R² 0.23 (0.17 without the last second before goals), against 0.73 / 0.08 +for ball position alone. V rises toward the opponent goal and orders our ball > loose > theirs. +On the 66-match v3 baseline, as a way to rank strategies with fewer matches: mean V (k = 0) +fails again (split-half 0.05 against 0.27 for goal difference). Real goals for k s and then V +improves with k, but the best (k = 20 s, split-half 0.41) is only a little better than +discounted goals with no V at all (0.39), and both are inside each other's noise. +Most of the gain over goal difference needs no model: goal difference per live minute is +already 0.30. So V does not yet save matches; it is for explaining results and for scoring +paired starts cut off at T s (the scenario bench, retired 2026-10-17). 12.7% of sim states have some feature outside the real +1st–99th percentile range (mostly ball speed toward goal). + +Pass 3 (2026-10-17, `report3.md`): converted real logs hold an unseen ball at its last +position with zero velocity and do not keep the seen flag (`ssl_log.py` `finish()`), so a hidden +ball reads as a stopped one. Dropping balls held over 0.5 s, frames with no robots of a team and +the last 80 live s, and weighting matches equally, changes little: H = 10 s AUC 0.805 (pass 2 +0.821, on a different set of states); in the ranking test the best V estimator rises to +split-half 0.42 (discounted goals alone 0.39, goal difference per live minute 0.30). +Writing `ball_seen` into converted logs would let a later pass tell hidden from stopped. + +## Evaluation infrastructure (2026-10-17) + +Built instead of a cheaper outcome (V, shots, xG, stat combinations: all rejected, see +`docs/signals.md`, "Ranking"): `tools/evaluation/evaluate.py` judges a candidate against the +champion of a fixed, versioned pool (`tools/evaluation/pools/pool_v1.json`) in rounds of seeded +noisy matches, with ratings from goal margins (`analysis/ratings.py`) and a ledger of every +match (`replays/eval/ledger.jsonl`); see `docs/STRATEGY_DEVELOPMENT.md`, "Evaluating a strategy". +This covers Phase 4's "several seeds per pairing, paired seeds for A/B". + +Measured on a second noisy round-robin (`tournament_20261017_002337`, seed 2, same 12 +strategies and code as `tournament_20261016_201213`, seed 1): +- Per-match goal-difference SD 1.85 (seed 1) and 2.83 (seed 2); residual SD after strengths 1.9, + close to normal tails. "~3 goals" above was the old sim. +- The noise is drawn per colour and robot, and config_a is always yellow: seed 1 gave yellow + +0.26 ± 0.23 goals per match, seed 2 gave blue +0.64 ± 0.35. split_shape went from +1.5 to + +4.9 between seeds. evaluate.py plays the candidate and the champion in the same colour for + each seed. +- Same-seed matches against a common opponent correlate slightly between *different* + strategies (residual correlation +0.03 against -0.11 across seeds), but fully for identical + code: a match is a pure function of (both strategies' code, seed, colours). A byte copy of + split_shape (6 rounds, 2026-10-17) reproduced every one of the champion's 24 results against + the opponents, while the old decision rule (from the ratings fit, which treats every match + as independent) put P(copy better) at 0.93 after four lucky head-to-heads. The decision now + uses the paired differences, with their spread estimated from the data + (`ratings.paired_difference`): the copy stays at P 0.55-0.68, undecided. Small edits of a + strategy, the common case, sit between the two and gain the most. +- Mirror matches of one strategy (yellow minus blue over seeds 1-6: +1, -4, +1, 0, -2, -9) show + how much a colour's robot draws can matter in one match. Giving both teams the same draws + narrowed it (+2, 0, -1, -5, +1, 0) but not to zero, the rest being the match's own chaos; not + adopted, since it would replay the whole ledger and the paired decision already cancels the + draws (candidate and champion get identical ones against each opponent). + +## P2: Structured strategy search + +Search over what the repo already composes: +- which tactics fill each slot; +- tactic parameters; +- the partitioner that splits robots between slots (for example `split_shape`'s + possession split). + +Candidates are scored by `evaluate.py` (match results in the randomized sim, paired seeds; the +value signal was dropped as a scorer). The 2026-10-11 search (PR #142) is the manual version of this loop: candidates +written by hand, screened by round-robin, held out on a ladder (retired 2026-10-17, replaced by `evaluate.py`). Its lesson carries over: a lead +from one match per pairing is not evidence. + +## Risks and rules + +- **Don't tune the sim toward goal totals.** Only components, with a named physical cause each. +- **League ranges are a prior.** Don't treat the league median as our robot. +- **More randomness means more matches** for the same confidence. Paired seeds and sequential + stopping keep that affordable. Measure the cost in Phase 3 before adopting. +- **Real logs are other teams under real referees.** Live time and restarts differ for + referee reasons, not physics. Compare per live minute. + +## Related + +- [`signals.md`](signals.md): how strategies are measured. +- [`roadmap.md`](roadmap.md): the workstream entry. +- `tools/convert_ssl_log.py`: official logs as replays. +- `vendor/rSim/FORK_NOTES.md`: physics fork changes. diff --git a/docs/strategies.md b/docs/strategies.md index f52557e2..2be50fb1 100644 --- a/docs/strategies.md +++ b/docs/strategies.md @@ -7,16 +7,16 @@ Run: - `pixi run python tools/evaluation/round_robin.py [name ...]` — full-match round-robin (two halves of 300 s of playing time) over every config (or the named ones). Common flags: `--reuse`, `--strict`, `--pair A B` (one match); `--help` lists all. -- `pixi run python tools/evaluation/ladder.py ` — one candidate against the top of the - newest round-robin, over both sides and both kickoffs. +- `pixi run python tools/evaluation/evaluate.py ` — judges a candidate against the champion + of the reference pool (`tools/evaluation/pools/pool_v1.json`). The way to judge a strategy. - `pixi run python tools/debug_match.py --strategy --opponent [--dump-ticks PATH]` — one instrumented matchup. rsim is deterministic: re-running an identical pair gives an identical result. Variance comes only from `--both-sides`, side/kickoff cells, or `--fuzz-restarts`. -**In the round-robin**, in order of the 2026-10-10 standings (below). A round-robin or a -ladder with no strategies named plays these. +**In the round-robin**, in order of the 2026-10-10 standings (below). A round-robin with no +strategies named plays these. | Strategy | Description | |---|---| @@ -34,9 +34,9 @@ ladder with no strategies named plays these. | `clear_danger` | `counter_flow` postures plus an own-third `ClearBallTactic` danger-clearance valve. | **Retired** (2026-10-10): the bottom ten of the 2026-10-05 round-robin. Left out of a round-robin -or a ladder pool unless named (`RETIRED` in `tools/evaluation/round_robin.py`). Their code stays: -kept strategies and tests build on some of them. A retired strategy comes back by laddering -against the round-robin's top. +unless named (`RETIRED` in `tools/evaluation/round_robin.py`). Their code stays: +kept strategies and tests build on some of them. A retired strategy comes back by being judged +with `evaluate.py` against the pool. | Strategy | Description | |---|---| @@ -53,31 +53,39 @@ against the round-robin's top. `default`: Everyone runs `PassAndShootTactic`; only 2 robots are ever commanded (see bugs). Excluded from round-robin discovery. -## Latest results +Strategies are judged with `evaluate.py` against the reference pool `pool_v1` +(`docs/STRATEGY_DEVELOPMENT.md`, "Evaluating a strategy"). The round-robin results below are a +snapshot of the catalog, not the verdict on any one strategy. -**Round-robin** (2026-10-10, strategy-guard at a17e2791, the 12 strategies above, 66 matches, -600 s, `replays/tournament_20261010_090819/summary.json`). Points are 3 a win, 1 a draw; 8 of 66 -matches were draws. No match stalled; 99% of 1802 restarts reached NORMAL_START. +## Latest results -Against the 2026-10-05 run: the retired ten are gone from the pool, so every strategy's record is -now against the top 12 only; the teams change ends at half-time; and an aimless clearance is a free -kick at the kick point. `split_shape` won all 11; `clear_danger`, fourth before, is last. +**Round-robin** (2026-10-17, sim-realism-20261016 at 423f63a0, the 12 strategies above, 66 +matches, two halves of 300 s of playing time, seeded sim noise `v3_seed2`, +`replays/tournament_20261017_151052/summary.json`). Points are 3 a win, 1 a draw; 14 of 66 +matches were draws. 4 matches stalled (three committed robots frozen about 10 s, and one kick-off +that waited the referee's 15 s cap, fixed after this run in 1731e904); 97% of 3232 restarts +reached NORMAL_START. + +Against the 2026-10-10 run: rsim is our fork with the v3 sim noise (latency, motion limits, ball +friction, kick direction), and restarts, ball placement and the carry limit were fixed, so goals +fell from 7.1 a match to 4.0 and the results are not comparable with earlier runs. +`split_shape` is still first, unbeaten; `counter_flow`, second before, is eleventh. What each strategy does well and badly in this run, in figures: [`signal_report.md`](signal_report.md). | Strategy | W-D-L | GF-GA | Points per match | |---|---|---|---| -| `split_shape` | 11-0-0 | 80-26 | 3.00 | -| `counter_flow` | 7-2-2 | 35-24 | 2.09 | -| `score_aware_counter_flow` | 6-2-3 | 32-24 | 1.82 | -| `press_and_pass` | 5-2-4 | 40-35 | 1.55 | -| `clear_press_plus` | 5-2-4 | 34-31 | 1.55 | -| `high_press` | 5-1-5 | 36-47 | 1.45 | -| `tiki_taka_plus` | 5-1-5 | 33-48 | 1.45 | -| `give_and_go_solo` | 5-0-6 | 44-51 | 1.36 | -| `overload_press` | 4-0-7 | 52-51 | 1.09 | -| `press_trigger_flow` | 2-2-7 | 26-35 | 0.73 | -| `decoy_and_overload` | 2-2-7 | 38-55 | 0.73 | -| `clear_danger` | 1-2-8 | 21-44 | 0.45 | +| `split_shape` | 10-1-0 | 66-14 | 2.82 | +| `clear_press_plus` | 5-5-1 | 14-9 | 1.82 | +| `overload_press` | 6-2-3 | 22-18 | 1.82 | +| `clear_danger` | 5-2-4 | 20-24 | 1.55 | +| `tiki_taka_plus` | 5-1-5 | 23-21 | 1.45 | +| `high_press` | 4-3-4 | 24-27 | 1.36 | +| `press_trigger_flow` | 4-1-6 | 13-19 | 1.18 | +| `give_and_go_solo` | 3-3-5 | 20-30 | 1.09 | +| `press_and_pass` | 3-3-5 | 14-28 | 1.09 | +| `decoy_and_overload` | 3-2-6 | 16-27 | 1.00 | +| `counter_flow` | 2-4-5 | 17-21 | 0.91 | +| `score_aware_counter_flow` | 2-1-8 | 16-27 | 0.64 | ## Known open bugs diff --git a/docs/tactic_model_design_decisions.md b/docs/tactic_model_design_decisions.md index 626e9b45..62316bc4 100644 --- a/docs/tactic_model_design_decisions.md +++ b/docs/tactic_model_design_decisions.md @@ -45,7 +45,23 @@ warns every 100 consecutive committed ticks. **§5. Single-writer partition.** One place (the `Partitioner`, via `Strategy`) decides the whole outfield assignment once per tick, before any Tactic runs. Two Tactics can never contend for a robot, so no locks are needed. The goalkeeper is pinned outside the scheduler -(`Strategy.set_goalkeeper`). +(`Strategy.set_goalkeeper`). The per-tick order that enforces this, from `Strategy._choose_partition` +and `_validate_partition`: + +```mermaid +flowchart TD + slot{"each tactic slot:
is_committed() and not deadline-released?"} + pinned["robots stay with that slot
(absolute veto, §3)"] + free["free pool = outfield robots not pinned"] + avail["available_tactic_ids =
applicable() and not pinned"] + part["Partitioner splits the free pool
(the only code that assigns free robots)"] + val["Strategy._validate_partition:
no robot in two slots, none outside the outfield pool"] + run["each slot's Tactic.tick runs on its own robots"] + + slot -- yes --> pinned --> free + slot -- no --> free + free --> avail --> part --> val --> run +``` **§6. No state-machine framework.** No Sumatra-style `IState`/`AState` base class; phase logic lives in `mem`. Add a concept only after a concrete case breaks the plain version. diff --git a/docs/tools.md b/docs/tools.md index c6e0636f..04c1761d 100644 --- a/docs/tools.md +++ b/docs/tools.md @@ -13,16 +13,24 @@ Every script in the repository, by what it is for. Run them from the repository | `lint` | the whole pre-commit stack (black, ruff, isort) on every file | | `precommit-install` / `precommit-uninstall` | install or remove the pre-commit hook | | `main` | `main.py`: watch one match between two strategies in rsim (below) | +| `build-planner` | build the C++ fast-path planner (`utama_core/motion_planning/src/fastpathplanning/cpp`) into `build/fpp` with cmake | | `replay [-n NAME] [-p]` | play a legacy pickle replay `replays/NAME.pkl` in the rSoccer viewer (newest if no name; `-p` step by step). Today's `.npz` replays open in the dashboard instead | | `runs` | list the tournament runs in `replays/`: start time, commit, matches, stalls, arguments | | `debug-robots` | keyboard/click teleoperation GUI for real robots over the serial radio | +## Worktrees + +| Script | Purpose | +|---|---| +| `tools/setup_worktree.sh` | Run inside a git worktree: links `.pixi` to the main checkout's and copies the gitignored compiled `.so` extensions (planner, rsim). Idempotent; one line per action | + ## Playing matches | Script | Purpose | |---|---| -| `tools/evaluation/round_robin.py` | Every strategy config against every other, one full match per pair (two halves of 300 s of playing time); writes `replays/tournament_/` with `summary.json`. `--pair A B` plays one match; `--reuse` replays only matches whose code changed, plus a 5% spot-check of reused ones; `summary.json`'s `reuse.mismatches` lists any that came out different, which means a match no longer replays exactly. The ground truth for which strategy is better | -| `tools/evaluation/ladder.py` | One candidate against a reference pool (default: the top 5 of the newest complete round-robin): mirrored pairs over side and kickoff, up to 8 matches per opponent, stopping once a pairing is clear (`decided`). Reuses and fills the round-robin's match cache; writes `replays/ladder_/ladder.json`. A cheaper check of one strategy than a round-robin, not a ranking of all | +| `tools/evaluation/round_robin.py` | The whole-catalog health and stall check: every strategy config against every other, one full match per pair (two halves of 300 s of playing time); writes `replays/tournament_/` with `summary.json`, and adds its matches to the evaluation ledger. `--pair A B` plays one match; `--reuse` replays only matches whose code changed, plus a 5% spot-check of reused ones; `summary.json`'s `reuse.mismatches` lists any that came out different, which means a match no longer replays exactly. `--sim-noise JSON` (inline or a file) plays every match with the same seeded latency, kick speed and direction noise, ball friction, motion limits and vision noise (`SimNoise`, `docs/STRATEGY_DEVELOPMENT.md`). A single strategy is judged with `evaluate.py`, not by its place here | +| `tools/evaluation/evaluate.py` | One or more candidates against the champion of a fixed reference pool (`tools/evaluation/pools/pool_v1.json`), in rounds under seeded sim noise, until its paired results against the champion say better (P >= 0.99), worse (P <= 0.2) or undecided after 6 rounds; `--held-out` plays the pool's held-out opponents before promoting. Results go to the evaluation ledger `replays/eval/ledger.jsonl` (round-robins add theirs too), reports to `replays/eval/reports/`; no replays kept. The way to judge a strategy (`docs/STRATEGY_DEVELOPMENT.md`, "Evaluating a strategy"). `--explain` (off by default) also writes pictures and a `summary.json` of each candidate against the champion to `replays/eval/explain/` (`tools/evaluation/explain.py`); they explain the verdict, never change it | +| `tools/evaluation/explain.py` | `explain.py RUN_DIR CANDIDATE CHAMPION --out DIR`: positions (with and without the ball), shots for and against, and fouls by rule, for two strategies from the matches in a run dir (a round-robin's or an `evaluate.py` run's), plus `summary.json` with the numbers behind them; goals stay the verdict. Used by `evaluate.py --explain` | | `tools/evaluation/match.py` | Match construction shared by the two above (not run directly) | | `dashboard_server.py` | Standalone browser dashboard at http://localhost:8080: replays and tournament results | @@ -33,35 +41,67 @@ What these measure and how far to trust each: [STRATEGY_DEVELOPMENT.md](STRATEGY | Script | Purpose | |---|---| -| `tools/scenario_bench.py` | Paired A/B of a candidate against a baseline on a bank of situation starts harvested from a round-robin (10 s each, against the opponent each came from): change in P(goal) per situation type, weighted by real or sim situation rates (`--weights`) into goals per match; also builds new banks (`--harvest-from`), and plays one start with a timeline and picture (`--play`) | | `tools/signal_report.py` | Figures of a round-robin's strategy signals for [signal_report.md](signal_report.md) | -| `utama_core/analysis/goal_ledger.py` | `python -m utama_core.analysis.goal_ledger `: every goal attributed to the situation that started its play (a restart, or the ball won in open play, by third); per strategy, situations per match × P(goal) = goals per match, scored and conceded. Reads only the replay and its referee file, so it runs on converted official logs too. `--rates` writes the situation rates the scenario bench weights by. See [signals.md](signals.md#where-do-goals-come-from) | -| `tools/bench_vs_standings.py` | How well the scenario bench's effect on goal difference per match predicts a round-robin's, for several candidates against one baseline (Pearson, Spearman) | -| `tools/metric_correlation.py` | Offline study of which cheap per-match metrics predict results; computes the *offline* signals in [signals.md](signals.md) | +| `tools/pitch_maps.py` | Where each strategy's robots stand, shoots and fouls, drawn on the pitch, for [signal_report.md](signal_report.md#on-the-pitch). `--findings PATH`: six descriptive `zone_share` facts per strategy (its in- and out-of-possession samples in each third; no threshold) | +| `utama_core/analysis/goal_ledger.py` | `python -m utama_core.analysis.goal_ledger `: every goal attributed to the situation that started its play (a restart, or the ball won in open play, by third); per strategy, situations per match × P(goal) = goals per match, scored and conceded. Reads only the replay and its referee file, so it runs on converted official logs too. `--rates` writes the situation rates per team per live minute. See [signals.md](signals.md#where-do-goals-come-from) | +| `utama_core/analysis/physics_ranges.py` | `python -m utama_core.analysis.physics_ranges --real --sim `: physics parameter ranges for the sim-realism plan ([sim_realism.md](sim_realism.md)): ball deceleration by speed and time since a kick, rolling-segment fits, robot speed, acceleration, braking and turn-rate percentiles per robot, and dropped detections, with spreads between matches, robots and teams | | `tools/check_strategy_branch.py` | CI check on `strategy/*` pull requests: the branch must not change the evaluation or its opponents | +### Validation studies + +These check how far cheap metrics agree with round-robin results. They do not judge a strategy: use `evaluate.py`. + +| Script | Purpose | +|---|---| +| `tools/metric_correlation.py` | Which cheap per-match metrics predict results; computes the *offline* signals in [signals.md](signals.md) | + ## Real matches | Script | Purpose | |---|---| -| `tools/convert_ssl_log.py` | Official RoboCup SSL game logs to replays in our format, so the analyses run on real matches: given logs, or `--corpus 2022-2026` to download and convert every published Division B match, deleting each raw log once converted. What a converted match holds and its limits: [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md#real-matches) | +| `tools/convert_ssl_log.py` | Official RoboCup SSL game logs to replays in our format, so the analyses run on real matches: given logs, or `--corpus 2022-2026` to download and convert every published Division B match, deleting each raw log once converted (`--keep-raw` keeps it; downloads cut short are resumed; each conversion's memory is capped by `--max-memory-gb`). Also writes sidecars for measuring physics: the tracker's kicks and visibility, and with `--raw-vision` every raw camera detection; `--backfill-extras` adds them to matches already converted. What a converted match holds and its limits: [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md#real-matches) | +| `tools/realism_scorecard.py` | Our sim against real Division B matches, component by component: attempts per live minute (open play and set pieces), attempt distance, real xG per attempt, finishing, goal rate and keeper saves per xG band, shot outcomes (keeper, defender, wide, post, crossed with no goal), aim outside the posts and miss distance, ball slow-down, kick speed, keeper depth, keeper and nearest-defender reaction time (`utama_core/analysis/realism/reaction.py`), pass line offset and completion (`passes.py`). Each row: the real value and 95% interval, the sim value, and whether it is inside. `--real` rebuilds the committed reference (`utama_core/analysis/realism/real_2025_2026.json`: the real xG model and rows) from converted logs; `--sim RUN_DIR` scores a round-robin against it. Attempts exclude passes and kicks from beyond 6 m. See [sim_realism.md](sim_realism.md) | ## Debugging a match +The four picture tools (`match_timeline.py`, `restart_picture.py`, `plan_trace.py`, `pitch_maps.py`) each take +`--findings PATH` (`-` for stdout) and write the problems they flag as JSON lines, one `Finding` per line +(`utama_core/analysis/findings.py`): + +```json +{"tool": "match_timeline", "kind": "tactic_thrash", "replay": "replays/.../a_vs_b.npz", "side": "friendly", "t0": 0.0, "t1": 480.0, "robots": [4], "value": 5.5, "threshold": 2.0, "unit": "flip-backs per live min", "message": "R4 took a tactic for under 1 s and reverted 11 times in 2.0 min of live play (5.50 per min, threshold 2); first at t=12.3 s.", "image": null} +``` + +Fields: `tool`, `kind`, `replay` (path or null), `side` (`friendly`, `enemy` or null), `t0` and `t1` (seconds or null), +`robots`, `value`, `threshold` (null for a descriptive fact), `unit`, `message` (one sentence with the numbers) and +`image` (the PNG drawn, or null). Filter on the JSON; open the picture to understand a flag, never conclude from it alone. + | Script | Purpose | |---|---| -| `tools/replay_trace.py` | Text trace of a replay window: referee commands, ball, nearest robot per side. The first thing to run on a stall or a voided restart | -| `tools/repro_from_replay.py` | Load a replay's field state at a timestamp into a fresh headless rsim match and tick forward with tracing on | +| `tools/triage.py` | **Start here.** `RUN_DIR [--strategy NAME] [--matches 3] [--restarts] [--out DIR] [--max-pictures 8] [--workers 2]`: each strategy's N worst matches by goal difference, among those it played as config_a (the side with an intentions log: about half its round-robin matches, every other round in an evaluation), every flagged problem with its numbers (`tactic_thrash`, `stall`, and with `--restarts` the `target_in_keep_out` / `keep_out_short` / `target_in_defense_area` restart flags), the top K drawn with the picture tool for that kind. Writes `OUT/index.md` (read first), `OUT/findings.jsonl` and `OUT/pictures/`; default OUT is `RUN_DIR/triage/`. `--restarts` is slow (about 45 s a match). Read-only. See [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md), "Debugging a foul or failure" | +| `tools/replay_trace.py` | Text trace of a replay window: referee commands, ball, nearest robot per side. `--events` lists the window's kicks (with the nearest robot as kicker), possession changes, referee changes and fouls, one line each; `--json` writes rows or events as JSON. The first thing to run on a stall, a voided restart or a foul | +| `tools/match_timeline.py` | One match window as swimlanes: `REPLAY.npz T0 T1 [--side friendly\|enemy]`. Referee command band, one lane per robot coloured by its tactic (ticks at switches, strip where it had the ball), distance of each robot to the ball, and goals, fouls and shots on top; prints switches per minute and the longest tactic span per robot. Use it for thrashing and everyone-chasing-the-ball; example in [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md). `--findings PATH`: one `tactic_thrash` per robot at or above `FLIP_BACKS_PER_MIN` (2) flip-backs per minute of live play (a tactic held under 1 s, then reverted; raw switch rate is not the signal, most strategies switch 10-25 times a minute by design); `timeline_findings(replay, t0, t1, side)` computes it without drawing | +| `tools/repro_from_replay.py` | Load a replay's field state at a timestamp into a fresh headless rsim match and tick forward with tracing on. Plays with the replay's own sim noise by default (`--sim-noise none` for noiseless, `--sim-noise PATH` for another). `--summary ROBOT_IDS` prints each listed robot's tactic, position, distance to the ball, referee command and traced target every 0.25 s; `--trace-out` JSONL row kinds are in `--help` | +| `tools/plan_snapshot.py` | Picture of what the fast-path planner decided for one robot on one tick: `REPLAY.npz --t S --robot ID [--target X Y] [--side enemy]` runs the planner's `_path_to` on the replay's field state and draws the pitch around the robot: every robot with its id and radius, the ball and its keep-out circle, each clearance ring, the target, the carrot returned, and the referee command and distances. `--full-pitch` shows the whole pitch; with no `--target` the robot's position 0.5 s later stands in. Read-only. See [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md), "Debugging a foul or failure" | +| `tools/restart_picture.py` | Picture of where the referee override sends every friendly robot for one restart: `REPLAY.npz --t S [--side enemy]`, or `--all-restarts` for one small panel per restart command in the replay's referee sidecar (at most 8). Runs `RefereeOverride.tick` for the replay's command and draws each robot's commanded target with its role (kicker, placer, keeper), the ball and its 0.8 m keep-out, the placement spot and the other team faded. A robot whose target is inside the keep-out or a defence area it must avoid is flagged in red, with the text box. Read-only. `--findings PATH`: `target_in_keep_out`, `keep_out_short` (the 1 cm stop deadband) and `target_in_defense_area` for every restart in the match, each occurrence, without drawing (`restart_findings(replay, side)`). See [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md), "Referee handling" | +| `tools/plan_trace.py` | The fast-path planner over a time window: `REPLAY.npz --t0 S --t1 S --robot ID [--side enemy] [--target X Y] [--step 0.1] [--lookahead 0.5] [--json]` runs the planner's `_path_to` on every sampled tick and draws one PNG: the robot's path coloured by time with each tick's carrot joined to it, the ball path, the keep-out circle and clearance rings at the end, and time panels for robot-to-ball distance, robot-to-carrot length, speed and the referee command. Without `--target` each tick's target is the robot's position `--lookahead` s later. Robot positions are the replay's, not re-simulated: it shows what the current planner would do from each recorded state. Read-only; `--json` prints the per-tick rows. `--findings PATH` writes one `parked` or `pushed_out` finding per run of at least 1 s (`PARKED_MIN_S`) of short carrot with the target far. See [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md), "Debugging a foul or failure" | +| `tools/moments.py` | Regression moments (`utama_core/tests/moments/`): `fouls RUN_DIR` lists our side's fouls in a round-robin with the restart side (`--skip-own-restart`, `--by strategy,rule` for counts, `--include-stoppages` for out_of_bounds, `--json` for either as JSON; each foul names its match's `replay` .npz relative to the repo root), `capture REPLAY --t S [--out PATH]` writes a moment from a replay, `new NAME --command CMD` writes a hand-made template, `play JSON [--seed N \| --seeds 0-4] [--against REF] [--events] [--strategy FILE.py:FACTORY]` plays one and prints what broke its checks (a seed table, the same moment at another commit, its events, a scratch strategy). See [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md), "Debugging a foul or failure" | | `tools/debug_match.py` | One ad hoc match between two strategies for tactic debugging; `--dump-ticks` writes per-tick poses and commanded targets | Library helpers for the same job (`render_window`, `render_clip`, `MatchLog.trace()`) are described in [STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md). +## Architecture + +| Script | Purpose | +|---|---| +| `tools/package_graph.py` | The import graph between `utama_core/` packages, as a Mermaid flowchart with import counts, from `ast` (nothing is imported), plus any violation of the stated import rules (`replay/` never imports `analysis/`). Its output is kept in [architecture.md](architecture.md) | + ## Benchmarks | Script | Purpose | |---|---| -| `tools/motion_planning_benchmark.py` | The motion planners (`fpp`, `dwa`, `trajsample`) on fixed headless rsim scenarios; see [motion_planning_comparison.md](motion_planning_comparison.md) | +| `tools/motion_planning_benchmark.py` | The motion planners (`fpp`, `dwa`, `trajsample`) on fixed headless rsim scenarios; `--figures DIR` also writes trajectory and summary PNGs; see [motion_planning_comparison.md](motion_planning_comparison.md) | ## Demos and external environments diff --git a/docs/visual_guide.md b/docs/visual_guide.md new file mode 100644 index 00000000..eac6cd56 --- /dev/null +++ b/docs/visual_guide.md @@ -0,0 +1,199 @@ +# Seeing the game + +What our robots actually do, drawn on the pitch from real replays: how each strategy plays, one match +over time, a restart, and the motion planner, from fixed scenarios down to a single tick. Each section +gives the picture, how to read it, what it has shown us so far, and the command that draws it. + +A picture explains a result; it never makes one. Strategies are judged on goals +([STRATEGY_DEVELOPMENT.md](STRATEGY_DEVELOPMENT.md), "Evaluating a strategy"), and every claim a +picture suggests is confirmed with numbers before it is acted on. Each picture tool can also write the +problems it flags as JSON (`--findings`, format in [tools.md](tools.md)), for filtering without +opening images. An agent misread a planner trace once in this repo: the numbers caught it. + +Conventions in every pitch picture: blue is the side being studied, orange the other team (faded when it +is context), the pitch is in metres with the centre at (0, 0), and the strategy maps turn every match +so the studied strategy attacks right. Zone names (defensive third, the box, ...) are the ones in +[pitch_zones.md](pitch_zones.md). + +## Start here: triage + +`pixi run python tools/triage.py RUN_DIR --strategy NAME` picks the strategy's worst matches (by goal +difference, among those it played as config_a), collects every problem the tools flag (tactic flip-backs, +stalls, and with `--restarts` illegal restart targets), and draws the worst few with the picture for that +kind. Read its `index.md` first: each problem's numbers, then the picture path. About 20 s a match +(a minute with `--restarts`); `evaluate.py --explain` runs it on the candidate's two worst matches. + +A stall it found in `split_shape` against `tiki_taka_plus`: from 783 to 786 s, four of our robots +oscillate with the ball frozen. The picture is the match timeline around it, recorded data only: + +![a stall in the match timeline](img/match_timeline/stall_split_shape_773-796.png) + +Robots 2-4 switch between `ShadowAndMarkTactic` and `LeadAndSupportTactic` together, three times within +half a second at 780.5 s and again at 782.4, 783.2 and 785.2 s, while robots 0, 1, 2 and 5 stay within +about 2 m of the ball and robot 4 stays 5-8 m away. + +## 1. How a strategy plays + +### Where its robots stand: the position map + +![positions in possession](img/signals/positions_attack.png) + +One panel per strategy from the latest round-robin, in order of points per match (the number after the +name). Darker cells hold more of the outfield robots' samples while the strategy has the ball; black dots +are each robot's mean position. The same picture without the ball: + +![positions out of possession](img/signals/positions_defence.png) + +What it shows: + +- Most strategies stand on **vertical lines**: robots spread across the pitch at one depth, one line + just outside our box (x ≈ −2.6 m) and one near theirs (x ≈ +3.0 m), with or without the ball. + Which tactic draws them, from the intentions log (live play, robots on the line): + - the line in our half is mostly `BlockShapeTactic`'s screen, held at a fixed depth by design + (`_SCREEN_OFFSET` in front of our box, shifting only sideways with the ball): 73-83% of the + samples there in `clear_danger`, `counter_flow`, `press_trigger_flow`, `score_aware_counter_flow` + and `clear_press_plus`. Those robots stand still 57-63% of the time, with the ball a median + 2-3 m ahead of them. `split_shape` and `decoy_and_overload` draw the same kind of line with + `ShadowAndMarkTactic` a little deeper (x ≈ −3.0 m). + - the lines from `GiveAndGoTactic` (x ≈ −2.4 and +3.0 m in `give_and_go_solo` and `high_press`) + are not explained yet: the ball is at the same depth (median 0 m ahead), mostly near a + touchline (median |y| 2.4-2.6 m). + A fixed line is not by itself why a strategy loses: `clear_danger` holds the screen and ranks + fourth. Whether a ball-following depth beats it is a question for `evaluate.py`, not this + picture. +- `split_shape`, the top scorer, keeps most of its outfield robots in **its own defensive third even + with the ball**, and attacks with one or two. `overload_press` is the opposite: a block just past + halfway, in and out of possession. +- `decoy_and_overload` sits deep in a cluster in front of its own box. + +### Where it shoots from, and concedes: the shot map + +![shots for and against](img/signals/shots.png) + +Blue are the strategy's own shots at the spot they were struck from (filled: a goal), faded orange the +shots it conceded. `split_shape` takes 241 shots in its 11 matches, from all over the attacking half, +two and a half times the next strategy; the others take 69-98. + +### Where it fouls + +![fouls by rule](img/signals/fouls_map.png) + +Each foul at the ball's spot, coloured by rule (one fixed colour per rule, the same in every panel): +which rules a strategy breaks, and where on the pitch. + +Draw: `pixi run python tools/pitch_maps.py replays/tournament_` (a full round-robin, about 3 min). +The rest of the strategy report, as numbers: [signal_report.md](signal_report.md). + +## 2. A candidate against the champion + +![candidate against champion, positions](img/explain/positions.png) + +The same maps for just two strategies, a candidate and the champion, side by side on one colour scale; +`evaluate.py --explain` draws them (with shots, fouls and a `summary.json`) before an evaluation removes +its replays. This example is drawn from the round-robin: `split_shape` holds 37% of its possession in the +attacking half against `clear_press_plus`'s 65%, yet takes 21.9 shots a match against 6.3, and commits +twice the fouls (10.0 against 4.7 a match, mostly ball held in the defense area and crashing). + +Draw: `tools/evaluation/evaluate.py CANDIDATE --explain`, or `tools/evaluation/explain.py RUN_DIR +CANDIDATE CHAMPION --out DIR` on any run dir. + +## 3. One match over time + +![split_shape timeline](img/match_timeline/split_shape_100-160.png) + +Sixty seconds of `split_shape` against `tiki_taka_plus`. From the top: events (fouls, shots), the +referee command, one lane per robot coloured by the tactic it was in (a tick at each switch, a black strip +while it had the ball), and each robot's distance to the ball. Hatched lanes are stoppages, where the +referee override moves robots instead of their tactics. + +What it shows: robots 2-4 switch between `ShadowAndMarkTactic` and `LeadAndSupportTactic` together, +several times within a few seconds (112-117 s, 139-146 s), while robots 1 and 5 hold one tactic. Over a +whole round-robin, a tactic held under 1 s and then reverted (a flip-back) happens about 5 times a +minute in most strategies that split attack and defence on possession, 13 in `press_and_pass`, and +almost never in `give_and_go_solo`, `high_press` or `decoy_and_overload`. Not fixed yet. + +Draw: `pixi run python tools/match_timeline.py REPLAY.npz T0 T1`. + +## 4. A restart + +![free kick against us](img/restart_picture/free_kick_against_us.png) + +Where the referee override sends each of our robots at one restart: a free kick against us, ball at the +cross, the 0.8 m keep-out circle dashed. Each robot moves toward its commanded target (grey cross; red +when a target breaks a rule) or holds; the table lists each target and its distance from the ball. +Robot 3 starts inside the circle and is sent to exactly 0.80 m. + +Across more replays this picture found a real defect: the step that clears robots out of the circle +stops 1 cm short and leaves them at 0.79 m (`keep_out_short` in the findings). Not fixed yet. + +Draw: `pixi run python tools/restart_picture.py REPLAY.npz --t S`, or `--all-restarts` for one panel per +restart command in the match. + +## 5. The motion planner on fixed scenarios + +The benchmark runs the three planners (`fpp`, the fast path planner we use, `dwa` and `trajsample`) on +fixed scenarios in headless rsim, with no strategy involved. + +![static slalom](img/motion_planning/trajectories_static_slalom.png) + +One robot crosses a staggered line of three parked opponents. `fpp` and `trajsample` drive almost +straight through (the gaps are wide enough) at 2 m/s, 12 cm from the nearest robot at worst; +`trajsample` brakes briefly at each obstacle. `dwa` slows down from 1.3 s, weaves, stalls beside the +middle obstacle at x = 0 and touches it. + +![crossing](img/motion_planning/trajectories_crossing.png) + +Two of our robots, both driven by the planner under test, cross at right angles (dashed is the +second robot). With `fpp` one robot holds its line and the other swerves round it: closest pass 0.18 m, +both arrive. With `dwa` they converge and collide, and neither arrives. `trajsample` gives a wide berth +(0.9 m) but detours 55% further and takes 5.1 s instead of 3.3. + +![benchmark summary](img/motion_planning/summary.png) + +Every metric per planner and scenario, each on its own scale; hollow dots are failed runs. + +Draw: `pixi run python tools/motion_planning_benchmark.py --figures DIR`. How to read the full +benchmark: [motion_planning_comparison.md](motion_planning_comparison.md). + +## 6. The motion planner in a real match + +### One tick + +![planner snapshot](img/plan_snapshot/kickoff_keep_out.png) + +The planner's decision for one robot on one recorded tick: every robot with its clearance ring, the +ball's keep-out circle (dashed), the robot's target and the carrot (the point the planner steers to this +tick). Here `tiki_taka_plus`'s robot 4 (the replay's enemy side) sits on the keep-out circle at a kickoff, 0.799 m from the ball; its target +(−1.0, −0.3) is across the circle. The planner's answer is a carrot 5 cm away: push the robot out to +0.85 m first. + +Draw: `pixi run python tools/plan_snapshot.py REPLAY.npz --t S --robot ID [--side enemy] [--target X Y]`. + +### A window + +![planner trace](img/plan_trace/kickoff_keep_out.png) + +The same robot over 12 s: its path coloured by time, the carrot at each sample (green dots, joined to +where the robot was), then distance to the ball, distance to the carrot, speed and the referee command. +The robot runs in, reaches the circle at about 255.5 s (dipping to 0.76 m), then holds 0.80 m from the ball for the rest of the +prepare-kickoff, 1.76 m short of its target. That was the planner before the push-out fix: it +parked robots on the circle. + +The trace replays recorded positions and runs today's planner on them; it does not re-simulate. So the +robot's path is the old behaviour, and the carrot line (0.05 m, at 0.85 m from the ball) is what today's +planner would do from there: push out first. The annotation names it "pushed out of the keep-out"; a +short carrot with nothing to push out of is labelled "parked". + +Draw: `pixi run python tools/plan_trace.py REPLAY.npz --t0 S --t1 S --robot ID [--side enemy] +[--target X Y]`. + +## Which picture for which question + +| Question | Picture | +|---|---| +| How does this strategy play, overall? | pitch maps (1) | +| Why did the candidate win or lose against the champion? | `--explain` (2) | +| What were the robots doing in this stretch of a match? Thrashing, everyone chasing the ball? | match timeline (3) | +| Where did the referee override put us at this restart, and was it legal? | restart picture (4) | +| Is the planner itself good at driving, with no strategy involved? | benchmark (5) | +| Why did this robot stop, detour or go the wrong way? | plan snapshot, then plan trace (6) | diff --git a/pixi.lock b/pixi.lock index 356456cb..a27a2962 100644 --- a/pixi.lock +++ b/pixi.lock @@ -524,7 +524,7 @@ environments: - conda: https://prefix.dev/conda-forge/linux-64/zlib-1.3.2-h25fd6f3_2.conda - conda: https://prefix.dev/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda - pypi: https://files.pythonhosted.org/packages/fa/66/f7177ab331876200ac7563a580140643d1179c8b4b6a6b0fc9838de2a9b8/numpy-2.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl - - pypi: https://files.pythonhosted.org/packages/d3/90/5c5960e30acd13eb43a780023761218612d26dd540d90b15edfef730b180/rc_robosim-1.2-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl + - pypi: ./vendor/rSim osx-arm64: - conda: https://prefix.dev/conda-forge/osx-arm64/bzip2-1.0.8-hd037594_9.conda - conda: https://prefix.dev/conda-forge/osx-arm64/c-ares-1.34.6-hc919400_0.conda @@ -562,7 +562,7 @@ environments: - conda: https://prefix.dev/conda-forge/osx-arm64/zlib-1.3.2-h8088a28_2.conda - conda: https://prefix.dev/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda - pypi: https://files.pythonhosted.org/packages/05/33/26178c7d437a87082d11019292dce6d3fe6f0e9026b7b2309cbf3e489b1d/numpy-2.0.2-cp310-cp310-macosx_11_0_arm64.whl - - pypi: https://files.pythonhosted.org/packages/f0/af/fbd88656ad694164defc69d032faf606d938db05051c8f1f87449e42e70a/rc-robosim-1.2.tar.gz + - pypi: ./vendor/rSim packages: - conda: https://prefix.dev/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda build_number: 20 @@ -5347,14 +5347,10 @@ packages: purls: [] size: 886953 timestamp: 1772541394570 -- pypi: https://files.pythonhosted.org/packages/d3/90/5c5960e30acd13eb43a780023761218612d26dd540d90b15edfef730b180/rc_robosim-1.2-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl +- pypi: ./vendor/rSim name: rc-robosim - version: '1.2' - sha256: 2df3e53f92362ef88ad7d08b5c96b63b171a53a4deee54ce698d00dd54a6b5ed -- pypi: https://files.pythonhosted.org/packages/f0/af/fbd88656ad694164defc69d032faf606d938db05051c8f1f87449e42e70a/rc-robosim-1.2.tar.gz - name: rc-robosim - version: '1.2' - sha256: 5080167ece949ad83b6e59fa96420ad0a1b5b8abd8c602776b70fa6c127961e7 + version: 1.2.0+utama + sha256: 3ee70be6ae49588d10349b223ee29716b1694b243a04c7523b8a78f667820579 - conda: https://prefix.dev/conda-forge/linux-64/readline-8.3-h853b02a_0.conda sha256: 12ffde5a6f958e285aa22c191ca01bbd3d6e710aa852e00618fa6ddc59149002 md5: d7d95fc8287ea7bf33e0e7116d2b95ec diff --git a/pixi.toml b/pixi.toml index b74eed48..b63fb5f4 100644 --- a/pixi.toml +++ b/pixi.toml @@ -43,7 +43,7 @@ cmake = "<3.27" libode = ">=0.16.2,<0.17" [feature.robosim.pypi-dependencies] -rc-robosim = ">=1.2,<2" +rc-robosim = { path = "vendor/rSim" } # our fork (vendor/rSim/FORK_NOTES.md), built on install numpy = "<=2.0.2" [tasks] @@ -59,6 +59,3 @@ precommit-uninstall = "pre-commit uninstall" [environments] robosim = { features = ["robosim"], no-default-feature = true } - -[target.linux-64.tasks] -main = "python -m main" diff --git a/ruff.toml b/ruff.toml index 9aef3370..49c78724 100644 --- a/ruff.toml +++ b/ruff.toml @@ -1,3 +1,11 @@ +# Exclusions apply to all of ruff. +exclude = [ + "*_pb2.py", + "build/", + "dist/", +] + +[lint] ignore = [ "F401", # Module imported but unused "E402", # Module level import not at top of file @@ -6,10 +14,3 @@ ignore = [ # Enable auto-fix for fixable errors fixable = ["E"] # Fix all fixable E codes (like E402) - -# Optionally, exclude certain files or directories -exclude = [ - "*_pb2.py", - "build/", - "dist/", -] diff --git a/tools/bench_vs_standings.py b/tools/bench_vs_standings.py deleted file mode 100644 index 95de5e9c..00000000 --- a/tools/bench_vs_standings.py +++ /dev/null @@ -1,186 +0,0 @@ -#!/usr/bin/env python3 -# ruff: noqa: E402 -"""Does the scenario bench predict round-robin goal difference? - -Plays a baseline and every candidate once on each start of a bank, each start against the -opponent it came from (`scenario_bench.start_opponent`), and compares per candidate the -bench's effect on goal difference per match (`scenario_bench.situation_effect`, candidate -minus baseline) with the round-robin's: the candidate's mean goal difference per match minus -the baseline's, over the opponents the bench played them against. A start whose opponent is -the candidate or the baseline is left out of that candidate's effect, so both numbers are -against the same opponents. Prints Pearson and Spearman correlations over the candidates. - -The round-robin's own situation rates (sim) are the ones that should predict it; others (real -Division B rates) are printed beside them, compared with the same round-robin for reference. - - pixi run python tools/bench_vs_standings.py \\ - --summary replays/tournament_20261011_230119/summary.json \\ - --bank utama_core/scenario_bench/banks/bank_v8.json --baseline split_shape \\ - --candidates overload_press press_and_pass counter_flow clear_press_plus high_press decoy_and_overload \\ - --weights utama_core/scenario_bench/banks/situation_rates_sim.json \\ - utama_core/scenario_bench/banks/situation_rates_real.json \\ - --workers 15 --output bench_vs_standings.json -""" - -from __future__ import annotations - -import argparse -import json -import statistics -import sys -from concurrent.futures import ProcessPoolExecutor -from pathlib import Path - -REPO_ROOT = Path(__file__).resolve().parents[1] -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -from tools.scenario_bench import ( - _start_result, - load_weights, - situation_effect, - start_opponent, -) -from utama_core.analysis.goal_ledger import WINDOW_S -from utama_core.rsoccer_simulator.src.Simulators.robosim.robosim_wrapper import ( - enable_sim_reuse, -) -from utama_core.scenario_bench.scenario_scorer import _resolve_config_name -from utama_core.scenario_bench.start import BenchScenario, load_bank - - -def goal_difference(summary: dict, config: str, opponents: set[str]) -> float: - """`config`'s mean goal difference per match against `opponents`, from a round-robin's - `summary.json` (each opponent weighted equally).""" - per_opponent: dict[str, list[int]] = {} - for r in summary["results"]: - if r["config_a"] == config and r["config_b"] in opponents: - per_opponent.setdefault(r["config_b"], []).append(r["score_a"] - r["score_b"]) - elif r["config_b"] == config and r["config_a"] in opponents: - per_opponent.setdefault(r["config_a"], []).append(r["score_b"] - r["score_a"]) - return statistics.fmean(statistics.fmean(v) for v in per_opponent.values()) - - -def _ranks(values: list[float]) -> list[float]: - """1-based ranks, ties sharing their average rank.""" - order = sorted(range(len(values)), key=lambda i: values[i]) - ranks = [0.0] * len(values) - i = 0 - while i < len(order): - j = i - while j + 1 < len(order) and values[order[j + 1]] == values[order[i]]: - j += 1 - for k in range(i, j + 1): - ranks[order[k]] = (i + j) / 2 + 1 - i = j + 1 - return ranks - - -def pearson(x: list[float], y: list[float]) -> float: - mx, my = statistics.fmean(x), statistics.fmean(y) - cov = sum((a - mx) * (b - my) for a, b in zip(x, y)) - var = (sum((a - mx) ** 2 for a in x) * sum((b - my) ** 2 for b in y)) ** 0.5 - return cov / var if var else 0.0 - - -def spearman(x: list[float], y: list[float]) -> float: - return pearson(_ranks(x), _ranks(y)) - - -def _play(job: tuple[str, BenchScenario, float]) -> tuple[str, str, dict]: - config, bench_scenario, horizon_s = job - enable_sim_reuse() # a worker plays many starts: keep its sim subprocess between them - return ( - config, - bench_scenario.scenario_id, - _start_result(bench_scenario, config, start_opponent(bench_scenario), horizon_s), - ) - - -def paired_rows(starts: list[BenchScenario], results: dict, candidate: str, baseline: str) -> list[dict]: - """`situation_effect`'s rows for `candidate` against `baseline`, from `results[config][scenario - id]`, leaving out starts played against either of them.""" - rows = [] - for bs in starts: - if start_opponent(bs) in (candidate, baseline): - continue - cand, base = results[candidate][bs.scenario_id], results[baseline][bs.scenario_id] - rows.append( - { - "situation": bs.provenance.situation, - "candidate_outcomes": [cand["outcome"]], - "baseline_outcomes": [base["outcome"]], - "candidate_errors": [cand["error"]] if cand["error"] else [], - "baseline_errors": [base["error"]] if base["error"] else [], - } - ) - return rows - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - parser.add_argument("--summary", type=Path, required=True, help="the round-robin's summary.json") - parser.add_argument("--bank", type=Path, required=True) - parser.add_argument("--baseline", required=True) - parser.add_argument("--candidates", nargs="+", required=True) - parser.add_argument("--weights", type=Path, nargs="+", required=True, help="situation rates files, each reported") - parser.add_argument("--horizon", type=float, default=WINDOW_S) - parser.add_argument("--workers", type=int, default=1) - parser.add_argument("--output", type=Path, default=None, help="write per-start outcomes and the table here") - args = parser.parse_args() - - summary = json.loads(args.summary.read_text()) - baseline = _resolve_config_name(args.baseline) - candidates = [_resolve_config_name(c) for c in args.candidates] - weights = [load_weights(w) for w in args.weights] - _bank_id, bank = load_bank(args.bank) - starts = [bs for bs in bank if bs.provenance.situation and start_opponent(bs) is not None] - print(f"{len(starts)} of {len(bank)} starts playable here; {len(candidates) + 1} configs", flush=True) - - jobs = [(config, bs, args.horizon) for config in [baseline, *candidates] for bs in starts] - results: dict[str, dict[str, dict]] = {config: {} for config in [baseline, *candidates]} - with ProcessPoolExecutor(max_workers=args.workers) as pool: - for done, (config, sid, result) in enumerate(pool.map(_play, jobs, chunksize=4), 1): - results[config][sid] = result - if done % 200 == 0: - print(f" {done}/{len(jobs)} runs", flush=True) - - table = {} - for candidate in candidates: - rows = paired_rows(starts, results, candidate, baseline) - opponents = {start_opponent(bs) for bs in starts} - {candidate, baseline} - table[candidate] = { - "n_starts": len(rows), - "actual": goal_difference(summary, candidate, opponents) - goal_difference(summary, baseline, opponents), - "effects": [situation_effect(rows, w) for w in weights], - } - - names = [Path(w["path"]).stem.removeprefix("situation_rates_") for w in weights] - print(f"\nGoal difference per match relative to {baseline}, against the same opponents") - print(f"{'candidate':42s} {'n':>4s} {'round-robin':>11s} " + " ".join(f"{'bench, ' + n:>16s}" for n in names)) - for candidate, t in table.items(): - cells = [] - for effect in t["effects"]: - total = effect["total"] - se = total["net_per_match_se"] - cells.append( - f"{total['net_per_match']:+7.2f} ± {se:4.2f}" if se is not None else f"{total['net_per_match']:+7.2f}" - ) - print(f"{candidate:42s} {t['n_starts']:4d} {t['actual']:+11.2f} " + " ".join(f"{c:>16s}" for c in cells)) - actual = [t["actual"] for t in table.values()] - print() - for i, name in enumerate(names): - predicted = [t["effects"][i]["total"]["net_per_match"] for t in table.values()] - print( - f"bench ({name} weights) vs round-robin over {len(candidates)} candidates: " - f"Pearson {pearson(predicted, actual):+.2f}, Spearman {spearman(predicted, actual):+.2f}" - ) - if args.output: - payload = {"baseline": baseline, "weights": weights, "table": table, "results": results} - args.output.write_text(json.dumps(payload, indent=1)) - print(f"wrote {args.output}") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/tools/check_strategy_branch.py b/tools/check_strategy_branch.py index d5d9783c..22a4f382 100644 --- a/tools/check_strategy_branch.py +++ b/tools/check_strategy_branch.py @@ -3,7 +3,7 @@ Run: python tools/check_strategy_branch.py --base origin/main -A strategy is scored by the round-robin and the scenario bench. If the branch that changes it, often +A strategy is scored by the round-robin and the evaluation. If the branch that changes it, often written by an agent, could also change the evaluation or its opponents, it could raise its score without playing better. CI runs this on every pull request from a `strategy/*` branch, using the copy of this file on the base branch, so the branch under test can't loosen it. Three checks: diff --git a/tools/convert_ssl_log.py b/tools/convert_ssl_log.py index c0fa3a0c..37df43cd 100644 --- a/tools/convert_ssl_log.py +++ b/tools/convert_ssl_log.py @@ -1,7 +1,8 @@ """Official RoboCup SSL game logs as replays in our format (`utama_core/replay/ssl_log.py`). - pixi run python tools/convert_ssl_log.py LOG_OR_DIR ... --out DIR + pixi run python tools/convert_ssl_log.py LOG_OR_DIR ... --out DIR [--raw-vision] pixi run python tools/convert_ssl_log.py --corpus 2022-2026 --out DIR [--division div-b] [--jobs 4] + [--raw-vision] [--backfill-extras] The first form converts game logs (`*.log.gz`; a directory: every log under it). The second lists the logs the league publishes (https://ssl.robocup.org/game-logs/, the public share @@ -13,12 +14,26 @@ Each replay is `.npz` + `.sparse_referee.pkl` + `.source.json` (teams, score, tracker source, field geometry), `` the log's name without `.log.gz`. Yellow is the friendly team. Downloads are parsed as data only. Do not write them into `replays/`. + +Sidecars for measuring physics (see `ssl_log`): `.kicks.json` (the tracker's kicks) and +`.tracker.zip` (visibility, angular velocity) always, `.vision.zip` (every raw +camera detection, about the replay's size again) with `--raw-vision`. `--backfill-extras` +downloads a log whose replay exists but lacks a sidecar asked for, and adds the sidecars +without rewriting the replay; without it such a log is skipped. `--keep-raw` keeps each +download in `--raw-dir//` instead of deleting it (about 15–20 GB for 2022–2026 Division +B), reuses a kept download instead of fetching it again, and downloads the raw log of a match +that is otherwise complete. + +Each conversion may use at most `--max-memory-gb` of address space (default 7): a 350 MB log peaks +near 4 GB resident. A log that needs more fails with `MemoryError` (listed in `corpus.json`) +instead of exhausting the machine's memory. """ from __future__ import annotations import argparse import json +import resource import shutil import sys import time @@ -59,18 +74,29 @@ def list_logs(year: int, division: str) -> list[dict]: return sorted(logs, key=lambda log: log["name"]) -def _download(url: str, path: Path, attempts: int = 3) -> None: +def _download(url: str, path: Path, expected_bytes: int = 0, attempts: int = 5) -> None: + """Fetch `url` into `path` via `path.part`, resuming a partial `.part`. The share sometimes + closes a connection early without an error, so a download shorter than `expected_bytes` + (when known) is resumed too, not taken as complete.""" part = path.with_name(path.name + ".part") for attempt in range(attempts): try: - with urllib.request.urlopen(urllib.request.Request(url, headers=HEADERS), timeout=120) as response: - with open(part, "wb") as f: + have = part.stat().st_size if part.exists() else 0 + headers = {**HEADERS, **({"Range": f"bytes={have}-"} if have else {})} + with urllib.request.urlopen(urllib.request.Request(url, headers=headers), timeout=120) as response: + resumed = have and response.status == 206 + with open(part, "ab" if resumed else "wb") as f: shutil.copyfileobj(response, f, length=1 << 20) + size = part.stat().st_size + if expected_bytes and size != expected_bytes: + if size > expected_bytes: + part.unlink() + raise OSError(f"{path.name}: got {size} of {expected_bytes} bytes") part.replace(path) return except OSError: - part.unlink(missing_ok=True) if attempt == attempts - 1: + part.unlink(missing_ok=True) raise time.sleep(5 * (attempt + 1)) @@ -83,40 +109,85 @@ def _summary_line(stem: str, summary: dict) -> str: ) +def has_extras(out_dir: Path, stem: str, raw_vision: bool) -> bool: + """Whether a converted replay has the sidecars asked for (per its `source.json`).""" + extras = json.loads((out_dir / f"{stem}.source.json").read_text()).get("extras") + return extras is not None and (extras["raw_vision"] or not raw_vision) + + +def limit_memory(gigabytes: float) -> None: + """Cap this process's address space, so an allocation past it raises `MemoryError`.""" + limit = int(gigabytes * 2**30) + resource.setrlimit(resource.RLIMIT_AS, (limit, limit)) + + def _corpus_job(job: dict) -> dict: - """Download, convert and delete one published log.""" + """Download, convert (or only add sidecars to) and delete (unless kept) one published log.""" stem = log_stem(job["name"]) - out_dir, raw = Path(job["out"]) / str(job["year"]), Path(job["raw_dir"]) / f"{stem}.log.gz" + keep_raw = job.get("keep_raw", False) + out_dir = Path(job["out"]) / str(job["year"]) + raw_dir = Path(job["raw_dir"]) / str(job["year"]) if keep_raw else Path(job["raw_dir"]) + raw = raw_dir / f"{stem}.log.gz" row = {"year": job["year"], "log": job["name"], "stem": stem, "bytes": job["bytes"]} - if (out_dir / f"{stem}.npz").exists() and (out_dir / f"{stem}.source.json").exists(): + exists = (out_dir / f"{stem}.npz").exists() and (out_dir / f"{stem}.source.json").exists() + complete = exists and (not job["backfill_extras"] or has_extras(out_dir, stem, job["raw_vision"])) + raw_ok = raw.exists() and (not job["bytes"] or raw.stat().st_size == job["bytes"]) + if complete and (not keep_raw or raw_ok): return {**row, "status": "exists"} started = time.time() try: - _download(job["url"], raw) - summary = convert_log(raw, out_dir, stem=stem) + raw_dir.mkdir(parents=True, exist_ok=True) + if raw.exists() and not raw_ok: + raw.replace(raw.with_name(raw.name + ".part")) # a cut-short download: resume it + if not raw.exists(): # `_download` writes `.part` and renames it once complete + _download(job["url"], raw, job["bytes"]) + if complete: + return {**row, "status": "raw kept", "seconds": round(time.time() - started)} + summary = convert_log(raw, out_dir, stem=stem, raw_vision=job["raw_vision"], write_replay=not exists) return { **row, - "status": "converted", + "status": "extras added" if exists else "converted", "seconds": round(time.time() - started), "line": _summary_line(stem, summary), } except Exception as exc: # one bad log must not stop the corpus return {**row, "status": "failed", "error": f"{type(exc).__name__}: {exc}", "trace": traceback.format_exc()} finally: - raw.unlink(missing_ok=True) + if not keep_raw: + raw.unlink(missing_ok=True) raw.with_name(raw.name + ".part").unlink(missing_ok=True) -def run_corpus(years: list[int], division: str, out: Path, raw_dir: Path, jobs: int) -> list[dict]: +def run_corpus( + years: list[int], + division: str, + out: Path, + raw_dir: Path, + jobs: int, + raw_vision: bool = False, + backfill_extras: bool = False, + keep_raw: bool = False, + max_memory_gb: float = 7.0, +) -> list[dict]: raw_dir.mkdir(parents=True, exist_ok=True) out.mkdir(parents=True, exist_ok=True) work = [] for year in years: logs = list_logs(year, division) print(f"{year}/{division}: {len(logs)} logs, {sum(log['bytes'] for log in logs) / 1e9:.1f} GB", flush=True) - work += [{**log, "out": str(out), "raw_dir": str(raw_dir)} for log in logs] + work += [ + { + **log, + "out": str(out), + "raw_dir": str(raw_dir), + "raw_vision": raw_vision, + "backfill_extras": backfill_extras, + "keep_raw": keep_raw, + } + for log in logs + ] rows = [] - with ProcessPoolExecutor(max_workers=jobs) as pool: + with ProcessPoolExecutor(max_workers=jobs, initializer=limit_memory, initargs=(max_memory_gb,)) as pool: for row in pool.map(_corpus_job, work): rows.append(row) print( @@ -140,10 +211,32 @@ def main() -> None: parser.add_argument("--division", default="div-b", help="share directory per year (default div-b)") parser.add_argument("--raw-dir", type=Path, help="where downloads go while converted (default OUT/raw)") parser.add_argument("--jobs", type=int, default=1, help="logs downloaded and converted at once") + parser.add_argument("--raw-vision", action="store_true", help="also keep every raw camera detection") + parser.add_argument( + "--backfill-extras", + action="store_true", + help="add missing sidecars to replays already converted, without rewriting them", + ) + parser.add_argument( + "--keep-raw", action="store_true", help="keep each download in RAW_DIR// instead of deleting it" + ) + parser.add_argument( + "--max-memory-gb", type=float, default=7.0, help="address space one conversion may use (default 7)" + ) args = parser.parse_args() if args.corpus: - rows = run_corpus(_years(args.corpus), args.division, args.out, args.raw_dir or args.out / "raw", args.jobs) + rows = run_corpus( + _years(args.corpus), + args.division, + args.out, + args.raw_dir or args.out / "raw", + args.jobs, + raw_vision=args.raw_vision, + backfill_extras=args.backfill_extras, + keep_raw=args.keep_raw, + max_memory_gb=args.max_memory_gb, + ) failed = [row for row in rows if row["status"] == "failed"] print(f"{len(rows) - len(failed)}/{len(rows)} logs converted or already there; failed: {len(failed)}") sys.exit(1 if failed else 0) @@ -153,9 +246,16 @@ def main() -> None: logs += sorted([*path.rglob("*.log.gz"), *path.rglob("*.log")]) if path.is_dir() else [path] if not logs: parser.error("no game logs given") + limit_memory(args.max_memory_gb) for log in logs: - summary = convert_log(log, args.out) - print(_summary_line(log_stem(log.name), summary), flush=True) + stem = log_stem(log.name) + exists = (args.out / f"{stem}.npz").exists() and (args.out / f"{stem}.source.json").exists() + if exists and args.backfill_extras and has_extras(args.out, stem, args.raw_vision): + print(f"{stem}: sidecars already there", flush=True) + continue + replay = not (exists and args.backfill_extras) + summary = convert_log(log, args.out, raw_vision=args.raw_vision, write_replay=replay) + print(_summary_line(stem, summary) + ("" if replay else " (sidecars added)"), flush=True) if __name__ == "__main__": diff --git a/tools/evaluation/evaluate.py b/tools/evaluation/evaluate.py new file mode 100644 index 00000000..ee6f775f --- /dev/null +++ b/tools/evaluation/evaluate.py @@ -0,0 +1,329 @@ +"""evaluate.py — is a candidate strategy better than the champion? As few matches as that takes. + +Run: + pixi run python tools/evaluation/evaluate.py CANDIDATE [CANDIDATE ...] [flags] + + --pool PATH the reference pool (default tools/evaluation/pools/pool_v1.json): the + champion, the opponents, the held-out opponents and the sim noise + --held-out play the pool's held-out opponents instead (the check before promoting) + --baseline NAME compare against this strategy instead of the champion: a new version + against the one it changes. The baseline takes the champion's role, the + champion joins the opponents, and a baseline from the pool leaves them + --max-rounds N rounds before a candidate is called undecided (default 6) + --max-workers N matches at once (default 8) + --explain also write on-pitch pictures of each candidate against the champion to + replays/eval/explain/_/ (positions, shots, fouls and a + summary; see tools/evaluation/explain.py). Explains the verdict, never + changes it. Off by default; the matches are kept until it has run. + +How it decides +-------------- +Matches are played in rounds. In round k each candidate plays every opponent and the champion +once, and the champion plays every opponent, all with sim-noise seed k, so the candidate and the +champion meet the same opponents under the same noise. The candidate is config_a in odd rounds +and config_b in even ones. + +After each round the candidate is compared with the champion on paired matches +(`ratings.paired_difference`): its goal difference minus the champion's against the same +opponent with the same seed and colour, from every match in the ledger (`replay/eval_ledger.py`: +this run's, earlier runs', round-robins'), plus their head-to-head matches. Paired results share +the opponent and the noise, so their difference cancels much of it: all of it for identical +code, little for unrelated strategies; the spread is estimated from the differences themselves. +P(candidate stronger than champion) decides: + +- **better** once P >= PROMOTE (0.99), +- **worse** once P <= DROP (0.2), +- **undecided** after `--max-rounds`: not clearly better, which for a search means drop it. + +Thresholds come from simulating the rule (match noise SD 1.9 goals, six opponents, 6 rounds, +paired results correlated 0 to 1): two equal strategies are called "better" at most 3% of the +time. Unrelated strategies (correlation 0): one goal per match worse is dropped after ~2 rounds, +one goal better found 61% of the time, half a goal better 20%. A small change to a strategy +(correlation ~0.9): one goal better found every time in ~3 rounds, half a goal better 55%. +Strengths printed at the end come from the ratings over all matches (`analysis/ratings.py`). + +A fixture already in the ledger is never played again, so the champion's matches are played +once per seed and reused by every candidate. Replays are not kept: a match is a pure function +of its fixture, so `round_robin.py --pair A B --sim-noise` with the pool's noise and the seed +plays it again. + +Promoting is a decision, not a side effect: a candidate called better should pass `--held-out` +(it must not lose clearly to opponents never used for selection) before you set it as the +pool's champion by hand. +""" + +from __future__ import annotations + +import argparse +import json +import os +import shutil +import sys +from concurrent.futures import FIRST_COMPLETED, ProcessPoolExecutor, wait +from datetime import datetime, timezone +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[2] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from tools.evaluation.match import _short_name, run_match # noqa: E402 +from tools.evaluation.round_robin import ( # noqa: E402 + MAX_MATCH_SECONDS, + _resolve_config_names, + _run_metadata, +) +from utama_core.analysis import ratings # noqa: E402 +from utama_core.config.settings import REPLAY_BASE_PATH # noqa: E402 +from utama_core.replay import eval_ledger # noqa: E402 +from utama_core.replay.fingerprint import CodeGraph # noqa: E402 +from utama_core.rsoccer_simulator.src.Utils.sim_noise import SimNoise # noqa: E402 + +DEFAULT_POOL = Path(__file__).with_name("pools") / "pool_v1.json" +PROMOTE = 0.99 +DROP = 0.2 +MAX_ROUNDS = 6 + + +def round_fixtures(candidates: list[str], champion: str, opponents: list[str], k: int) -> list[tuple[str, str, int]]: + """Round `k`'s (config_a, config_b, seed): each candidate against every opponent and the + champion, and the champion against every opponent; the side under test is config_a in odd + rounds.""" + out = [] + for side in [*candidates, champion]: + rivals = opponents if side == champion else [*opponents, champion] + for o in rivals: + out.append((side, o, k) if k % 2 else (o, side, k)) + return list(dict.fromkeys(out)) + + +def verdict(p_better: float, rounds: int, max_rounds: int) -> str | None: + """The candidate's verdict after `rounds` rounds, or None to play another.""" + if p_better >= PROMOTE: + return "better" + if p_better <= DROP: + return "worse" + return "undecided" if rounds >= max_rounds else None + + +def play(a: str, b: str, seed: int, noise: dict, run_dir: Path, keep: bool = False) -> dict: + """One fixture's `MatchResult` as a dict. The match is recorded like a round-robin's (same + play), then its files are deleted unless `keep` (`--explain` reads them): only the result is kept.""" + tag = f"{_short_name(a)}_vs_{_short_name(b)}_s{seed}" + result = run_match( + a, + b, + duration_seconds=MAX_MATCH_SECONDS, + run_dir=run_dir, + match_tag_suffix=f"_s{seed}", + sim_noise=SimNoise.from_dict({**noise, "seed": seed}), + ) + if not keep: + for f in run_dir.glob(f"{tag}.*"): + f.unlink() + return { + "config_a": result.config_a, + "config_b": result.config_b, + "score_a": result.score_a, + "score_b": result.score_b, + "stats": {"shots": (result.stats or {}).get("shots")}, + } + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("candidates", nargs="+") + parser.add_argument("--pool", type=Path, default=DEFAULT_POOL) + parser.add_argument("--held-out", action="store_true") + parser.add_argument("--baseline") + parser.add_argument("--max-rounds", type=int, default=MAX_ROUNDS) + parser.add_argument("--max-workers", type=int, default=min(8, max(1, (os.cpu_count() or 1) - 1))) + parser.add_argument("--explain", action="store_true", help="also write on-pitch pictures per candidate (see above)") + return parser + + +def main() -> None: + args = build_parser().parse_args() + + pool = json.loads(args.pool.read_text()) + champion = _resolve_config_names([args.baseline or pool["champion"]])[0] + opponents = _resolve_config_names(pool["held_out"] if args.held_out else pool["opponents"]) + pool_champion = _resolve_config_names([pool["champion"]])[0] + if champion != pool_champion: + opponents.append(pool_champion) # against a baseline, the champion is one more opponent + opponents = [o for o in opponents if o != champion] # a baseline from the pool leaves its seat + candidates = [c for c in _resolve_config_names(args.candidates) if c != champion] + noise = SimNoise.from_dict(pool["sim_noise"]).to_dict() + noise.pop("seed", None) + + graph = CodeGraph() + env = eval_ledger.environment(graph, duration_seconds=MAX_MATCH_SECONDS, sim_noise=noise) + fps = {n: graph.strategy_fingerprint(n)[:16] for n in {*candidates, champion, *opponents}} + + def fixture_id(f: tuple[str, str, int]) -> str: + a, b, seed = f + return eval_ledger.fixture_id(env, a, fps[a], b, fps[b], seed) + + run_id = datetime.now(timezone.utc).strftime("eval_%Y%m%d_%H%M%S") + run_dir = Path(REPLAY_BASE_PATH) / run_id # replay files must sit directly under replays/ + run_dir.mkdir(parents=True, exist_ok=True) + print( + f"Pool v{pool['version']}{' (held out)' if args.held_out else ''}: " + f"{'baseline' if args.baseline else 'champion'} {_short_name(champion)}; " + f"opponents {', '.join(_short_name(o) for o in opponents)}; environment {env}\n" + ) + + open_ = list(candidates) + report: dict[str, dict] = {} + played = reused = 0 + with ProcessPoolExecutor(max_workers=args.max_workers) as executor: + for k in range(1, args.max_rounds + 1): + if not open_: + break + ledger = eval_ledger.load(env) + todo = [f for f in round_fixtures(open_, champion, opponents, k) if fixture_id(f) not in ledger] + reused += len(round_fixtures(open_, champion, opponents, k)) - len(todo) + futures = {executor.submit(play, *f, noise, run_dir, args.explain): f for f in todo} + while futures: + done, _ = wait(futures, return_when=FIRST_COMPLETED) + for future in done: + a, b, seed = futures.pop(future) + result = future.result() + eval_ledger.append([eval_ledger.entry(env, graph, result, seed, source=run_id)]) + played += 1 + print( + f" round {k}: {_short_name(a):<24} {result['score_a']} - {result['score_b']} {_short_name(b)}", + flush=True, + ) + + fit = _fit(env, fps, {*open_, champion, *opponents}) + ledger = _up_to_round(eval_ledger.load(env), k) + for c in list(open_): + margin, sd = ratings.paired_difference(*paired(ledger, fps, c, champion, opponents), fit.noise_sd) + p = ratings.p_positive(margin, sd) + v = verdict(p, k, args.max_rounds) + print(f"after round {k}: {_short_name(c):<28} P(better) {p:.2f} margin {margin:+.2f} ± {sd:.2f}") + if v is not None: + open_.remove(c) + report[c] = {"verdict": v, "rounds": k, "p_better": round(p, 3), "margin": round(margin, 2)} + report[c]["margin_sd"] = round(sd, 2) + print() + if args.explain: + _explain(run_dir, run_id, candidates, champion) + shutil.rmtree(run_dir, ignore_errors=True) # this run's own scratch dir; results are in the ledger + + final = _fit(env, fps, {*candidates, champion, *opponents}) + ledger = eval_ledger.load(env) + print(f"Verdicts ({played} matches played, {reused} reused from the ledger):") + for c in candidates: + r = report[c] + print( + f" {_short_name(c):<28} {r['verdict'].upper():<10} P(better) {r['p_better']:.2f} " + f"margin {r['margin']:+.2f} ± {r['margin_sd']:.2f} goals/match after {r['rounds']} round(s)" + ) + r["per_opponent"] = _per_opponent(_up_to_round(ledger, r["rounds"]), fps, c, champion, opponents) + print(f" {'opponent':<26} {'candidate GD':>12} {'champion GD':>12} {'matches':>8} (same seed and colour)") + for o, row in r["per_opponent"].items(): + name = o if o == "head-to-head" else _short_name(o) + print(f" {name:<26} {row['candidate']:>+12} {row['champion']:>+12} {row['matches']:>8}") + if r["verdict"] == "better" and not args.held_out: + print(f" -> next: evaluate.py {_short_name(c)} --held-out, then set it as the pool's champion") + print("\nStrengths (goals per match, ± SD):") + for n in sorted(final.names, key=lambda n: -final.strength(n)[0]): + m, sd = final.strength(n) + print(f" {_short_name(n):<28} {m:+.2f} ± {sd:.2f}") + + out = Path(REPLAY_BASE_PATH) / "eval" / "reports" / f"{run_id}.json" + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text( + json.dumps( + { + "run_id": run_id, + "run": _run_metadata(), + "pool": str(args.pool), + "held_out": args.held_out, + "environment": env, + "rule": {"promote": PROMOTE, "drop": DROP, "max_rounds": args.max_rounds}, + "candidates": report, + "strengths": {n: final.strength(n) for n in final.names}, + "noise_sd": final.noise_sd, + }, + indent=2, + ) + ) + print(f"\nReport: {out.relative_to(REPO_ROOT) if out.is_relative_to(REPO_ROOT) else out}") + + +def _explain(run_dir: Path, run_id: str, candidates: list[str], champion: str) -> None: + """`--explain`: pictures of each candidate against the champion, from this run's kept matches. + Runs before the scratch dir is removed; a failure is printed and never loses the verdicts.""" + from tools.evaluation import explain # matplotlib: imported only when asked for + + for c in candidates: + out = Path(REPLAY_BASE_PATH) / "eval" / "explain" / f"{_short_name(c)}_{run_id}" + try: + explain.explain(run_dir, c, champion, out) + print(f"explain: {_short_name(c)} vs {_short_name(champion)} written to {out}") + except Exception as exc: # noqa: BLE001 - the pictures are optional; the verdicts are not + print(f"explain: {_short_name(c)} failed ({type(exc).__name__}: {exc}); verdicts are unaffected") + + +def _fit(env: str, fps: dict[str, str], players: set[str]) -> ratings.Ratings: + """Ratings over every ledger match in `env` between `players` at their current code.""" + matches = [ + (e["a"], e["b"], e["score_a"] - e["score_b"]) + for e in eval_ledger.load(env).values() + if e["a"] in players and e["b"] in players and fps.get(e["a"]) == e["a_fp"] and fps.get(e["b"]) == e["b_fp"] + ] + return ratings.fit(matches, names=sorted(players)) + + +def _up_to_round(ledger: dict, k: int) -> dict: + """The ledger's matches of rounds 1..k (seeds 1..k): a verdict after round k uses those only, + whatever later rounds the ledger already holds, so it doesn't depend on earlier runs.""" + return {f: e for f, e in ledger.items() if e["seed"] is not None and e["seed"] <= k} + + +def _margins(ledger: dict, fps: dict, side: str, o: str) -> dict[tuple, int]: + """{(seed, side is config_a): goal difference} for `side` against `o`, at current code.""" + out = {} + for e in ledger.values(): + if e["a_fp"] != fps.get(e["a"]) or e["b_fp"] != fps.get(e["b"]): + continue + if (e["a"], e["b"]) == (side, o): + out[(e["seed"], True)] = e["score_a"] - e["score_b"] + elif (e["a"], e["b"]) == (o, side): + out[(e["seed"], False)] = e["score_b"] - e["score_a"] + return out + + +def paired(ledger: dict, fps: dict, candidate: str, champion: str, opponents: list[str]) -> tuple[list, list]: + """The candidate's goal difference minus the champion's against each opponent, over the + matches both played with the same seed and colour, and the candidate's margins against the + champion: `ratings.paired_difference`'s inputs.""" + diffs = [] + for o in opponents: + c, ch = _margins(ledger, fps, candidate, o), _margins(ledger, fps, champion, o) + diffs += [c[k] - ch[k] for k in sorted(set(c) & set(ch))] + return diffs, list(_margins(ledger, fps, candidate, champion).values()) + + +def _per_opponent(ledger: dict, fps: dict, candidate: str, champion: str, opponents: list[str]) -> dict: + """Goal difference against each opponent, the candidate's and the champion's, over the + matches both played with the same seed and colour (so the two columns face the same noise).""" + rows = {} + for o in opponents: + c, ch = _margins(ledger, fps, candidate, o), _margins(ledger, fps, champion, o) + common = sorted(set(c) & set(ch)) + rows[o] = { + "candidate": sum(c[k] for k in common), + "champion": sum(ch[k] for k in common), + "matches": len(common), + } + h2h = _margins(ledger, fps, candidate, champion) + rows["head-to-head"] = {"candidate": sum(h2h.values()), "champion": -sum(h2h.values()), "matches": len(h2h)} + return rows + + +if __name__ == "__main__": + main() diff --git a/tools/evaluation/explain.py b/tools/evaluation/explain.py new file mode 100644 index 00000000..e8ffa6c6 --- /dev/null +++ b/tools/evaluation/explain.py @@ -0,0 +1,225 @@ +"""On-pitch pictures of a candidate against the champion, from the replays of one run dir. + + pixi run python tools/evaluation/explain.py RUN_DIR CANDIDATE CHAMPION --out DIR + +Explains a verdict; it never makes one. Goals (`evaluate.py`'s verdict) are the only ranking; +these pictures and numbers show how the two strategies played, so a search agent can see WHY a +candidate won or lost. Each strategy's view is every match it played in the run dir, against any +opponent (`find_matches`): a match file's stem is `_vs_` (round-robin) or +`_vs__s` (an evaluate.py run), and config_a is the friendly side. + +Writes to DIR: + +- `positions.png`: 2x2, candidate and champion, with and without the ball; one colour scale; +- `shots.png`: each strategy's shots for and against (`pitch_maps.plot_shots`); +- `fouls.png`: each strategy's fouls by rule, one fixed colour per rule (`pitch_maps.plot_fouls`); +- `summary.json`: the numbers behind the pictures per side, and the paired goal difference per + opponent (same opponent, seed and colour, as `evaluate.py` pairs them), where the run has them. +- `triage/`: `tools/triage.py` on the candidate's two worst matches as config_a (`index.md` first). + +Everything is in the attacking frame of `pitch_maps.py` (attacking right). Goals come from the +replay's final referee state (the score), fouls from the match's `.stats.json`. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from collections import Counter +from pathlib import Path +from typing import Optional + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 + +REPO_ROOT = Path(__file__).resolve().parents[2] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from tools import pitch_maps as pm # noqa: E402 +from tools import signal_report as sr # noqa: E402 +from tools import triage # noqa: E402 +from tools.evaluation.match import _short_name # noqa: E402 +from utama_core.analysis.matches import Match, find_matches # noqa: E402 + +ROLES = ("candidate", "champion") + + +def _mean(values: list[Optional[int]]) -> Optional[float]: + known = [v for v in values if v is not None] + return float(np.mean(known)) if known else None + + +def _gd_by_pair(matches: list[Match], name: str, opp: str) -> dict[tuple, int]: + """{(seed, name is config_a): goal difference of `name`} over the matches of `name` vs `opp`.""" + out = {} + for m in matches: + if {m.a, m.b} != {name, opp} or m.a == m.b: + continue + gd = m.goal_difference(name) + if gd is not None: + out[(m.seed, m.a == name)] = gd + return out + + +def paired_rows(matches: list[Match], cand: str, champ: str) -> dict[str, dict]: + """Per opponent: the candidate's and the champion's goal difference over the matches both + played with the same seed and colour (evaluate.py's pairing), and how many pairs there are. + The last row is the candidate against the champion itself.""" + opponents = sorted({n for m in matches for n in (m.a, m.b)} - {cand, champ}) + rows = {} + for o in opponents: + c, ch = _gd_by_pair(matches, cand, o), _gd_by_pair(matches, champ, o) + common = [k for k in c if k in ch] + rows[o] = { + "candidate": sum(c[k] for k in common), + "champion": sum(ch[k] for k in common), + "matches": len(common), + } + h2h = _gd_by_pair(matches, cand, champ) + rows["head-to-head"] = {"candidate": sum(h2h.values()), "champion": -sum(h2h.values()), "matches": len(h2h)} + return rows + + +def side_numbers(name: str, matches: list[Match], views: list[pm.MatchView], rows: dict[str, dict]) -> dict: + mine = [m for m in matches if name in (m.a, m.b)] + n = len(mine) + marks = pm.shot_marks(views, name) + fouls = Counter(f["rule"] for v in views if v.strategy == name for f in v.fouls) + goals_for = [None if m.score_a is None else (m.score_a if name == m.a else m.score_b) for m in mine] + goals_against = [None if m.score_a is None else (m.score_b if name == m.a else m.score_a) for m in mine] + row = rows[name] + return { + "matches": n, + "goals_for_per_match": _mean(goals_for), + "goals_against_per_match": _mean(goals_against), + "shots_for_per_match": len(marks["own"]) / n if n else None, + "shots_against_per_match": len(marks["faced"]) / n if n else None, + "in_possession_attacking_half": row.get("in_possession_attacking_half"), + "in_possession_mean_abs_y": row.get("in_possession_mean_abs_y"), + "out_possession_own_half": row.get("out_possession_own_half"), + "shots_from_attacking_third": row.get("shots_from_attacking_third"), + "fouls_in_defensive_third": row.get("fouls_in_defensive_third"), + "fouls_per_match": {rule: round(c / n, 3) for rule, c in sorted(fouls.items())} if n else {}, + } + + +# --- pictures ----------------------------------------------------------------------------- + + +def plot_positions(views: list[pm.MatchView], cand: str, champ: str, path: Path) -> None: + """2x2: columns candidate and champion, rows with and without the ball, one colour scale.""" + cols = (cand, champ) + states = (True, False) + grids = {(n, s): pm.position_grid(views, n, s) for n in cols for s in states} + nonzero = np.concatenate([g["share"][g["share"] > 0] for g in grids.values() if (g["share"] > 0).any()]) + vmax = float(np.percentile(nonzero, 99)) if nonzero.size else 1.0 + fig, axes = plt.subplots(2, 2, figsize=(7.6, 5.4), squeeze=False) + for i, state in enumerate(states): + for j, name in enumerate(cols): + ax = axes[i][j] + pm._pitch(ax) + grid = grids[(name, state)] + ax.imshow( + grid["share"], + extent=[-pm.L, pm.L, -pm.W, pm.W], + origin="lower", + cmap=pm.SHARE_CMAP, + vmin=0, + vmax=vmax, + interpolation="bilinear", + zorder=1, + ) + if grid["means"]: + mx, my = zip(*grid["means"]) + ax.scatter(mx, my, s=22, color=sr.INK, edgecolors=sr.SURFACE, linewidths=0.8, zorder=3) + ax.set_title( + f"{ROLES[j]}: {name}, {'with' if state else 'without'} the ball", loc="left", fontsize=8.5, color=sr.INK + ) + fig.suptitle( + "Where the outfield robots stand: share of live samples per cell, one scale for all four panels; " + "attacking right, dots are each robot's mean", + x=0.0, + ha="left", + fontsize=9, + color=sr.INK, + ) + sm = plt.cm.ScalarMappable(cmap=pm.SHARE_CMAP, norm=plt.Normalize(0, vmax)) + fig.colorbar( + sm, + ax=axes.ravel().tolist(), + orientation="horizontal", + shrink=0.6, + pad=0.08, + label="share of the samples in a cell", + ) + fig.savefig(path, dpi=100, bbox_inches="tight") + plt.close(fig) + + +# --- entry point ----------------------------------------------------------------------------- + + +def explain(run_dir, candidate: str, champion: str, out_dir, workers: int = 2) -> dict: + """Write positions.png, shots.png, fouls.png and summary.json for `candidate` against + `champion` into `out_dir`, from the replays in `run_dir`. Returns the summary.""" + run_dir, out_dir = Path(run_dir), Path(out_dir) + cand, champ = _short_name(candidate), _short_name(champion) + if cand == champ: + raise ValueError(f"candidate and champion are the same strategy: {cand}") + matches = find_matches(run_dir, {cand, champ}) + if not any(cand in (m.a, m.b) for m in matches) or not any(champ in (m.a, m.b) for m in matches): + raise FileNotFoundError(f"no replays of both {cand} and {champ} in {run_dir}") + views = [v for v in pm.views_for_matches([m.job() for m in matches], workers) if v.strategy in (cand, champ)] + order = [cand, champ] + labels = {cand: f"candidate: {cand}", champ: f"champion: {champ}"} + + out_dir.mkdir(parents=True, exist_ok=True) + plot_positions(views, cand, champ, out_dir / "positions.png") + pm.plot_shots(views, order, labels, out_dir / "shots.png") + pm.plot_fouls(views, order, labels, out_dir / "fouls.png") + + rows = {r["strategy"]: r for r in pm.summary_rows(views, order)} + summary = { + "run_dir": str(run_dir), + "candidate": cand, + "champion": champ, + "replays_read": len(matches), + "sides": {name: side_numbers(name, matches, views, rows) for name in order}, + "paired": paired_rows(matches, cand, champ), + "fouls_rules_shown": pm.top_rules(views), + "notes": ( + "Matches are those in the run dir; fixtures reused from the ledger have no replay here. " + "Positions and fouls exclude the goalkeeper and out-of-bounds (as pitch_maps.py)." + ), + } + (out_dir / "summary.json").write_text(json.dumps(summary, indent=2)) + # The candidate's two worst matches as config_a (the friendly side the intentions log covers), for an agent to + # start from; evaluate.py's failure guard covers this call too. + triage.triage(run_dir, strategies={cand}, matches=2, out=out_dir / "triage", workers=workers) + return summary + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("run_dir", type=Path) + parser.add_argument("candidate") + parser.add_argument("champion") + parser.add_argument("--out", type=Path, required=True) + parser.add_argument("--workers", type=int, default=2) + args = parser.parse_args() + summary = explain(args.run_dir, args.candidate, args.champion, args.out, args.workers) + for name, side in summary["sides"].items(): + print( + f"{name:<28} {side['matches']} matches, goals for/against per match " + f"{side['goals_for_per_match']} / {side['goals_against_per_match']}" + ) + print(f"wrote positions.png, shots.png, fouls.png and summary.json to {args.out}") + + +if __name__ == "__main__": + main() diff --git a/tools/evaluation/ladder.py b/tools/evaluation/ladder.py deleted file mode 100644 index 8f3d6792..00000000 --- a/tools/evaluation/ladder.py +++ /dev/null @@ -1,297 +0,0 @@ -"""ladder.py — one candidate strategy against a reference pool, instead of a full round-robin. - -Run: - pixi run python tools/evaluation/ladder.py CANDIDATE [flags] - - --pool A B ... opponents (short or factory names); default: the top POOL_SIZE of the - newest complete round-robin's standings, the candidate and - round_robin.RETIRED left out - --standings PATH the round-robin to take the pool from (its summary.json or run dir) - --no-reuse play every match, even ones stored in replays/match_cache/ - --max-workers N, -h/--help - -Every pairing is played in mirrored pairs: the same `run_match` settings (which side -config_a starts on, who kicks off) once with the candidate as config_a and once with the -opponent, so each team gets the other's start and side luck cancels. The four settings, in -order, give at most 8 matches per pairing; the first is the round-robin's own (config_a right, -config_a kicks off), so a match a round-robin stored is reused, not played. rsim is -deterministic: playing a setting twice gives the same match, so 8 is all a pairing has until -matches vary by sampled world (docs/roadmap.md item 2a). - -Stopping rule (`decided`): after each mirrored pair, a pairing stops once at least -MIN_MATCHES are played and the candidate's wins minus losses is at least LEAD either way, or -can no longer change sign over the matches left. Draws count for neither. - -Reports win/draw/loss, points (3 a win, 1 a draw) and goal difference per opponent and in -total, and writes replays/ladder_/ladder.json beside the matches' replays. Played -matches are stored in the match cache in the round-robin's format, so a round-robin reuses -them too. Unlike `round_robin.py --reuse`, reused matches are not spot-checked. -""" - -from __future__ import annotations - -import argparse -import dataclasses -import json -import os -import sys -from concurrent.futures import FIRST_COMPLETED, ProcessPoolExecutor, wait -from datetime import datetime, timezone -from pathlib import Path -from typing import Optional - -REPO_ROOT = Path(__file__).resolve().parents[2] -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -from tools.evaluation.match import _short_name, run_match -from tools.evaluation.round_robin import ( - MAX_MATCH_SECONDS, - RETIRED, - _resolve_config_names, - _run_metadata, -) -from utama_core.analysis import restart_outcomes, turnover_breakdown -from utama_core.config.settings import REPLAY_BASE_PATH -from utama_core.replay import match_cache -from utama_core.replay.fingerprint import CodeGraph, match_key - -# (a_is_right, a_kicks_off), in the order a pairing plays them; the first is the round-robin's. -SETTINGS = ((True, True), (True, False), (False, True), (False, False)) -MIN_MATCHES = 4 -LEAD = 3 -POOL_SIZE = 5 - - -def decided(wins: int, losses: int, played: int, max_matches: int) -> bool: - """Whether a pairing can stop: see the module docstring's stopping rule.""" - margin = abs(wins - losses) - return played >= max_matches or (played >= MIN_MATCHES and (margin >= LEAD or margin > max_matches - played)) - - -def standings(results: list[dict]) -> dict[str, dict]: - """Points (3 a win, 1 a draw), goal difference and matches per strategy, over - `summary.json`-shaped `results`.""" - table: dict[str, dict] = {} - for r in results: - for name, gf, ga in ((r["config_a"], r["score_a"], r["score_b"]), (r["config_b"], r["score_b"], r["score_a"])): - row = table.setdefault(name, {"points": 0, "goal_difference": 0, "matches": 0}) - row["points"] += 3 if gf > ga else 1 if gf == ga else 0 - row["goal_difference"] += gf - ga - row["matches"] += 1 - return table - - -def reference_pool(results: list[dict], candidate: str, size: int = POOL_SIZE) -> list[str]: - """The `size` best strategies in `results` other than `candidate` and the retired ones, by - points per match, then goal difference per match, then name.""" - table = standings(results) - ranked = sorted( - (n for n in table if n != candidate and n not in RETIRED), - key=lambda n: ( - -table[n]["points"] / table[n]["matches"], - -table[n]["goal_difference"] / table[n]["matches"], - n, - ), - ) - return ranked[:size] - - -def latest_round_robin(replays: Path) -> Optional[Path]: - """The newest `tournament_*/summary.json` under `replays` that played every pair of its - configs at least once without restart fuzzing: a full round-robin, not a `--pair` run.""" - for path in sorted(replays.glob("tournament_*/summary.json"), reverse=True): - summary = json.loads(path.read_text()) - n = len(summary.get("config_names", [])) - if n >= 3 and summary.get("fuzz_seed") is None and len(summary.get("results", [])) >= n * (n - 1) // 2: - return path - return None - - -def fixtures(candidate: str, opponent: str, setting: tuple[bool, bool]) -> list[tuple[str, str, bool, bool]]: - """One mirrored pair: `setting` with the candidate as config_a, then with the opponent.""" - return [(candidate, opponent, *setting), (opponent, candidate, *setting)] - - -def _suffix(a_is_right: bool, a_kicks_off: bool) -> str: - """The match file tag's suffix (`_RK`, `_Lk`, ...), except none for the - round-robin's own setting so its records and file names match a round-robin's.""" - if a_is_right and a_kicks_off: - return "" - return f"_{'R' if a_is_right else 'L'}{'K' if a_kicks_off else 'k'}" - - -def play(a: str, b: str, a_is_right: bool, a_kicks_off: bool, run_dir: Path) -> dict: - """Play one fixture and analyse its replay into a match-cache record, as `round_robin.py` - stores them.""" - suffix = _suffix(a_is_right, a_kicks_off) - result = run_match( - a, - b, - duration_seconds=MAX_MATCH_SECONDS, - a_is_right=a_is_right, - a_kicks_off=a_kicks_off, - run_dir=run_dir, - match_tag_suffix=suffix, - ) - replay = run_dir / f"{_short_name(a)}_vs_{_short_name(b)}{suffix}.npz" - return { - "result": dataclasses.asdict(result), - "restarts": restart_outcomes.analyse_match(replay), - "losses": turnover_breakdown.analyse_match(str(replay)), - } - - -def outcome(result: dict, candidate: str) -> tuple[int, int]: - """The candidate's goals for and against in a stored `MatchResult` dict.""" - if result["config_a"] == candidate: - return result["score_a"], result["score_b"] - return result["score_b"], result["score_a"] - - -def summarise(matches: list[dict], candidate: str) -> dict: - """W-D-L, points and goal difference over a pairing's (or the ladder's) matches.""" - goals = [outcome(m["result"], candidate) for m in matches] - wins = sum(gf > ga for gf, ga in goals) - draws = sum(gf == ga for gf, ga in goals) - n = len(goals) - return { - "matches": n, - "wins": wins, - "draws": draws, - "losses": n - wins - draws, - "points": 3 * wins + draws, - "points_per_match": round((3 * wins + draws) / n, 2) if n else None, - "goal_difference": sum(gf - ga for gf, ga in goals), - } - - -def main() -> None: - parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - parser.add_argument("candidate") - parser.add_argument("--pool", nargs="+") - parser.add_argument("--standings", type=Path) - parser.add_argument("--no-reuse", action="store_true") - parser.add_argument("--max-workers", type=int, default=max(1, (os.cpu_count() or 1) - 1)) - args = parser.parse_args() - - candidate = _resolve_config_names([args.candidate])[0] - rr_table: dict[str, dict] = {} - if args.pool: - pool = [n for n in _resolve_config_names(args.pool) if n != candidate] - else: - path = args.standings or latest_round_robin(REPLAY_BASE_PATH) - if path is not None and path.is_dir(): - path = path / "summary.json" - if path is None or not path.exists(): - raise SystemExit("No complete round-robin under replays/: pass --pool or --standings") - results = json.loads(path.read_text())["results"] - pool = reference_pool(results, candidate) - rr_table = standings(results) - print(f"Pool: the top {len(pool)} of {path.parent.name}, by points per match") - - run_id = datetime.now(timezone.utc).strftime("ladder_%Y%m%d_%H%M%S") - run_dir = REPLAY_BASE_PATH / run_id - run_dir.mkdir(parents=True, exist_ok=True) - cache = match_cache.MatchCache() - graph = CodeGraph() - - def key(fixture: tuple[str, str, bool, bool]) -> str: - a, b, right, kicks = fixture - return match_key(graph, a, b, duration_seconds=MAX_MATCH_SECONDS, a_is_right=right, a_kicks_off=kicks) - - max_matches = 2 * len(SETTINGS) - matches: dict[str, list[dict]] = {opp: [] for opp in pool} - next_setting = {opp: 0 for opp in pool} - pending = {opp: 0 for opp in pool} - played: dict[tuple, dict] = {} # fixture -> record, for the cache - reused = 0 - keys: dict[tuple, str] = {} - futures: dict = {} - - print(f"Ladder: {_short_name(candidate)} vs {', '.join(_short_name(o) for o in pool)}; run dir replays/{run_id}/\n") - - with ProcessPoolExecutor(max_workers=args.max_workers) as executor: - - def advance(opp: str) -> None: - nonlocal reused - while pending[opp] == 0: - s = summarise(matches[opp], candidate) - if next_setting[opp] == len(SETTINGS) or decided(s["wins"], s["losses"], s["matches"], max_matches): - return - for fixture in fixtures(candidate, opp, SETTINGS[next_setting[opp]]): - keys[fixture] = key(fixture) - record = None if args.no_reuse else cache.get(keys[fixture]) - if record is not None: - reused += 1 - matches[opp].append(record) - else: - futures[executor.submit(play, *fixture, run_dir)] = (opp, fixture) - pending[opp] += 1 - next_setting[opp] += 1 - - for opp in pool: - advance(opp) - while futures: - done, _ = wait(futures, return_when=FIRST_COMPLETED) - for future in done: - opp, fixture = futures.pop(future) - record = future.result() - played[fixture] = record - matches[opp].append(record) - pending[opp] -= 1 - r = record["result"] - print( - f" {_short_name(r['config_a']):<24} {r['score_a']} - {r['score_b']} {_short_name(r['config_b']):<24} " - f"{'right' if fixture[2] else 'left':<5} {'kicks off' if fixture[3] else ''}", - flush=True, - ) - advance(opp) - - graph = CodeGraph() - if all(key(f) == keys[f] for f in played): - for fixture, record in played.items(): - cache.put(keys[fixture], record) - else: - print("\nThe code changed while the ladder played; storing nothing in the match cache") - - rows = {opp: summarise(matches[opp], candidate) for opp in pool} - total = summarise([m for opp in pool for m in matches[opp]], candidate) - print(f"\n{_short_name(candidate)}: {len(played)} played, {reused} reused from the match cache") - print(f" {'opponent':<28} {'rr pts/m':>8} {'W-D-L':>7} {'pts/m':>6} {'GD':>4} {'matches':>8}") - for opp, s in rows.items(): - rr = rr_table.get(opp) - rr_ppm = f"{rr['points'] / rr['matches']:.2f}" if rr else "-" - print( - f" {_short_name(opp):<28} {rr_ppm:>8} {s['wins']:>3}-{s['draws']}-{s['losses']:<1} " - f"{s['points_per_match']:>6.2f} {s['goal_difference']:>+4} {s['matches']:>8}" - ) - print( - f" {'total':<28} {'':>8} {total['wins']:>3}-{total['draws']}-{total['losses']:<1} " - f"{total['points_per_match']:>6.2f} {total['goal_difference']:>+4} {total['matches']:>8}" - ) - - (run_dir / "ladder.json").write_text( - json.dumps( - { - "run_id": run_id, - "run": _run_metadata(), - "candidate": candidate, - "pool": pool, - "rule": {"settings": SETTINGS, "min_matches": MIN_MATCHES, "lead": LEAD}, - "opponents": rows, - "total": total, - "played": len(played), - "reused": reused, - "results": [ - {k: v for k, v in m["result"].items() if k != "stats"} for opp in pool for m in matches[opp] - ], - }, - indent=2, - ) - ) - print(f"\nFull results: replays/{run_id}/ladder.json") - - -if __name__ == "__main__": - main() diff --git a/tools/evaluation/match.py b/tools/evaluation/match.py index 52479ca8..fc43998d 100644 --- a/tools/evaluation/match.py +++ b/tools/evaluation/match.py @@ -1,5 +1,5 @@ """match.py — shared match-construction/running mechanics for the -evaluation drivers (`round_robin.py`, `ladder.py`). +evaluation drivers (`round_robin.py`, `evaluate.py`). Why this exists: `round_robin.py`'s `run_match` and the since-retired `full_match_tournament.py`'s `run_match_cell` were ~80% identical code (build @@ -34,6 +34,7 @@ from utama_core.rsoccer_simulator.src.Simulators.robosim.robosim_wrapper import ( enable_sim_reuse, ) +from utama_core.rsoccer_simulator.src.Utils.sim_noise import SimNoise from utama_core.run import StrategyRunner from utama_core.strategy import kernel_strategy @@ -97,8 +98,7 @@ def _stats_to_dict(stats) -> dict: class MatchResult: """One match's outcome. `a_is_right`/`a_kicks_off` default to the `round_robin.py`'s fixed convention (config_a starts on the right and - kicks off the first half; with halves the teams change ends at half-time) — `ladder.py` passes - both explicitly per match. + kicks off the first half; with halves the teams change ends at half-time). """ config_a: str @@ -130,6 +130,7 @@ def run_match( control_scheme: str = "fpp", fuzz_seed: Optional[int] = None, fuzz_interval_s: tuple[float, float] = (25.0, 45.0), + sim_noise: Optional[SimNoise] = None, ) -> MatchResult: """Play one match between two kernel-strategy factories, until the referee calls full time (two halves of the profile's `half_duration_seconds` of playing time, the clock @@ -170,6 +171,11 @@ def run_match( `utama_core/custom_referee/restart_fuzzer.py`) instead of plain `CustomReferee`, so this match's referee injects extra seeded-random legal restarts during live play. + + `sim_noise`, if set, plays the match with seeded latency, kick speed noise and vision noise + (`utama_core/rsoccer_simulator/src/Utils/sim_noise.py`, `docs/sim_realism.md`). The draws + depend on the seed, team colour and robot id only, so the same `sim_noise` gives every + pairing the same physics. """ enable_sim_reuse() @@ -235,6 +241,7 @@ def run_match( enable_vision_stream=False, referee_initial_command=initial_command, control_scheme=resolved_scheme, + sim_noise=sim_noise, **extra_kwargs, ) diff --git a/tools/evaluation/pools/pool_v1.json b/tools/evaluation/pools/pool_v1.json new file mode 100644 index 00000000..febf8859 --- /dev/null +++ b/tools/evaluation/pools/pool_v1.json @@ -0,0 +1,30 @@ +{ + "version": 1, + "note": "Chosen 2026-10-17 from the 12 strategies of tournament_20261016_201213 for spread of styles, not standings. Change it only on purpose, as pool_v2.json: ratings are comparable within one pool version.", + "champion": "split_shape", + "opponents": [ + "clear_danger", + "decoy_and_overload", + "counter_flow", + "high_press", + "tiki_taka_plus", + "press_and_pass" + ], + "held_out": [ + "overload_press", + "score_aware_counter_flow", + "give_and_go_solo" + ], + "sim_noise": { + "latency_s": [0.03, 0.15], + "kick_speed_offset_sd": 0.2, + "kick_speed_sd": 0.45, + "vision": {"x_stddev": 0.0007, "y_stddev": 0.0007, "th_stddev_deg": 0.3}, + "vanishing": 0.005, + "ball_roll_decel": [0.27, 0.33], + "ball_slide_decel": [2.5, 4.0], + "kick_angle_offset_sd_deg": 1.5, + "kick_angle_sd_deg": 3.0, + "max_accel": [1.5, 3.5] + } +} diff --git a/tools/evaluation/round_robin.py b/tools/evaluation/round_robin.py index 01e3044f..5d613894 100644 --- a/tools/evaluation/round_robin.py +++ b/tools/evaluation/round_robin.py @@ -1,9 +1,10 @@ """round_robin.py (formerly smoke_tournament.py, before that tournament.py) — Round-robin every `build_*_kernel_strategy` config against every other, one full match per pair -(two halves, see `MAX_MATCH_SECONDS` below; `ladder.py` also sweeps side and kickoff, for one candidate). +(two halves, see `MAX_MATCH_SECONDS` below). This is the whole-catalog health and stall check; +a single strategy is judged with `evaluate.py`. Match construction (build strategies, referee, StrategyRunner, kickoff -ceremony, run_dir file layout) lives in `match.py`, shared with `ladder.py`. +ceremony, run_dir file layout) lives in `match.py`. Run: pixi run python tools/evaluation/round_robin.py [config ...] [flags] @@ -18,6 +19,8 @@ --both-sides play each pair twice, sides swapped --control-scheme NAME planner (fpp, trajsample, ...) --fuzz-restarts SEED inject random legal restarts; --fuzz-interval LO HI sets the gap + --sim-noise JSON seeded sim noise for every match: a SimNoise as inline JSON or a file + --match-seconds S match length in sim seconds (default 1200) --max-workers N, --sequential, --no-save, -v/--verbose, -h/--help What this does @@ -104,10 +107,12 @@ _stats_to_dict, ) from tools.evaluation.match import run_match as _lib_run_match -from utama_core.analysis import chances, restart_outcomes, turnover_breakdown +from utama_core.analysis import ratings, restart_outcomes, turnover_breakdown +from utama_core.analysis.strategy_table import strategy_table from utama_core.config.settings import REPLAY_BASE_PATH -from utama_core.replay import match_cache +from utama_core.replay import eval_ledger, match_cache from utama_core.replay.fingerprint import CodeGraph, match_key +from utama_core.rsoccer_simulator.src.Utils.sim_noise import SimNoise # Left out of a round-robin unless named: the bottom ten of the 2026-10-05 full round-robin # (docs/strategies.md). Their code stays, since kept strategies and tests build on some of them. @@ -145,10 +150,11 @@ def run_match( fuzz_seed: Optional[int] = None, fuzz_interval_s: tuple[float, float] = (25.0, 45.0), max_match_seconds: float = MAX_MATCH_SECONDS, + sim_noise: Optional[SimNoise] = None, ) -> MatchResult: """Play one full match (capped at `max_match_seconds`), config_a fixed to the right side and kickoff (this module's historical, un-swept - convention — `ladder.py` sweeps side and kickoff). Thin wrapper over `match.run_match`; see its + convention). Thin wrapper over `match.run_match`; see its docstring for what each parameter does. """ return _lib_run_match( @@ -159,6 +165,7 @@ def run_match( control_scheme=control_scheme, fuzz_seed=fuzz_seed, fuzz_interval_s=fuzz_interval_s, + sim_noise=sim_noise, ) @@ -305,6 +312,19 @@ def main() -> None: fuzz_interval_s = (float(args[idx + 1]), float(args[idx + 2])) args = args[:idx] + args[idx + 3 :] + # `--sim-noise JSON` plays every match with the same seeded latency, kick speed and direction + # noise, ball friction, per-robot motion limits and vision noise: a `SimNoise` as inline JSON or + # a path to a JSON file, e.g. '{"seed": 1, "latency_s": [0.05, 0.1], "kick_speed_sd": 0.3}' + # (field list in `SimNoise`'s docstring). Every pairing gets the same + # draws (they depend on colour and robot id, not strategy), so standings stay paired. + sim_noise: Optional[SimNoise] = None + if "--sim-noise" in args: + idx = args.index("--sim-noise") + spec = args[idx + 1] + sim_noise = SimNoise.from_dict(json.loads(Path(spec).read_text() if Path(spec).is_file() else spec)) + args = args[:idx] + args[idx + 2 :] + sim_noise_dict = sim_noise.to_dict() if sim_noise is not None else None + match_seconds = MAX_MATCH_SECONDS if "--match-seconds" in args: idx = args.index("--match-seconds") @@ -356,10 +376,11 @@ def _keys() -> dict[tuple[str, str], str]: graph, a, b, - duration_seconds=MAX_MATCH_SECONDS, + duration_seconds=match_seconds, control_scheme=control_scheme, fuzz_seed=fuzz_seed, fuzz_interval_s=fuzz_interval_s, + sim_noise=sim_noise_dict, ) for a, b in all_pairs } @@ -463,6 +484,8 @@ def _analyse(result: MatchResult) -> None: } if match_seconds != MAX_MATCH_SECONDS: match_kwargs["max_match_seconds"] = match_seconds + if sim_noise is not None: + match_kwargs["sim_noise"] = sim_noise result = run_match( config_a_name, config_b_name, @@ -482,7 +505,9 @@ def _analyse(result: MatchResult) -> None: # only user-visible reordering is the interleaved progress log. with ProcessPoolExecutor(max_workers=n_workers) as pool: futures = [ - pool.submit(run_match, a, b, run_dir, control_scheme, fuzz_seed, fuzz_interval_s, match_seconds) + pool.submit( + run_match, a, b, run_dir, control_scheme, fuzz_seed, fuzz_interval_s, match_seconds, sim_noise + ) for a, b in pairs ] for future in as_completed(futures): @@ -560,9 +585,10 @@ def _match_tag_of(r: MatchResult) -> str: "run": _run_metadata(), "config_names": sorted(config_names), "control_scheme": control_scheme, - "max_match_seconds": MAX_MATCH_SECONDS, + "max_match_seconds": match_seconds, "fuzz_seed": fuzz_seed, "fuzz_interval_s": list(fuzz_interval_s) if fuzz_seed is not None else None, + "sim_noise": sim_noise_dict, "results": [ { "config_a": r.config_a, @@ -623,6 +649,7 @@ def _match_tag_of(r: MatchResult) -> str: summary["strategies"] = strategy_table(summary["results"], real_losses_by_match, chances_by_match) _print_strategy_table(summary["strategies"]) + summary["ratings"] = _ratings(summary["results"]) _print_loss_kinds(summary["strategies"]) _print_chances(summary["strategies"]) summary["fouls"] = foul_table(summary["results"]) @@ -632,6 +659,7 @@ def _match_tag_of(r: MatchResult) -> str: with open(summary_path, "w") as f: json.dump(summary, f, indent=2) print(f"\nFull results + stats: replays/{run_id}/summary.json") + _append_to_ledger(results, run_id, match_seconds, control_scheme, fuzz_seed, sim_noise_dict) if strict and reuse and mismatches: raise SystemExit(f"--strict: {len(mismatches)} spot-checked match(es) differ from their stored records") @@ -642,6 +670,34 @@ def _match_tag_of(r: MatchResult) -> str: ) +def _append_to_ledger( + results: list[MatchResult], + run_id: str, + match_seconds: float, + control_scheme: str, + fuzz_seed: Optional[int], + sim_noise_dict: Optional[dict], +) -> None: + """Add the run's matches to the evaluation ledger (`replay/eval_ledger.py`), so + `evaluate.py`'s ratings count them. Only matches in `evaluate.py`'s setting: config_a on + the right kicking off, no restart fuzzing.""" + if fuzz_seed is not None: + return + graph = CodeGraph() + env = eval_ledger.environment( + graph, duration_seconds=match_seconds, sim_noise=sim_noise_dict, control_scheme=control_scheme + ) + seed = sim_noise_dict.get("seed") if sim_noise_dict else None + eval_ledger.append( + ( + eval_ledger.entry(env, graph, dataclasses.asdict(r), seed, source=run_id) + for r in results + if r.a_is_right and r.a_kicks_off + ), + Path(REPLAY_BASE_PATH) / "eval" / "ledger.jsonl", # this module's, so tests can redirect it + ) + + def _tag(pair: tuple[str, str]) -> str: """A match's file stem in a run directory (`match.run_match`'s `match_tag`).""" return f"{_short_name(pair[0])}_vs_{_short_name(pair[1])}" @@ -738,91 +794,6 @@ def git(*args: str) -> Optional[str]: } -def strategy_table( - results: list[dict], - real_losses_by_match: Optional[dict[str, dict[str, int]]] = None, - chances_by_match: Optional[dict[str, dict]] = None, -) -> dict[str, dict]: - """Per-strategy totals over `summary.json`-shaped `results`. Match stats are from - config_a's side, so config_b reads the `enemy_*` counterparts. Real ball losses are only - measured for config_a (the side with an intentions log), so they are totalled over the - matches a strategy played as config_a (`matches_as_a`), in total and by kind - (`turnover_breakdown.TURNOVER_KINDS` and `RESTART_KINDS`). `stalled` counts matches that - recorded a stall event or tripped the possession backstop: their result is still in - W-D-L, flagged rather than dropped, since a stall can be the strategy's own fault. - `chances` (`analysis.chances.rates`) is measured for both sides, over the matches in - `chances_by_match`; `pass_progress_m` is the mean metres gained toward goal per - completed pass, `forward_pass_share` the share gaining at least 1 m.""" - table: dict[str, dict] = {} - chance_totals: dict[str, dict[str, float]] = {} - progress: dict[str, list[float]] = {} - for r in results: - stats = r.get("stats") or {} - fouls = stats.get("fouls_by_side") or {} - tag = f"{_short_name(r['config_a'])}_vs_{_short_name(r['config_b'])}" - for name, own, is_a in ((r["config_a"], "friendly", True), (r["config_b"], "enemy", False)): - row = table.setdefault( - name, - { - "matches": 0, - "wins": 0, - "draws": 0, - "losses": 0, - "goals_for": 0, - "goals_against": 0, - "shots": 0, - "completed_passes": 0, - "attacking_third_entries": 0, - "fouls": 0, - "stalled": 0, - "matches_as_a": 0, - "real_losses_as_a": 0, - "real_loss_kinds_as_a": {}, - "robot_overlap_ticks": 0, - "robot_overlap_incidents": 0, - "teammate_overlap_ticks": 0, - "teammate_overlap_incidents": 0, - "near_miss_ticks": 0, - "worst_clearance_m": None, - }, - ) - prefix = "" if is_a else "enemy_" - gf, ga = (r["score_a"], r["score_b"]) if is_a else (r["score_b"], r["score_a"]) - row["matches"] += 1 - row["wins" if gf > ga else "draws" if gf == ga else "losses"] += 1 - row["goals_for"] += gf - row["goals_against"] += ga - row["shots"] += (stats.get("shots") or {}).get(own, 0) - row["completed_passes"] += stats.get(f"{prefix}completed_passes", 0) - row["attacking_third_entries"] += stats.get(f"{prefix}attacking_third_entries", 0) - row["fouls"] += sum((fouls.get(own) or {}).values()) - row["stalled"] += bool(stats.get("stall_events") or r.get("possession_backstop")) - row["robot_overlap_ticks"] += stats.get("robot_overlap_ticks", 0) - row["robot_overlap_incidents"] += stats.get("robot_overlap_incidents", 0) - row["teammate_overlap_ticks"] += (stats.get("teammate_overlap_ticks") or {}).get(own, 0) - row["teammate_overlap_incidents"] += (stats.get("teammate_overlap_incidents") or {}).get(own, 0) - row["near_miss_ticks"] += stats.get("near_miss_ticks", 0) - wc = stats.get("worst_clearance_m") - if wc is not None: - if row["worst_clearance_m"] is None or wc < row["worst_clearance_m"]: - row["worst_clearance_m"] = round(wc, 3) - progress.setdefault(name, []).extend(stats.get(f"{prefix}pass_progress_m") or []) - if chances_by_match and tag in chances_by_match: - chances.add(chance_totals.setdefault(name, {}), chances.side_totals(chances_by_match[tag], own)) - if is_a and real_losses_by_match is not None and tag in real_losses_by_match: - row["matches_as_a"] += 1 - kinds = real_losses_by_match[tag] - row["real_losses_as_a"] += sum(kinds.values()) - for kind, n in kinds.items(): - row["real_loss_kinds_as_a"][kind] = row["real_loss_kinds_as_a"].get(kind, 0) + n - for name, row in table.items(): - gains = progress.get(name, []) - row["pass_progress_m"] = round(sum(gains) / len(gains), 2) if gains else None - row["forward_pass_share"] = round(sum(g >= 1.0 for g in gains) / len(gains), 2) if gains else None - row["chances"] = chances.rates(chance_totals.get(name, {})) - return table - - def stall_incidents(results: list[dict]) -> list[dict]: """Stall events over `summary.json`-shaped `results`, with deterministic duplicates merged: the same kind, onset tick and duration tick in matches that share a strategy @@ -901,6 +872,12 @@ def _print_strategy_table(table: dict[str, dict]) -> None: ) +def _ratings(results: list[dict]) -> dict[str, dict[str, float]]: + """Each config's strength from goal margins, with its SD (`analysis.ratings`).""" + fit = ratings.fit([(r["config_a"], r["config_b"], r["score_a"] - r["score_b"]) for r in results]) + return {n: dict(zip(("strength", "sd"), (round(v, 2) for v in fit.strength(n)))) for n in fit.names} + + def _print_loss_kinds(table: dict[str, dict]) -> None: """Real ball losses per match as config_a, by kind: where each strategy gives the ball away.""" rows = {name: t for name, t in table.items() if t["matches_as_a"]} diff --git a/tools/match_timeline.py b/tools/match_timeline.py new file mode 100644 index 00000000..332d6300 --- /dev/null +++ b/tools/match_timeline.py @@ -0,0 +1,708 @@ +#!/usr/bin/env python3 +# ruff: noqa: E402 +"""One match window as swimlanes: referee command, each robot's tactic, distance to the ball, events. + + pixi run python tools/match_timeline.py replays/tournament_/_vs_.npz 100 160 --side friendly + +Stacked panels on one time axis (Gantt-style, no dual axes): +1. the referee command band: live play, STOP, kickoff, free kick, penalty, placement, HALT (goal and + timeout stoppages count as STOP), labelled; +2. one lane per robot of the chosen side: a span per tactic the robot was in (the Tactic class named in + the `.intentions.jsonl` row beside the replay), a thin black tick at each switch to another tactic, + and a dark strip along the bottom edge where the replay says the robot had the ball; +3. the distance from each of the side's robots (up to 6) to the ball, one colour per robot id; +4. events along the top: goals, fouls (rule names) and shots, ours in blue and theirs in orange. + +The intentions log is written for the friendly side only (config_a), so `--side enemy` leaves the +lanes empty; the command band, possession strips and distances still work. + +Prints, for the side: tactic switches per minute and the longest single tactic span, per robot. +`--out` defaults to a `.timeline__-.png` beside the replay. + +Reuses `tools/replay_trace.py` for the event list (kicks, possession, referee changes, fouls, goals) and +the status messages, and `analysis/turnover_breakdown.analyse_match` for shots. +""" + +from __future__ import annotations + +import argparse +import json +import math +import os +import sys +from collections import Counter +from pathlib import Path +from typing import NamedTuple, Optional + +os.environ.setdefault("PYGAME_HIDE_SUPPORT_PROMPT", "1") +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +from matplotlib.patches import Patch # noqa: E402 + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from tools import replay_trace # noqa: E402 +from utama_core.analysis import turnover_breakdown # noqa: E402 +from utama_core.analysis.findings import Finding, write_jsonl # noqa: E402 +from utama_core.entities.referee.referee_command import RefereeCommand # noqa: E402 + +# A flip-back is a tactic held for less than FLIP_S between two spans of the same other tactic (A, B, A): +# the robot reverted before B could do anything. A robot with at least FLIP_BACKS_PER_MIN of them per minute +# of live play gets a `tactic_thrash` finding. Raw switch rate is not the signal: most strategies switch +# 10-25 times per live minute by design (attack/defence on possession). Measured on tournament_20261017_151052 +# (66 matches, friendly robots, median flip-backs under 1 s per live min): give_and_go_solo 0.0, decoy_and_overload +# 0.1, high_press 0.3, overload_press 0.5; counter_flow, clear_danger, clear_press_plus, split_shape, +# press_trigger_flow, score_aware_counter_flow 4.6-5.6; press_and_pass 13.4. 2 per min (one every 30 s of live +# play) sits between the steady and the flipping strategies. +FLIP_S = 1.0 +FLIP_BACKS_PER_MIN = 2.0 +# Under this much live play a rate is noise (one switch in 10 s reads as 6 per minute): no thrash finding. +MIN_LIVE_S = 30.0 + +# Styling copied from tools/signal_report.py so the figures read as one set. +INK, INK_2, INK_3, SURFACE, GRID = "#0b0b0b", "#52514e", "#8a8984", "#fcfcfb", "#e4e3df" +FOR, AGAINST = "#2a78d6", "#eb6834" # categorical slots 1 and 2 +SLOTS = ["#2a78d6", "#eb6834", "#1baf7a", "#eda100", "#e87ba4", "#008300", "#6250d6"] +OTHER = "#b9b8b3" # tactics beyond the seven slots fold into this grey +UNASSIGNED_EDGE = "#c9c8c3" + +plt.rcParams.update( + { + "figure.facecolor": SURFACE, + "axes.facecolor": SURFACE, + "axes.edgecolor": INK_3, + "axes.labelcolor": INK_2, + "axes.titlesize": 10, + "axes.titlecolor": INK, + "axes.spines.top": False, + "axes.spines.right": False, + "xtick.color": INK_2, + "ytick.color": INK, + "font.size": 9, + } +) + +# Referee command groups, in the order the legend lists them. Pastel fills; text is drawn on top. +REF_GROUP_FILL = { + "live play": GRID, + "STOP": INK_3, + "HALT": INK_2, + "kickoff": "#c9d7ee", + "free kick": "#f6d7c3", + "penalty": "#eacbdc", + "placement": "#efe6b8", + "other": "#fcfcfb", + "no referee": "#fcfcfb", +} +REF_GROUP_TEXT = {"STOP": SURFACE, "HALT": SURFACE} + +NO_TACTIC = None # a Span label for "no tactic assigned" +BALL = "ball" # a possession Span label + + +class Span(NamedTuple): + t0: float + t1: float + label: Optional[str] + + +# --- intentions -> per-robot tactic spans ------------------------------------------------------- + + +def load_intentions(npz_path: Path) -> list[dict]: + """The `intention` rows of the `.intentions.jsonl` beside the replay (empty if there is none).""" + path = npz_path.with_name(npz_path.name[: -len(".npz")] + ".intentions.jsonl") + if not path.exists(): + return [] + rows = [] + with open(path) as f: + for line in f: + row = json.loads(line) + if row.get("event") == "intention": + rows.append(row) + return sorted(rows, key=lambda r: r["sim_time"]) + + +def goalkeeper_ids(npz_path: Path) -> set[int]: + """Robot ids the strategy marked as keeper (`highlights.goalkeeper` trace rows). The keeper has no slot.""" + path = npz_path.with_name(npz_path.name[: -len(".npz")] + ".intentions.jsonl") + if not path.exists(): + return set() + keepers: set[int] = set() + with open(path) as f: + for line in f: + if '"highlights.goalkeeper"' in line: + row = json.loads(line) + keepers |= {int(k) for k in (row.get("value") or {})} + return keepers + + +def tactic_changes(rows: list[dict], robot_ids) -> dict[int, list[tuple[float, Optional[str]]]]: + """For each robot: the (sim_time, tactic) points where its tactic changes, in time order. + + A robot's tactic is the slot (`tactic_id`) whose latest row lists it. The name is the Tactic class in + the row's `note`; rows whose note is not a class name (deadline releases, "released ...") keep the name + the slot had. A "referee reset" row clears every slot. + """ + members: dict[str, frozenset] = {} # slot -> robots, insertion order = last update last + names: dict[str, str] = {} # slot -> Tactic class name + changes: dict[int, list[tuple[float, Optional[str]]]] = {int(r): [] for r in robot_ids} + for row in rows: + t = float(row["sim_time"]) + if row["tactic_id"] == "referee reset": + members.clear() + else: + slot = row["tactic_id"] + note = row.get("note") or "" + if note.isidentifier(): + names[slot] = note + names.setdefault(slot, slot) + members.pop(slot, None) + members[slot] = frozenset(int(r) for r in row["robot_ids"]) + for rid in changes: + label = None + for slot in reversed(list(members)): + if rid in members[slot]: + label = names[slot] + break + points = changes[rid] + if not points and label is None: + continue + if points and points[-1][1] == label: + continue + if points and points[-1][0] == t: + points[-1] = (t, label) # several rows at one tick: the last one wins + else: + points.append((t, label)) + return changes + + +def spans_from_changes(changes: list[tuple[float, Optional[str]]], t0: float, t1: float) -> list[Span]: + """Spans tiling [t0, t1] from change points; before the first change a robot has no tactic.""" + points = [(-math.inf, NO_TACTIC)] + list(changes) + out: list[Span] = [] + for i, (start, label) in enumerate(points): + end = points[i + 1][0] if i + 1 < len(points) else math.inf + a, b = max(start, t0), min(end, t1) + if b <= a: + continue + if out and out[-1].label == label: + out[-1] = Span(out[-1].t0, b, label) + else: + out.append(Span(a, b, label)) + return out + + +def runs(flag: np.ndarray, t: np.ndarray, label: str) -> list[Span]: + """Contiguous True runs of `flag` over the sample times `t` (each run ends at the next sample).""" + out: list[Span] = [] + start = None + for i, on in enumerate(flag): + if on and start is None: + start = t[i] + elif not on and start is not None: + out.append(Span(float(start), float(t[i]), label)) + start = None + if start is not None and float(t[-1]) > float(start): + out.append(Span(float(start), float(t[-1]), label)) + return out + + +def switch_times(spans: list[Span]) -> list[float]: + """Start times of spans whose tactic differs from the previous assigned tactic (unassigned gaps skipped).""" + out = [] + prev = None + for s in spans: + if s.label is None: + continue + if prev is not None and s.label != prev: + out.append(s.t0) + prev = s.label + return out + + +def lane_summary(spans: list[Span], t0: float, t1: float) -> dict: + assigned = [s for s in spans if s.label is not None] + longest = max(assigned, key=lambda s: s.t1 - s.t0, default=None) + minutes = max(t1 - t0, 1e-9) / 60.0 + switches = len(switch_times(spans)) + return { + "switches": switches, + "per_min": switches / minutes, + "longest": None if longest is None else (longest.label, longest.t1 - longest.t0), + } + + +def tactic_colours(lanes: list[list[Span]]) -> dict[str, str]: + """Slot colours for the window's tactics, most time first; tactics beyond the seven slots are OTHER.""" + total: Counter = Counter() + for spans in lanes: + for s in spans: + if s.label is not None: + total[s.label] += s.t1 - s.t0 + ranked = [name for name, _ in sorted(total.items(), key=lambda kv: (-kv[1], kv[0]))] + return {name: SLOTS[i] for i, name in enumerate(ranked[: len(SLOTS)])} + + +# --- referee command band ----------------------------------------------------------------------- + + +def ref_group(command: Optional[str]) -> str: + """The legend group of a referee command name ("-" or None when the replay has no referee).""" + if command in (None, "-"): + return "no referee" + if command in ("NORMAL_START", "FORCE_START"): + return "live play" + if command == "HALT": + return "HALT" + if command == "STOP" or command.startswith(("GOAL_", "TIMEOUT_")): + return "STOP" + if command.startswith("PREPARE_KICKOFF"): + return "kickoff" + if command.startswith(("DIRECT_FREE", "INDIRECT_FREE")): + return "free kick" + if command.startswith("PREPARE_PENALTY"): + return "penalty" + if command.startswith("BALL_PLACEMENT"): + return "placement" + return "other" + + +def group_spans(groups: list[str], t: np.ndarray, t0: float, t1: float) -> list[Span]: + """Runs of one referee group over the sample times `t`, clipped to [t0, t1].""" + out: list[Span] = [] + for i, g in enumerate(groups): + a = float(t[i]) + b = float(t[i + 1]) if i + 1 < len(t) else t1 # the last sample lasts to the window end + a, b = max(a, t0), min(b, t1) + if b <= a: + continue + if out and out[-1].label == g: + out[-1] = Span(out[-1].t0, b, g) + else: + out.append(Span(a, b, g)) + return out + + +# --- drawing ------------------------------------------------------------------------------------ + +_LANE_H = 0.8 +_LABEL_CHAR_IN = 0.062 # width of one 7 pt character, inches (approximate) +_AXIS_IN = 10.6 # width of the plotting area, inches + + +def _fits(text: str, span_s: float, seconds_per_inch: float) -> bool: + return len(text) * _LABEL_CHAR_IN * seconds_per_inch <= span_s + + +def _ink_on(fill: str) -> str: + r, g, b = (int(fill[i : i + 2], 16) / 255 for i in (1, 3, 5)) + lum = 0.2126 * r + 0.7152 * g + 0.0722 * b + return INK if lum > 0.55 else SURFACE + + +LEGEND_COLS = 6 + + +def _legend_handles(colours: dict[str, str], lanes: list[list[Span]], ref_spans: list[Span], events: list[dict]): + """Every key the figure uses, once: tactic colours, the other/none marks, possession, referee, events.""" + handles = [Patch(facecolor=fill, edgecolor=fill, label=name) for name, fill in colours.items()] + all_labels = {s.label for spans in lanes for s in spans} + if any(lab is not None and lab not in colours for lab in all_labels): + handles.append(Patch(facecolor=OTHER, label="other tactic")) + if None in all_labels: + handles.append(Patch(facecolor="none", edgecolor=UNASSIGNED_EDGE, hatch="////", label="no tactic")) + handles.append(Patch(facecolor=INK, label="had the ball")) + handles.append(plt.Line2D([], [], color=INK, lw=1.0, label="switched tactic")) + handles += [Patch(facecolor=REF_GROUP_FILL[g], edgecolor=INK_3, lw=0.4, label=g) for g in REF_GROUP_FILL] + kinds = {e["kind"] for e in events} + if "GOAL" in kinds: + handles.append( + plt.Line2D([], [], marker="D", ls="", color=FOR, mec=INK, label="goal (blue ours, orange theirs)") + ) + if "FOUL" in kinds: + handles.append(plt.Line2D([], [], marker="v", ls="", color=FOR, mec=INK, label="foul (ours)")) + handles.append(plt.Line2D([], [], marker="v", ls="", color=AGAINST, mec=INK, label="foul (theirs)")) + if "SHOT" in kinds: + handles.append(plt.Line2D([], [], marker="o", ls="", color=FOR, mec=INK, label="shot (ours / theirs)")) + return handles + + +def render_timeline( + out: Path, + *, + t0: float, + t1: float, + side: str, + robot_ids: list[int], + lanes: list[list[Span]], + possession: list[list[Span]], + ref_spans: list[Span], + t: np.ndarray, + dist: np.ndarray, + events: list[dict], + keepers: set[int] = frozenset(), + title: str = "", +) -> Path: + """Draw the swimlanes to `out` (PNG). `dist` is (len(t), len(robot_ids)) metres to the ball.""" + k = len(robot_ids) + colours = tactic_colours(lanes) + sec_per_in = (t1 - t0) / _AXIS_IN + height_ratios = [1.3, 1.0] + [1.0] * k + [3.2] + handles = _legend_handles(colours, lanes, ref_spans, events) + legend_rows = math.ceil(len(handles) / LEGEND_COLS) + legend_h = 0.2 * legend_rows + 0.15 # inches, under the panels + fig_h = 0.9 + 0.36 * k + 0.5 + 1.2 + legend_h + fig = plt.figure(figsize=(12, fig_h)) + gs = fig.add_gridspec(len(height_ratios), 1, height_ratios=height_ratios, hspace=0.12) + ax_ev = fig.add_subplot(gs[0]) + ax_ref = fig.add_subplot(gs[1], sharex=ax_ev) + lane_axes = [fig.add_subplot(gs[2 + i], sharex=ax_ev) for i in range(k)] + ax_d = fig.add_subplot(gs[2 + k], sharex=ax_ev) + + # 1. events along the top + ax_ev.set_ylim(0, 1) + ax_ev.set_yticks([]) + ax_ev.spines["left"].set_visible(False) + ax_ev.spines["bottom"].set_visible(False) + ax_ev.tick_params(axis="x", bottom=False, labelbottom=False) + alt = 0 + for ev in events: + colour = FOR if ev.get("side") == "ours" else AGAINST + if ev["kind"] == "GOAL": + for ax in lane_axes + [ax_d]: + ax.axvline(ev["t"], color=INK, lw=0.8, ls=(0, (3, 2)), zorder=0) + ax_ev.plot([ev["t"]], [0.5], marker="D", ms=5, color=colour, mec=INK, mew=0.6, zorder=3) + ax_ev.text(ev["t"], 0.98, ev["text"], fontsize=7, ha="center", va="top", color=INK, fontweight="bold") + elif ev["kind"] == "FOUL": + ax_ev.plot([ev["t"]], [0.5], marker="v", ms=6, color=colour, mec=INK, mew=0.4, zorder=3) + y = 0.3 if alt % 2 else 0.02 + alt += 1 + ax_ev.text( + ev["t"] + 0.2 * sec_per_in, y, ev["rule"].replace("_", " "), fontsize=6.5, color=INK_2, va="bottom" + ) + elif ev["kind"] == "SHOT": + ax_ev.plot([ev["t"]], [0.5], marker="o", ms=4, color=colour, mec=INK, mew=0.4, zorder=3) + ax_ev.set_ylabel("events", rotation=0, ha="right", va="center", color=INK_2, fontsize=8) + if title: + ax_ev.set_title(title, loc="left", fontsize=10) + + # 2. referee command band + ax_ref.set_ylim(0, 1) + ax_ref.set_yticks([]) + ax_ref.spines["left"].set_visible(False) + ax_ref.tick_params(axis="x", bottom=False, labelbottom=False) + for s in ref_spans: + fill = REF_GROUP_FILL.get(s.label, OTHER) + ax_ref.broken_barh([(s.t0, s.t1 - s.t0)], (0.08, 0.84), facecolors=fill, edgecolors=SURFACE, linewidth=0.5) + if _fits(s.label, s.t1 - s.t0, sec_per_in): + ax_ref.text( + (s.t0 + s.t1) / 2, + 0.5, + s.label, + fontsize=7, + ha="center", + va="center", + color=REF_GROUP_TEXT.get(s.label, INK), + ) + ax_ref.set_ylabel("referee", rotation=0, ha="right", va="center", color=INK_2, fontsize=8) + + # 3. one lane per robot + for i, rid in enumerate(robot_ids): + ax = lane_axes[i] + y = 0.0 + ax.set_ylim(-0.6, 0.6) + ax.set_yticks([]) + ax.spines["left"].set_visible(False) + ax.tick_params(axis="x", bottom=False, labelbottom=False) + for s in lanes[i]: + if s.label is None: + ax.broken_barh( + [(s.t0, s.t1 - s.t0)], + (y - _LANE_H / 2, _LANE_H), + facecolors="none", + edgecolors=UNASSIGNED_EDGE, + hatch="////", + linewidth=0, + ) + continue + fill = colours.get(s.label, OTHER) + ax.broken_barh( + [(s.t0, s.t1 - s.t0)], + (y - _LANE_H / 2, _LANE_H), + facecolors=fill, + edgecolors=SURFACE, + linewidth=0.5, + ) + if _fits(s.label, s.t1 - s.t0, sec_per_in): + ax.text( + (s.t0 + s.t1) / 2, + y, + s.label, + fontsize=7, + ha="center", + va="center", + color=_ink_on(fill), + clip_on=True, + ) + for tt in switch_times(lanes[i]): + ax.plot([tt, tt], [y - _LANE_H / 2, y + _LANE_H / 2], color=INK, lw=1.0, zorder=3) + for p in possession[i]: + ax.broken_barh([(p.t0, p.t1 - p.t0)], (y - _LANE_H / 2, 0.14), facecolors=INK, linewidth=0, zorder=4) + ax.set_ylabel( + f"R{rid}" + (" keeper" if rid in keepers else ""), + rotation=0, + ha="right", + va="center", + color=INK, + fontsize=8, + ) + + # 4. distance to the ball + ax_d.set_ylabel("dist to ball (m)", rotation=0, ha="right", va="center", color=INK_2, fontsize=8) + ymax = float(np.nanmax(dist)) if dist.size and np.isfinite(dist).any() else 1.0 + ax_d.set_ylim(0, max(1.0, min(ymax, 12.0)) * 1.05) + ax_d.set_xlim(t0, t1) + ax_d.grid(axis="y", color=GRID, lw=0.6) + end_labels = [] + for i, rid in enumerate(robot_ids): + colour = SLOTS[i % len(SLOTS)] + ax_d.plot(t, dist[:, i], color=colour, lw=1.1) + finite = np.where(np.isfinite(dist[:, i]))[0] + if finite.size: + end_labels.append((float(dist[finite[-1], i]), rid, colour)) + # Direct labels at the right edge, nudged apart so two robots never share a line. + last = -math.inf + gap = 0.12 * ax_d.get_ylim()[1] # enough for one 7 pt line of text at this panel height + for val, rid, colour in sorted(end_labels): + y = max(val, last + gap) + last = y + ax_d.text(t1, y, f" R{rid}", fontsize=7, color=colour, va="center", ha="left", clip_on=False) + ax_d.set_xlabel("time in window (s)", color=INK_2) + ax_d.set_xlim(t0, t1) + + # Legend, once, under the figure. + fig.legend( + handles=handles, loc="lower center", ncol=LEGEND_COLS, fontsize=7, frameon=False, bbox_to_anchor=(0.5, 0.0) + ) + fig.subplots_adjust(left=0.1, right=0.93, top=1 - 0.45 / fig_h, bottom=(legend_h + 0.2) / fig_h) + out.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(out, dpi=130, facecolor=SURFACE) + plt.close(fig) + return out + + +# --- glue --------------------------------------------------------------------------------------- + + +def _summary_lines( + robot_ids: list[int], lanes: list[list[Span]], t0: float, t1: float, keepers: set[int] = frozenset() +) -> list[str]: + lines = [f"tactic switches per minute, window {t0:.1f}-{t1:.1f} s (spans are clipped to the window):"] + lines.append(f" {'robot':>7} {'switches':>8} {'per min':>8} longest tactic span") + for rid, spans in zip(robot_ids, lanes): + s = lane_summary(spans, t0, t1) + longest = "-" if s["longest"] is None else f"{s['longest'][0]} {s['longest'][1]:.1f} s" + name = f"R{rid}" + (" keeper" if rid in keepers else "") + lines.append(f" {name:>7} {s['switches']:>8} {s['per_min']:>8.2f} {longest}") + return lines + + +class Window(NamedTuple): + """One window's computed data, before any drawing.""" + + t0: float + t1: float + robot_ids: list[int] + lanes: list[list[Span]] + possession: list[list[Span]] + ref_spans: list[Span] + tw: np.ndarray # sample times in the window, on the replay's clock from its first frame + dist: np.ndarray # (len(tw), robots) metres to the ball + keepers: set[int] + live_s: float # seconds of live play (NORMAL_START / FORCE_START) in the window + notes: list[str] + z: dict # the replay's arrays, as loaded + + +def load_window(npz_path: Path, t0: Optional[float], t1: Optional[float], side: str) -> Window: + """The window's lanes, possession, referee spans and distances; no drawing and no shot analysis.""" + z = dict(np.load(npz_path)) + ts = z["ts"] + t = ts - ts[0] + t_end = float(t[-1]) + t0 = 0.0 if t0 is None else max(0.0, t0) + t1 = t_end if t1 is None else min(t_end, t1) + if t1 <= t0: + raise SystemExit(f"empty window: t0={t0} t1={t1} (the replay runs 0-{t_end:.1f} s)") + win = (t >= t0) & (t <= t1) + tw = t[win] + + notes: list[str] = [] + robot_ids = [int(r) for r in z[f"{side}_ids"]] + rows = load_intentions(npz_path) if side == "friendly" else [] + if side == "enemy": + notes.append("note: the intentions log is written for the friendly side only; enemy lanes are empty") + elif not rows: + notes.append(f"note: no {npz_path.name[:-4]}.intentions.jsonl beside the replay; lanes are empty") + changes = tactic_changes(rows, robot_ids) + lanes = [spans_from_changes(changes[rid], t0, t1) for rid in robot_ids] + keepers = goalkeeper_ids(npz_path) if side == "friendly" else set() + has_ball = z[f"{side}_has_ball"][win] + possession = [runs(has_ball[:, i], tw, BALL) for i in range(len(robot_ids))] + + cmd_names = [ + RefereeCommand(int(c)).name if h else "-" for c, h in zip(z["referee_command"][win], z["has_referee"][win]) + ] + ref_spans = group_spans([ref_group(c) for c in cmd_names], tw, t0, t1) + live_s = sum(s.t1 - s.t0 for s in ref_spans if s.label == "live play") + + pos = z[f"{side}_p"][win] + ball = z["ball_p"][win] + dist = np.hypot(pos[:, :, 0] - ball[:, None, 0], pos[:, :, 1] - ball[:, None, 1]) + return Window(t0, t1, robot_ids, lanes, possession, ref_spans, tw, dist, keepers, live_s, notes, z) + + +def flip_backs(spans: list[Span], max_s: float = FLIP_S) -> list[float]: + """Start times of the tactic spans shorter than `max_s` that sit between two spans of one other tactic + (A, B, A; unassigned gaps skipped).""" + assigned = [s for s in spans if s.label is not None] + return [ + b.t0 + for a, b, c in zip(assigned, assigned[1:], assigned[2:]) + if a.label == c.label != b.label and b.t1 - b.t0 < max_s + ] + + +def thrash_findings( + w: Window, + *, + replay: Optional[str], + side: str, + image: Optional[str] = None, + threshold: float = FLIP_BACKS_PER_MIN, +) -> list[Finding]: + """One `tactic_thrash` finding per robot whose flip-backs (`flip_backs`) reach `threshold` per minute of + live play. + + Flip-backs are counted over the whole window; the rate divides by the live play in it, so a stoppage does + not dilute a robot that thrashes in open play. No finding when the window has less than `MIN_LIVE_S` of + live play: a rate over a few seconds is noise.""" + if w.live_s < MIN_LIVE_S: + return [] + minutes = w.live_s / 60.0 + out = [] + for rid, spans in zip(w.robot_ids, w.lanes): + flips = flip_backs(spans) + rate = len(flips) / minutes + if rate >= threshold: + out.append( + Finding( + tool="match_timeline", + kind="tactic_thrash", + replay=replay, + side=side, + t0=w.t0, + t1=w.t1, + robots=(rid,), + value=rate, + threshold=threshold, + unit="flip-backs per live min", + message=( + f"R{rid} took a tactic for under {FLIP_S:g} s and reverted {len(flips)} times in " + f"{minutes:.1f} min of live play ({rate:.2f} per min, threshold {threshold:g}); " + f"first at t={flips[0]:.1f} s." + ), + image=image, + ) + ) + return out + + +def timeline_findings(replay_path: Path, t0: Optional[float], t1: Optional[float], side: str) -> list[Finding]: + """The match_timeline findings for one window, computed without drawing (`image` is None).""" + replay_path = Path(replay_path) + w = load_window(replay_path, t0, t1, side) + return thrash_findings(w, replay=str(replay_path), side=side) + + +def build(npz_path: Path, t0: Optional[float], t1: Optional[float], side: str, out: Optional[Path]): + """Compute the window's data, draw it, and return (png path, summary lines, findings with `image` set).""" + w = load_window(npz_path, t0, t1, side) + z, ts, t0, t1 = w.z, w.z["ts"], w.t0, w.t1 + statuses = replay_trace._statuses(npz_path, len(ts)) + fouls = replay_trace._fouls(npz_path) + events = [] + my_yellow = bool(z["my_team_is_yellow"]) + for ev in replay_trace.events(z, statuses, fouls, t0, t1): + if ev["kind"] == "FOUL": + events.append({"kind": "FOUL", "t": ev["t"], "rule": ev["rule"], "side": ev["side"]}) + elif ev["kind"] == "REF" and ev["command"].startswith("GOAL_"): + scored_yellow = ev["command"] == "GOAL_YELLOW" + ours = scored_yellow == my_yellow + events.append( + { + "kind": "GOAL", + "t": ev["t"], + "side": "ours" if ours else "theirs", + "text": "GOAL ours" if ours else "GOAL theirs", + } + ) + for shot in turnover_breakdown.analyse_match(str(npz_path))["chances"]["shots"]: + st = float(shot["t"]) - float(ts[0]) + if t0 <= st <= t1: + events.append({"kind": "SHOT", "t": st, "side": "ours" if shot["side"] == "friendly" else "theirs"}) + events.sort(key=lambda e: e["t"]) + + if out is None: + out = npz_path.with_name(f"{npz_path.name[:-4]}.timeline_{side}_{int(t0)}-{int(t1)}.png") + title = f"{npz_path.name[:-4]} | {side} | {t0:.0f}-{t1:.0f} s" + png = render_timeline( + out, + t0=t0, + t1=t1, + side=side, + robot_ids=w.robot_ids, + lanes=w.lanes, + possession=w.possession, + ref_spans=w.ref_spans, + t=w.tw, + dist=w.dist, + events=events, + keepers=w.keepers, + title=title, + ) + findings = thrash_findings(w, replay=str(npz_path), side=side, image=str(png)) + return png, w.notes + _summary_lines(w.robot_ids, w.lanes, t0, t1, w.keepers), findings + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("replay", type=Path, help="a match's .npz (its .intentions.jsonl must sit beside it)") + parser.add_argument("t0", type=float, nargs="?", default=None, help="window start, seconds (default 0)") + parser.add_argument("t1", type=float, nargs="?", default=None, help="window end, seconds (default: the end)") + parser.add_argument("--t0", dest="t0_opt", type=float, default=None, help="same as the t0 argument") + parser.add_argument("--t1", dest="t1_opt", type=float, default=None, help="same as the t1 argument") + parser.add_argument("--side", choices=["friendly", "enemy"], default="friendly") + parser.add_argument("--out", type=Path, default=None, help="PNG path (default: beside the replay)") + parser.add_argument( + "--findings", metavar="PATH", help="also write the flagged problems as JSON lines (- for stdout)" + ) + args = parser.parse_args() + t0 = args.t0_opt if args.t0_opt is not None else args.t0 + t1 = args.t1_opt if args.t1_opt is not None else args.t1 + png, summary, findings = build(args.replay, t0, t1, args.side, args.out) + human = sys.stderr if args.findings == "-" else sys.stdout # keep stdout pure JSON for "-" + print(f"wrote {png}", file=human) + print("\n".join(summary), file=human) + if args.findings is not None: + write_jsonl(findings, args.findings) + + +if __name__ == "__main__": + main() diff --git a/tools/moments.py b/tools/moments.py new file mode 100644 index 00000000..408920e7 --- /dev/null +++ b/tools/moments.py @@ -0,0 +1,1106 @@ +"""Regression moments: one field state that reproduced a bug, replayed for a few seconds against +every strategy it concerns, with a check of what our robots must do instead. The workflow (find, +reproduce, fix the cause with a unit test, keep the moment) is in docs/STRATEGY_DEVELOPMENT.md, +"Debugging a foul or failure". + +A moment is a JSON file in `utama_core/tests/moments/` (`test_moments.py` plays each one): + + {"name": ..., "note": what went wrong and why it must not happen again, + "source": {"replay": ..., "t": ...} or null for a hand-made one, + "strategy": "all" or one strategy, "opponent": the other side (default split_shape), + "seconds": how long to play (default 3), + "checks": names from `CHECKS` below, or referee rule names our side must not break + (`custom_referee/rules/`, "*" for any), + "requires": names from `REQUIRES` below: conditions that must hold during the play, so a + moment whose situation never arose fails ("did not arise: ...") instead of passing, + "known_failure": null, or why it still fails (the test is then a strict xfail), + "designated_position": a ball placement's target [x, y], else null, + "sim_noise": the match's seeded noise (`sim_noise.py`), or null for none, + "scenario": the field state: ball, robots and referee command (`replay/scenario.py`)} + +Robots start from rest (`replay/scenario.py`, Known +limitations), so a moment pins what the field and the referee command alone cause, which is +most referee failures. Start a moment where the failure is about to happen, not seconds before: +from rest a robot often takes another path. A check is a function of the played ticks, stated +as the behaviour we want, so it does not wait on the referee's persistence before calling a foul. + +"all" plays every strategy in the round-robin catalog, except that during a restart the +referee override moves every outfield robot whatever the strategy, so then one strategy per +distinct override (its `referee_overrides` and goalkeeper tactic) is enough. + +Requires vs checks: a check says what must not happen. A requirement says the situation the moment is about arose: a restart lasted, +a robot came near the ball. Without them a moment passes when the opponent +does something else, the ball goes elsewhere, or our robot never gets there. + +Hand-made moments: `new` writes a template (both teams in a fixed formation, ours on the right, +the pool's noise with seed 1, source null) to edit by hand; `save` and `load` refuse an +invalid one, naming each problem (ids, field lines, check and strategy names). + +List our side's fouls in a round-robin run, as starting points, each with the referee command +active then and whether that restart is ours or theirs (`--skip-own-restart` leaves out ours): + pixi run python tools/moments.py fouls replays/tournament_ [--rule keep_out] [--skip-own-restart] +(`--by strategy,rule` counts them per combination instead; `out_of_bounds`, the ball leaving the +field, is a stoppage and is left out unless `--include-stoppages`.) +Capture one from a replay (its noise and designated position come with it; `--out` writes +elsewhere than utama_core/tests/moments/): + pixi run python tools/moments.py capture replays/tournament_/_vs_.npz --t 254.65 \\ + --name free_kick_defender_drives_in --check keeps_clear --strategy --opponent \\ + --note "..." [--out PATH] +Write a hand-made one, then edit its positions in the JSON: + pixi run python tools/moments.py new --command NORMAL_START [--ball X Y] \\ + [--strategy NAME] [--seconds N] --check --note "..." +Play one (`--seed N` replays its noise with another seed, the JSON untouched): + pixi run python tools/moments.py play utama_core/tests/moments/.json [--strategy NAME] [--seed N] +Play it under several seeds as one table, compare with the code at a git ref (a worktree under +`AGAINST_DIR`, to remove with `git worktree remove --force`), look at its events (referee changes, fouls, +keep-out entries, kicks, closest approaches), or try a scratch strategy file's factory: + pixi run python tools/moments.py play --seeds 0-4 [--against HEAD~3] [--events] + pixi run python tools/moments.py play --strategy path/to/scratch.py:build_my_kernel_strategy +""" + +from __future__ import annotations + +import os + +os.environ.setdefault("PYGAME_HIDE_SUPPORT_PROMPT", "1") # before any import: hides pygame's banner +import argparse +import dataclasses +import importlib.util +import json +import math +import os +import re +import shutil +import subprocess +import sys +import tempfile +from collections import Counter +from pathlib import Path +from typing import Callable, Optional + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from tools.evaluation.match import ( # noqa: E402 + N_OUTFIELD, + OUTFIELD_ROBOT_IDS, + TICKS_PER_SECOND, +) +from utama_core.entities.referee.referee_command import RefereeCommand # noqa: E402 +from utama_core.replay.scenario import ( # noqa: E402 + RobotState, + Scenario, + _referee_command_at, + scenario_from_replay, +) + +MOMENTS_DIR = REPO_ROOT / "utama_core" / "tests" / "moments" +DEFAULT_OPPONENT = "build_split_shape_kernel_strategy" +DEFAULT_SECONDS = 3.0 + +# Rulebook distances (`custom_referee/rules/keep_out_rule.py`, `robot_stop_speed_rule.py`). +KEEP_OUT_M = 0.5 +STOP_SPEED_MPS = 1.5 +# A robot inside the keep-out circle may drift with noise, never drive in: closer by more than +# this over `APPROACH_WINDOW_S` is driving in. +APPROACH_TOLERANCE_M = 0.05 +APPROACH_WINDOW_S = 0.25 +# ... and leaves it within the rulebook's grace for placement interference and STOP speed. +LEAVE_WITHIN_S = 2.0 +# A ball faster than this has been kicked (`events`). +KICK_SPEED_MPS = 2.0 +# `events` prints at most this many lines. +MAX_EVENT_LINES = 30 +# `play --against`: worktrees live beside the repo (not in it), each with the compiled extensions +# below copied in (gitignored, so a new worktree lacks them; without them the planner falls back to +# Python and results differ). +AGAINST_DIR = REPO_ROOT.parent / "moments-work-20261017" / "against" +BUILD_ARTIFACTS = ( + "utama_core/motion_planning/src/fastpathplanning/_fastpathplanner.cpython-*.so", + "utama_core/rsoccer_simulator/src/Simulators/robosim/_robosim.cpython-*.so", +) + + +@dataclasses.dataclass +class Moment: + name: str + note: str + scenario: Scenario + checks: list[str] + requires: list[str] = dataclasses.field(default_factory=list) + strategy: str = "all" + opponent: str = DEFAULT_OPPONENT + seconds: float = DEFAULT_SECONDS + known_failure: Optional[str] = None + designated_position: Optional[tuple[float, float]] = None + source: Optional[dict] = None + sim_noise: Optional[dict] = None + + +def short_name(name: str) -> str: + return name.removeprefix("build_").removesuffix("_kernel_strategy") + + +def _full_name(name: str) -> str: + if name == "all" or name.startswith("build_") or _is_file_spec(name): + return name + return f"build_{name}_kernel_strategy" + + +def _is_file_spec(name: str) -> bool: + """`PATH.py:FACTORY`: a factory in a scratch file, not a catalog name.""" + return name.rpartition(":")[0].endswith(".py") + + +def to_dict(m: Moment) -> dict: + s = m.scenario + return { + "name": m.name, + "note": m.note, + "source": m.source, + "strategy": m.strategy, + "opponent": m.opponent, + "seconds": m.seconds, + "checks": m.checks, + "requires": m.requires, + "known_failure": m.known_failure, + "designated_position": list(m.designated_position) if m.designated_position else None, + "sim_noise": m.sim_noise, + "scenario": { + "referee_command": s.referee_command.name if s.referee_command is not None else None, + "ball": [s.ball_x, s.ball_y, s.ball_vx, s.ball_vy], + "friendly": [dataclasses.asdict(r) for r in s.friendly_robots], + "enemy": [dataclasses.asdict(r) for r in s.enemy_robots], + }, + } + + +def from_dict(d: dict) -> Moment: + sc = d["scenario"] + bx, by, bvx, bvy = sc["ball"] + command = sc.get("referee_command") + scenario = Scenario( + sim_time=0.0, + ball_x=bx, + ball_y=by, + ball_vx=bvx, + ball_vy=bvy, + friendly_robots=tuple(RobotState(**r) for r in sc["friendly"]), + enemy_robots=tuple(RobotState(**r) for r in sc["enemy"]), + referee_command=RefereeCommand[command] if command else None, + source_replay=Path((d.get("source") or {}).get("replay") or "hand-made"), + ) + dp = d.get("designated_position") + return Moment( + name=d["name"], + note=d["note"], + scenario=scenario, + checks=list(d["checks"]), + requires=list(d.get("requires", [])), + strategy=_full_name(d.get("strategy", "all")), + opponent=_full_name(d.get("opponent") or DEFAULT_OPPONENT), + seconds=float(d.get("seconds", DEFAULT_SECONDS)), + known_failure=d.get("known_failure"), + designated_position=tuple(dp) if dp else None, + source=d.get("source"), + sim_noise=d.get("sim_noise"), + ) + + +def load(path: Path) -> Moment: + m = from_dict(json.loads(Path(path).read_text())) + validate(m) + return m + + +def save(m: Moment, path: Optional[Path] = None) -> Path: + validate(m) + path = Path(path or MOMENTS_DIR / f"{m.name}.json") + path.write_text(json.dumps(to_dict(m), indent=2) + "\n") + return path + + +def catalog() -> list[str]: + """The round-robin's strategies: every factory `kernel_strategy.py` re-exports, minus the + retired ones.""" + from tools.evaluation.round_robin import _CONFIG_NAMES, RETIRED + + return [n for n in _CONFIG_NAMES if n not in RETIRED] + + +def _referee_behaviour(name: str) -> tuple: + """What decides a strategy's robots during a restart: its referee overrides and its + goalkeeper tactic. Strategies equal here move identically while the override holds.""" + from utama_core.strategy import kernel_strategy + + strategy = getattr(kernel_strategy, name)(OUTFIELD_ROBOT_IDS)(None) + overrides = tuple( + sorted((c.name, getattr(f, "__qualname__", repr(f))) for c, f in strategy.referee_overrides.items()) + ) + pinned = strategy._pinned_slot + return overrides, type(pinned.tactic).__qualname__ if pinned is not None else None + + +def strategies_for(m: Moment) -> list[str]: + """The strategies a moment is played against (see the module docstring).""" + from utama_core.engine.referee_override import is_override_command + + if m.strategy != "all": + return [m.strategy] + names = catalog() + if not is_override_command(m.scenario.referee_command): + return names + seen, out = set(), [] + for n in names: + key = _referee_behaviour(n) + if key not in seen: + seen.add(key) + out.append(n) + return out + + +class MomentError(ValueError): + """A moment that cannot be played: every problem found, each named.""" + + +def referee_rule_names() -> set[str]: + """The `rule_name=` literals of the referee's rules, read from `custom_referee/` (rules and state machine).""" + import ast + + names = set() + for path in (REPO_ROOT / "utama_core" / "custom_referee").rglob("*.py"): + for node in ast.walk(ast.parse(path.read_text())): + if isinstance(node, ast.Call): + for kw in node.keywords: + if kw.arg == "rule_name" and isinstance(kw.value, ast.Constant) and isinstance(kw.value.value, str): + names.add(kw.value.value) + return names + + +def validate(m: Moment) -> None: + """Raise `MomentError` naming every way `m` cannot be played.""" + from utama_core.config.field_params import STANDARD_FIELD_DIMS + + half_x, half_y = STANDARD_FIELD_DIMS.full_field_half_length, STANDARD_FIELD_DIMS.full_field_half_width + s, problems = m.scenario, [] + for side, robots in (("friendly", s.friendly_robots), ("enemy", s.enemy_robots)): + got = sorted(r.id for r in robots) + if got != list(range(N_OUTFIELD + 1)): + problems.append(f"{side} robots must have ids 0-{N_OUTFIELD} exactly once, got {got}") + for r in robots: + if abs(r.x) > half_x or abs(r.y) > half_y: + problems.append(f"{side} robot {r.id} at ({r.x:.2f}, {r.y:.2f}) is off the pitch") + if abs(s.ball_x) > half_x or abs(s.ball_y) > half_y: + problems.append(f"ball at ({s.ball_x:.2f}, {s.ball_y:.2f}) is off the pitch") + rules = referee_rule_names() + if not m.checks: + problems.append("checks is empty: name at least one") + for c in m.checks: + if c not in CHECKS and c != "*" and c not in rules: + problems.append(f"unknown check {c!r}: use one of {sorted([*CHECKS, '*', *sorted(rules)])}") + for r in m.requires: + if r not in REQUIRES: + problems.append(f"unknown requires {r!r}: use one of {sorted(REQUIRES)}") + if m.seconds <= 0: + problems.append(f"seconds must be positive, got {m.seconds}") + names = catalog() + if m.strategy != "all" and m.strategy not in names: + problems.append(f"unknown strategy {short_name(m.strategy)!r}") + if m.opponent not in names: + problems.append(f"unknown opponent {short_name(m.opponent)!r}") + if problems: + raise MomentError(f"{m.name}:\n " + "\n ".join(problems)) + + +def template( + name: str, + note: str, + command: str, + checks: list[str], + ball: tuple[float, float] = (0.0, 0.0), + strategy: str = "all", + seconds: float = DEFAULT_SECONDS, +) -> Moment: + """A hand-made moment: both teams in a fixed formation (ours yellow on the right, facing left), + the given ball and referee command, the reference pool's noise with seed 1, source null.""" + from utama_core.replay.scenario import RobotState + + ours = [(4.0, 0.0), (2.2, 1.2), (2.2, -1.2), (1.0, 2.0), (1.0, -2.0), (0.6, 0.0)] + + def robots(positions, facing): + return tuple( + RobotState(id=i, x=x, y=y, orientation=facing, vx=0.0, vy=0.0) for i, (x, y) in enumerate(positions) + ) + + pool = json.loads((REPO_ROOT / "tools" / "evaluation" / "pools" / "pool_v1.json").read_text()) + scenario = Scenario( + sim_time=0.0, + ball_x=ball[0], + ball_y=ball[1], + ball_vx=0.0, + ball_vy=0.0, + friendly_robots=robots(ours, math.pi), + enemy_robots=robots([(-x, y) for x, y in ours], 0.0), + referee_command=RefereeCommand[command], + source_replay=Path("hand-made"), + ) + return Moment( + name=name, + note=note, + scenario=scenario, + checks=list(checks), + strategy=_full_name(strategy), + seconds=seconds, + sim_noise={**pool["sim_noise"], "seed": 1}, + ) + + +def with_seed(m: Moment, seed: int) -> Moment: + """`m` played with its sim noise's seed replaced (`play --seed`), the JSON untouched.""" + if m.sim_noise is None: + raise MomentError(f"{m.name}: no sim_noise to seed (it plays without noise)") + return dataclasses.replace(m, sim_noise={**m.sim_noise, "seed": seed}) + + +@dataclasses.dataclass(frozen=True) +class Tick: + """What the checks see of one played tick, in our side's frame.""" + + t: float # seconds since the moment started + command: Optional[RefereeCommand] + ball: Optional[tuple[float, float]] + ours: dict[int, tuple[float, float]] # robot id -> position + our_speeds: dict[int, float] + our_foul: Optional[tuple[str, tuple[int, ...]]] # (rule name, robot ids) charged to us this tick + ball_speed: Optional[float] = None # m/s, for `events`' kicks + + +def restart_side(command: Optional[RefereeCommand], we_are_yellow: bool = True) -> Optional[str]: + """ "ours" for a restart our team takes, "theirs" for one the other team takes, None for + STOP, live play and timeouts.""" + if command is None or command.name.startswith("TIMEOUT"): + return None + if command.name.endswith("YELLOW" if we_are_yellow else "BLUE"): + return "ours" + if command.name.endswith("BLUE" if we_are_yellow else "YELLOW"): + return "theirs" + return None + + +def _restart_against_us(command: Optional[RefereeCommand], we_are_yellow: bool) -> bool: + """STOP, or a restart the other team takes: when every robot of ours keeps out.""" + return command is RefereeCommand.STOP or restart_side(command, we_are_yellow) == "theirs" + + +def keeps_clear(ticks: list[Tick], we_are_yellow: bool = True) -> list[str]: + """During STOP and the other team's restarts, a robot of ours inside the keep-out circle + (0.5 m from the ball) never moves closer to the ball, and leaves within 2 s. Moving closer is + the robot's own move, measured against where the ball is now: a ball that is placed or + rolls toward a robot standing still is not the robot driving in.""" + window = round(APPROACH_WINDOW_S * TICKS_PER_SECOND) + failures = [] + inside_since: dict[int, float] = {} + for tick in ticks: + if not _restart_against_us(tick.command, we_are_yellow) or tick.ball is None: + inside_since.clear() + continue + for rid, (x, y) in tick.ours.items(): + if math.hypot(x - tick.ball[0], y - tick.ball[1]) >= KEEP_OUT_M: + inside_since.pop(rid, None) + continue + since = inside_since.setdefault(rid, tick.t) + if tick.t - since > LEAVE_WITHIN_S: + failures.append( + f"t={tick.t:.2f}s {tick.command.name}: robot {rid} inside the keep-out circle for {LEAVE_WITHIN_S} s" + ) + for before, now in zip(ticks, ticks[window:]): + if not _restart_against_us(now.command, we_are_yellow) or now.ball is None or before.ball is None: + continue + for rid, (x, y) in now.ours.items(): + d_now = math.hypot(x - now.ball[0], y - now.ball[1]) + if rid not in before.ours or d_now >= KEEP_OUT_M: + continue + bx, by = before.ours[rid] + d_before = math.hypot(bx - now.ball[0], by - now.ball[1]) + if d_before - d_now > APPROACH_TOLERANCE_M: + failures.append( + f"t={now.t:.2f}s {now.command.name}: robot {rid} drove in to {d_now:.2f} m from the ball " + f"(from {d_before:.2f} m {APPROACH_WINDOW_S} s before)" + ) + return _first_per_robot(failures) + + +def stop_speed(ticks: list[Tick], we_are_yellow: bool = True) -> list[str]: + """During STOP no robot of ours moves faster than 1.5 m/s.""" + failures = [ + f"t={tick.t:.2f}s STOP: robot {rid} at {v:.2f} m/s" + for tick in ticks + if tick.command is RefereeCommand.STOP + for rid, v in tick.our_speeds.items() + if v > STOP_SPEED_MPS + ] + return _first_per_robot(failures) + + +def _first_per_robot(failures: list[str]) -> list[str]: + """One failure per robot: the first is the one to look at.""" + seen, out = set(), [] + for f in failures: + robot = f.split("robot ")[1].split(" ")[0] + if robot not in seen: + seen.add(robot) + out.append(f) + return out + + +# A restart's command holds this long from the start, so the restart happened (a free kick ends +# only when it is taken; a ball placement needs the ball settled at the target for 2 s). +RESTART_LASTS_S = 1.0 +# A robot of ours this close to the ball: the keep-out circle (0.5 m) was tested, not a robot far off. +NEAR_BALL_M = 1.0 + + +def _dist(a: tuple[float, float], b: tuple[float, float]) -> float: + return math.hypot(a[0] - b[0], a[1] - b[1]) + + +def _restart_lasts(ticks: list[Tick], we_are_yellow: bool = True) -> Optional[str]: + """None when the restart the moment starts from (its first tick's referee command) stays + active for `RESTART_LASTS_S`, else why not.""" + if not ticks or restart_side(ticks[0].command, we_are_yellow) is None: + name = ticks[0].command.name if ticks and ticks[0].command else "none" + return f"the moment does not start from a restart (its first command is {name})" + start = ticks[0].command + for tick in ticks: + if tick.command is not start: + if tick.t < RESTART_LASTS_S: + return f"{start.name} ended at {tick.t:.2f} s, need {RESTART_LASTS_S} s" + return None + if ticks[-1].t < RESTART_LASTS_S: + return f"{start.name} still on at the end of the play, {ticks[-1].t:.2f} s, need {RESTART_LASTS_S} s" + return None + + +def _robot_near_ball(ticks: list[Tick], we_are_yellow: bool = True) -> Optional[str]: + """None when some robot of ours comes within `NEAR_BALL_M` of the ball, else the closest.""" + dists = [_dist(pos, t.ball) for t in ticks if t.ball is not None for pos in t.ours.values()] + closest = min(dists, default=math.inf) + if closest <= NEAR_BALL_M: + return None + return f"the closest robot of ours to the ball was {closest:.2f} m, need {NEAR_BALL_M} m" + + +# Named checks a moment can ask for. Add one only for a failure no referee rule names. +CHECKS = { + "keeps_clear": keeps_clear, + "stop_speed": stop_speed, +} + +# Named conditions a moment can `require`: each returns None when it holds during the play, else +# why it did not arise. Judged in `judge`, reported as "did not arise: ()". +REQUIRES = { + "restart_lasts": _restart_lasts, + "robot_near_ball": _robot_near_ball, +} + + +def judge(m: Moment, ticks: list[Tick], we_are_yellow: bool = True) -> list[str]: + """Every way `ticks` broke `m.checks`.""" + failures = [] + for name in m.requires: + why = REQUIRES[name](ticks, we_are_yellow) + if why: + failures.append(f"did not arise: {name} ({why})") + for name in m.checks: + if name in CHECKS: + failures += CHECKS[name](ticks, we_are_yellow) + rules = [c for c in m.checks if c not in CHECKS] + for tick in ticks: + if tick.our_foul is not None and ("*" in rules or tick.our_foul[0] in rules): + rule, robots = tick.our_foul + failures.append(f"t={tick.t:.2f}s referee: {rule} foul by robot(s) {list(robots)}") + return failures + + +def _command_name(command: Optional[RefereeCommand]) -> str: + return command.name if command is not None else "none" + + +def events(ticks: list[Tick], we_are_yellow: bool = True) -> list[str]: + """A compact account of `ticks`: the referee command changes, our fouls, our robots entering + and leaving the keep-out circle during STOP or the other team's restart, the ball's kicks + (speed rising above `KICK_SPEED_MPS`), in time order; then each of our robots' closest + approach to the ball. At most `MAX_EVENT_LINES` lines, the rest counted in a "... N more".""" + body: list[str] = [] + inside: set[int] = set() # robots in the keep-out circle during a restart against us + prev: Optional[Tick] = None + for tick in ticks: + if prev is not None and tick.command is not prev.command: + body.append(f"t={tick.t:.2f}s referee: {_command_name(prev.command)} -> {_command_name(tick.command)}") + if tick.our_foul is not None and tick.our_foul != (prev.our_foul if prev else None): + rule, robots = tick.our_foul + body.append(f"t={tick.t:.2f}s foul: {rule} by robot(s) {list(robots)}") + against = tick.ball is not None and _restart_against_us(tick.command, we_are_yellow) + for rid, pos in tick.ours.items(): + d = _dist(pos, tick.ball) if tick.ball is not None else math.inf + near = against and d < KEEP_OUT_M + if near and rid not in inside: + inside.add(rid) + body.append( + f"t={tick.t:.2f}s {_command_name(tick.command)}: robot {rid} enters the keep-out circle at {d:.2f} m" + ) + elif not near and rid in inside: + inside.discard(rid) + body.append(f"t={tick.t:.2f}s robot {rid} leaves the keep-out circle at {d:.2f} m") + fast = tick.ball_speed is not None and tick.ball_speed > KICK_SPEED_MPS + was_fast = prev is not None and prev.ball_speed is not None and prev.ball_speed > KICK_SPEED_MPS + if fast and not was_fast: + body.append(f"t={tick.t:.2f}s ball kicked: {tick.ball_speed:.2f} m/s") + prev = tick + closest: dict[int, tuple[float, float]] = {} # robot id -> (distance, t) + for tick in ticks: + if tick.ball is None: + continue + for rid, pos in tick.ours.items(): + d = _dist(pos, tick.ball) + if rid not in closest or d < closest[rid][0]: + closest[rid] = (d, tick.t) + summary = [f"robot {rid} closest to the ball: {d:.2f} m at t={t:.2f}s" for rid, (d, t) in sorted(closest.items())] + room = MAX_EVENT_LINES - len(summary) + if len(body) > room: + body = body[: room - 1] + [f"... {len(body) - room + 1} more"] + return body + summary + + +def factory_for(name: str) -> Callable: + """The strategy factory a name means: a catalog `build_*_kernel_strategy`, or `PATH.py:FACTORY`, + a factory in a scratch file imported as it is (so a tactic can be tried without a repo change).""" + from utama_core.strategy import kernel_strategy + + if not _is_file_spec(name): + try: + return getattr(kernel_strategy, name) + except AttributeError: + raise MomentError(f"unknown strategy {short_name(name)!r}") from None + path, _, func = name.rpartition(":") + if not Path(path).is_file(): + raise MomentError(f"{name}: no such file {path}") + spec = importlib.util.spec_from_file_location(f"scratch_{Path(path).stem}", path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + factory = getattr(module, func, None) + if not callable(factory): + raise MomentError(f"{name}: {path} has no factory {func!r}") + return factory + + +def label(name: str) -> str: + """How a strategy name is shown: the short catalog name, or `file:FACTORY` for a scratch file.""" + if _is_file_spec(name): + path, _, func = name.rpartition(":") + return f"{Path(path).stem}:{func}" + return short_name(name) + + +def play(m: Moment, strategy: str) -> list[Tick]: + """Play `m` with `strategy` on our side (yellow) for `m.seconds`.""" + from utama_core.custom_referee import CustomReferee + from utama_core.engine.abstract_strategy import AbstractStrategy + from utama_core.replay.scenario import apply_scenario + from utama_core.rsoccer_simulator.src.Utils.sim_noise import SimNoise + from utama_core.run import StrategyRunner + + def side(name: str) -> AbstractStrategy: + return AbstractStrategy(build_kernel_strategy=factory_for(name)(OUTFIELD_ROBOT_IDS)) + + referee = CustomReferee.from_profile_name( + "simulation", n_robots_yellow=N_OUTFIELD + 1, n_robots_blue=N_OUTFIELD + 1 + ) + runner = StrategyRunner( + strategy=side(strategy), + opp_strategy=side(m.opponent), + my_team_is_yellow=True, + my_team_is_right=True, + mode="rsim", + exp_friendly=N_OUTFIELD + 1, + exp_enemy=N_OUTFIELD + 1, + exp_ball=True, + referee=referee, + enable_vision_stream=False, + sim_noise=SimNoise.from_dict(m.sim_noise) if m.sim_noise else None, + ) + ticks = [] + try: + apply_scenario(runner, m.scenario, verify=True) + command = m.scenario.referee_command + if m.designated_position is not None and command is not None and "PLACEMENT" in command.name: + ts = runner.my.current_game_frame.ts + # From STOP, or the referee puts a STOP of its own first (`state_machine.set_command`). + # A placement leads to the free kick for the same team, as `state_machine.py` has it. + referee.set_command(RefereeCommand.STOP, ts) + referee.set_command( + command, + ts, + designated_position=m.designated_position, + next_command=RefereeCommand[command.name.replace("BALL_PLACEMENT", "DIRECT_FREE")], + ) + start = runner.my.current_game_frame.ts + for _ in range(int(m.seconds * TICKS_PER_SECOND)): + runner.step_once() + game = runner.my.game + v = referee.last_violation + foul = None + if v is not None and runner.my_team_is_yellow in v.offending_teams: + foul = ( + v.rule_name, + tuple(rid for yellow, rid in v.offending_robots if yellow == runner.my_team_is_yellow), + ) + ticks.append( + Tick( + t=round(game.ts - start, 4), + command=game.referee.referee_command if game.referee is not None else None, + ball=(game.ball.p.x, game.ball.p.y) if game.ball is not None else None, + ours={rid: (r.p.x, r.p.y) for rid, r in game.friendly_robots.items()}, + our_speeds={rid: r.v.mag() for rid, r in game.friendly_robots.items()}, + our_foul=foul, + ball_speed=game.ball.v.mag() if game.ball is not None else None, + ) + ) + finally: + runner.close() + return ticks + + +def capture(replay: Path, t: float, **fields) -> Moment: + """A moment from the frame of `replay` nearest `t` (its referee command and designated + position from the replay's match log, its noise from the run's summary.json). `fields` are + the other `Moment` fields.""" + scenario = scenario_from_replay(replay, t) + scenario = _on_the_pitch(scenario) + summary = Path(replay).parent / "summary.json" + if fields.get("sim_noise") is None and summary.exists(): + fields["sim_noise"] = json.loads(summary.read_text()).get("sim_noise") + placement = scenario.referee_command is not None and "PLACEMENT" in scenario.referee_command.name + if fields.get("designated_position") is None and placement: + fields["designated_position"] = _designated_position(Path(replay), t) + return Moment(scenario=scenario, source={"replay": str(replay), "t": t}, **fields) + + +def _on_the_pitch(scenario: Scenario) -> Scenario: + """`scenario` with every robot moved onto the field lines, the farthest rsim teleports to: + a keeper in its goal mouth is a few centimetres behind the line. A ball off the pitch is an + error: pick a time once a placement has brought it back.""" + from utama_core.config.field_params import STANDARD_FIELD_DIMS + + half_x, half_y = STANDARD_FIELD_DIMS.full_field_half_length, STANDARD_FIELD_DIMS.full_field_half_width + if abs(scenario.ball_x) > half_x or abs(scenario.ball_y) > half_y: + raise SystemExit( + f"The ball is off the pitch at ({scenario.ball_x:.2f}, {scenario.ball_y:.2f}): pick a later --t." + ) + + def clamp(robots): + return tuple( + dataclasses.replace(r, x=max(-half_x, min(half_x, r.x)), y=max(-half_y, min(half_y, r.y))) for r in robots + ) + + return dataclasses.replace( + scenario, friendly_robots=clamp(scenario.friendly_robots), enemy_robots=clamp(scenario.enemy_robots) + ) + + +def _designated_position(replay: Path, t: float) -> Optional[tuple[float, float]]: + """The referee's designated position at `t`, from the replay's match log, if it has one.""" + log = replay.with_name(replay.name.split(".")[0] + ".intentions.jsonl") + if not log.exists(): + return None + designated = None + with log.open() as f: + for line in f: + event = json.loads(line) + if event.get("event") != "referee": + continue + if event["sim_time"] > t: + break + designated = event.get("designated") + return tuple(designated) if designated else None + + +def without_own_restarts(fouls: list[dict]) -> list[dict]: + """`fouls` minus those during a restart our team takes: `keeps_clear` does not apply there.""" + return [f for f in fouls if f.get("restart") != "ours"] + + +# Rules that stop play without being a rules foul: the ball leaving the field. +STOPPAGE_RULES = frozenset({"out_of_bounds"}) +FOUL_COLUMNS = ("strategy", "rule", "tactic", "restart") + + +def without_stoppages(fouls: list[dict]) -> list[dict]: + return [f for f in fouls if f["rule"] not in STOPPAGE_RULES] + + +def _foul_column(f: dict, column: str) -> str: + if column == "strategy": + return short_name(f["config_a"]) + if column == "restart": + return f.get("restart") or "live" + return str(f.get(column) or "-") + + +def count_fouls(fouls: list[dict], columns: list[str]) -> list[tuple[tuple[str, ...], int]]: + """How many of `fouls` fall in each combination of `columns` (names from `FOUL_COLUMNS`), + most common first; ties in name order.""" + counts = Counter(tuple(_foul_column(f, c) for c in columns) for f in fouls) + return sorted(counts.items(), key=lambda item: (-item[1], item[0])) + + +def fouls_json(fouls: list[dict], columns: Optional[list[str]] = None) -> str: + """`fouls` as JSON: the foul dicts as they are, or with `columns` the `count_fouls` rows as + {column: value, ..., "count": n} objects, most common first.""" + if columns is None: + data = fouls + else: + data = [{**dict(zip(columns, key)), "count": n} for key, n in count_fouls(fouls, columns)] + return json.dumps(data, indent=1, sort_keys=True) + + +def print_fouls_by(fouls: list[dict], columns: list[str]) -> None: + """`count_fouls` as a table: a count column, then one column per name, then the total.""" + rows = count_fouls(fouls, columns) + widths = [max([len(c)] + [len(key[i]) for key, _ in rows]) for i, c in enumerate(columns)] + print(f"{'count':>5} " + " ".join(f"{c:<{w}}" for c, w in zip(columns, widths))) + for key, n in rows: + print(f"{n:>5} " + " ".join(f"{v:<{w}}" for v, w in zip(key, widths))) + print(f"{sum(n for _, n in rows):>5} total") + + +def our_fouls(run_dir: Path, rule: Optional[str] = None) -> list[dict]: + """Every foul config_a (our side in the replay) committed in a round-robin run, from its + summary.json: the starting points for new moments. Each carries the referee `command` active + at its time (from the match's `.intentions.jsonl`, None if absent), its `restart` side + ("ours", "theirs", or None for live play and STOP) and its match's `replay` (the .npz, else the + .pkl, relative to the repo root; None if neither is there), for replay_trace and repro.""" + run_dir = Path(run_dir) + summary = json.loads((run_dir / "summary.json").read_text()) + out = [] + for r in summary["results"]: + stem = run_dir / f"{short_name(r['config_a'])}_vs_{short_name(r['config_b'])}" + sidecar = Path(f"{stem}.intentions.jsonl") + replay = next((p for p in (Path(f"{stem}.npz"), Path(f"{stem}.pkl")) if p.exists()), None) + for f in (r.get("stats") or {}).get("fouls") or []: + if f.get("side") == "friendly" and (rule is None or f["rule"] == rule): + command = _referee_command_at(sidecar, f["sim_time"]) + out.append( + { + "config_a": r["config_a"], + "config_b": r["config_b"], + **f, + "command": command.name if command else None, + "restart": restart_side(command), + "replay": _repo_relative(replay) if replay is not None else None, + } + ) + return out + + +def _repo_relative(path: Path) -> str: + """`path` relative to the repo root (as a string), or as given if it is outside the repo.""" + try: + return str(Path(path).resolve().relative_to(REPO_ROOT)) + except ValueError: + return str(path) + + +def parse_seeds(spec: str) -> list[int]: + """`0-4` or `0,2,3` (or both, `0-1,3`): the seeds in order, each once.""" + seeds = [] + for part in spec.split(","): + if re.fullmatch(r"\d+-\d+", part): + lo, hi = (int(x) for x in part.split("-")) + if hi < lo: + raise ValueError(f"seed range {part} runs backwards") + seeds += range(lo, hi + 1) + elif re.fullmatch(r"\d+", part): + seeds.append(int(part)) + else: + raise ValueError(f"bad seed {part!r}: use a range like 0-4 or a list like 0,2,3") + return list(dict.fromkeys(seeds)) + + +def run_cells(m: Moment, names: list[str], seeds: list[Optional[int]]) -> dict: + """{(strategy, seed): (failures, ticks)} for each strategy and seed; seed None is the moment's own.""" + cells = {} + for name in names: + for seed in seeds: + played = m if seed is None else with_seed(m, seed) + ticks = play(played, name) + cells[(name, seed)] = (judge(played, ticks), ticks) + return cells + + +def _git(*args: str, cwd: Path = REPO_ROOT) -> str: + proc = subprocess.run(["git", *args], cwd=cwd, capture_output=True, text=True) + if proc.returncode: + raise SystemExit(f"git {' '.join(args)}: {proc.stderr.strip()}") + return proc.stdout.strip() + + +def ref_worktree(ref: str) -> Path: + """A worktree of the tree at `ref` under `AGAINST_DIR` (reused if it is there at that commit), + with the main tree's `.pixi` and compiled extensions, and this `tools/moments.py` in place of + its own: the same judge plays the moment, only the code under test differs, so any commit can + be compared, including ones from before `--seeds`. Not removed afterwards.""" + sha = _git("rev-parse", "--verify", f"{ref}^{{commit}}") + path = AGAINST_DIR / sha[:8] + if path.exists(): + if _git("rev-parse", "HEAD", cwd=path) != sha: + raise SystemExit( + f"{path} exists at another commit: remove it (git worktree remove --force {path}) and retry" + ) + else: + AGAINST_DIR.mkdir(parents=True, exist_ok=True) + _git("worktree", "add", "--detach", str(path), sha) + if not os.path.lexists(path / ".pixi"): + os.symlink(REPO_ROOT / ".pixi", path / ".pixi") + for pattern in BUILD_ARTIFACTS: + for src in REPO_ROOT.glob(pattern): + shutil.copy2(src, path / src.relative_to(REPO_ROOT)) + shutil.copy2(Path(__file__).resolve(), path / "tools" / "moments.py") + return path + + +def ref_cells(json_path: Path, name: str, seeds: list[int], tree: Path) -> dict[int, list[str]]: + """{seed: failures} for one strategy, played in `tree` by this `tools/moments.py` (copied there by + `ref_worktree`; its env's python, run directly so pixi cannot re-solve the shared env).""" + fd, out = tempfile.mkstemp(dir=AGAINST_DIR, suffix=".json") + os.close(fd) + python = tree / ".pixi" / "envs" / "default" / "bin" / "python" + env = {**os.environ, "PATH": f"{python.parent}{os.pathsep}{os.environ.get('PATH', '')}"} + cmd = [ + str(python), + "tools/moments.py", + "play", + str(json_path), + "--strategy", + name, + "--seeds", + ",".join(map(str, seeds)), + "--json", + out, + ] + proc = subprocess.run(cmd, cwd=tree, env=env, capture_output=True, text=True) + if proc.returncode: + Path(out).unlink(missing_ok=True) + raise SystemExit(f"REF {tree}: play failed for {label(name)}:\n{proc.stderr[-1500:]}") + result = json.loads(Path(out).read_text()) + Path(out).unlink() + return {int(seed): failures for seed, failures in result.items()} + + +def print_grid(seeds: list[Optional[int]], rows: list[tuple[str, str, list[list[str]]]]) -> None: + """One row per (strategy, version): ok/FAIL per seed, then each failing cell's first failure.""" + print(f"{'strategy':<28} {'':<9}" + "".join(f"{s:>8}" for s in seeds)) + for name, version, cells in rows: + print(f"{name:<28} {version:<9}" + "".join(f"{'FAIL' if f else 'ok':>8}" for f in cells)) + for name, version, cells in rows: + for seed, failures in zip(seeds, cells): + if failures: + tag = f"seed {seed} {name}" + (f" [{version}]" if version else "") + print(f" {tag}: {failures[0]}") + + +def print_events(cells: dict) -> None: + """The events of the first failing play, else the first play.""" + key = next((k for k, (failures, _) in cells.items() if failures), next(iter(cells))) + failures, ticks = cells[key] + name, seed = key + where = label(name) + ("" if seed is None else f" seed {seed}") + print(f"events ({where}, {'FAIL' if failures else 'ok'}):") + for line in events(ticks): + print(f" {line}") + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + sub = parser.add_subparsers(dest="cmd", required=True) + + p = sub.add_parser("capture", help="write a moment from a replay") + p.add_argument("replay", type=Path) + p.add_argument("--t", type=float, required=True, help="sim time (s) of the frame to start from") + p.add_argument("--name", required=True) + p.add_argument("--note", required=True, help="what went wrong, and why it must not happen again") + p.add_argument( + "--check", + nargs="+", + required=True, + help=f"checks ({', '.join(CHECKS)}) and/or referee rule names our side must not break ('*' for any)", + ) + p.add_argument("--strategy", default="all") + p.add_argument("--opponent", default=DEFAULT_OPPONENT) + p.add_argument("--seconds", type=float, default=DEFAULT_SECONDS) + p.add_argument("--designated-position", type=float, nargs=2, default=None) + p.add_argument("--out", type=Path, default=None, help=f"where to write it (default {MOMENTS_DIR}/.json)") + + p = sub.add_parser("fouls", help="list our side's fouls in a round-robin run") + p.add_argument("run_dir", type=Path) + p.add_argument("--rule", default=None) + p.add_argument( + "--skip-own-restart", + action="store_true", + help="leave out fouls during our own restarts (keeps_clear does not apply there)", + ) + p.add_argument( + "--include-stoppages", + action="store_true", + help="also list out_of_bounds (the ball leaving the field: a stoppage, not a rules foul)", + ) + p.add_argument( + "--by", + default=None, + metavar="COLUMNS", + help=f"print counts per combination of these columns, comma-separated, of {', '.join(FOUL_COLUMNS)} " + "(e.g. strategy,rule), instead of one line per foul", + ) + p.add_argument( + "--json", + action="store_true", + help="write JSON to stdout instead of text: the foul dicts, or with --by a list of {column: value, ..., " + "count} rows", + ) + + p = sub.add_parser("new", help="write a hand-made moment template to edit by hand") + p.add_argument("name", help="lowercase letters, digits and underscores") + p.add_argument("--command", required=True, choices=[c.name for c in RefereeCommand]) + p.add_argument("--ball", type=float, nargs=2, default=(0.0, 0.0), metavar=("X", "Y")) + p.add_argument("--strategy", default="all") + p.add_argument("--seconds", type=float, default=DEFAULT_SECONDS) + p.add_argument("--check", nargs="+", required=True, help=f"checks ({', '.join(CHECKS)}), rule names or '*'") + p.add_argument("--note", required=True, help="what went wrong, and why it must not happen again") + + p = sub.add_parser("play", help="play a moment and print what broke its checks") + p.add_argument("moment", type=Path) + p.add_argument( + "--strategy", + default=None, + help="one strategy instead of the moment's own: a catalog name, or PATH.py:FACTORY from a scratch file", + ) + p.add_argument("--seed", type=int, default=None, help="the sim noise seed for this run (the JSON is not changed)") + p.add_argument("--seeds", help="play under each seed (0-4 or 0,2,3) and print one table of ok/FAIL") + p.add_argument( + "--against", + metavar="REF", + help="also play under the code at this git ref (its worktree is left in place), rows side by side", + ) + p.add_argument("--events", action="store_true", help="after the verdict, the events of the first failing play") + p.add_argument("--json", type=Path, help=argparse.SUPPRESS) # --against reads the other tree's results + + args = parser.parse_args() + if args.cmd == "new": + if not re.fullmatch(r"[a-z0-9_]+", args.name): + parser.error("the name must be lowercase letters, digits and underscores") + path = MOMENTS_DIR / f"{args.name}.json" + if path.exists(): + parser.error(f"{path} exists") + try: + m = template( + args.name, + args.note, + args.command, + args.check, + ball=tuple(args.ball), + strategy=args.strategy, + seconds=args.seconds, + ) + print(f"Wrote {save(m)}: edit its positions by hand, then `play` it") + except MomentError as e: + parser.error(str(e)) + elif args.cmd == "capture": + m = capture( + args.replay, + args.t, + name=args.name, + note=args.note, + checks=args.check, + strategy=_full_name(args.strategy), + opponent=_full_name(args.opponent), + seconds=args.seconds, + designated_position=tuple(args.designated_position) if args.designated_position else None, + ) + print( + f"Wrote {save(m, args.out)} ({m.scenario.referee_command.name if m.scenario.referee_command else 'no command'})" + ) + elif args.cmd == "fouls": + fouls = our_fouls(args.run_dir, args.rule) + if args.skip_own_restart: + fouls = without_own_restarts(fouls) + if not args.include_stoppages: + fouls = without_stoppages(fouls) + if args.by: + columns = [c.strip() for c in args.by.split(",")] + unknown = [c for c in columns if c not in FOUL_COLUMNS] + if unknown: + parser.error(f"--by: unknown column(s) {unknown}; choose from {', '.join(FOUL_COLUMNS)}") + if args.json: + print(fouls_json(fouls, columns)) + else: + print_fouls_by(fouls, columns) + elif args.json: + print(fouls_json(fouls)) + else: + for f in fouls: + print( + f"{f['rule']:<28} t={f['sim_time']:>7.2f} robot {f.get('robot_id')} {f.get('tactic') or '':<24} " + f"{(f['command'] or '?'):<22} {f['restart'] or 'live':<7} " + f"{short_name(f['config_a'])} vs {short_name(f['config_b'])}" + ) + else: + m = load(args.moment) + if args.seed is not None and args.seeds: + parser.error("--seed and --seeds: pick one") + try: + seeds = parse_seeds(args.seeds) if args.seeds else None + if args.seed is not None: + m = with_seed(m, args.seed) + if args.against and seeds is None: + if m.sim_noise is None: + parser.error(f"{m.name} plays without noise: pass --seeds to say which runs to compare") + seeds = [m.sim_noise["seed"]] + except (MomentError, ValueError) as e: + parser.error(str(e)) + names = [_full_name(args.strategy)] if args.strategy else strategies_for(m) + try: + cells = run_cells(m, names, [None] if seeds is None else seeds) + except MomentError as e: + parser.error(str(e)) + if args.json: + args.json.write_text(json.dumps({str(seed): failures for (_, seed), (failures, _) in cells.items()})) + elif args.against: + tree = ref_worktree(args.against) + ref = {name: ref_cells(args.moment.resolve(), name, seeds, tree) for name in names} + now_rows = [(label(n), "now", [cells[(n, s)][0] for s in seeds]) for n in names] + ref_rows = [(label(n), tree.name, [ref[n][s] for s in seeds]) for n in names] + rows = [row for pair in zip(now_rows, ref_rows) for row in pair] + print_grid(seeds, rows) + n = len(names) * len(seeds) + now_ok = sum(not cells[(name, s)][0] for name in names for s in seeds) + ref_ok = sum(not ref[name][s] for name in names for s in seeds) + print(f"now: {now_ok}/{n} ok {tree.name}: {ref_ok}/{n} ok") + print(f"REF worktree: {tree} (remove it with: git worktree remove --force {tree})") + elif seeds is not None: + rows = [(label(n), "", [cells[(n, s)][0] for s in seeds]) for n in names] + print_grid(seeds, rows) + ok = sum(not f for f, _ in cells.values()) + print(f"{ok}/{len(cells)} ok") + else: + for name in names: + failures, _ = cells[(name, None)] + print(f"{label(name):<28} {'FAIL' if failures else 'ok'}") + for f in failures: + print(f" {f}") + if args.events: + print_events(cells) + + +if __name__ == "__main__": + main() diff --git a/tools/motion_planning_benchmark.py b/tools/motion_planning_benchmark.py index b544bd49..11357fcf 100644 --- a/tools/motion_planning_benchmark.py +++ b/tools/motion_planning_benchmark.py @@ -6,6 +6,7 @@ pixi run python tools/motion_planning_benchmark.py pixi run python tools/motion_planning_benchmark.py --scenarios direct static_slalom + pixi run python tools/motion_planning_benchmark.py --scenarios direct --figures docs/img/motion_planning The tool deliberately exercises every controller through StrategyRunner and the normal ``move()`` path. It writes a raw JSON artifact and a Markdown comparison @@ -19,8 +20,8 @@ import math import random import statistics -import subprocess import sys +import textwrap import time from dataclasses import asdict, dataclass, field from datetime import datetime, timezone @@ -45,6 +46,7 @@ from utama_core.entities.data.command import RobotCommand from utama_core.entities.data.vector import Vector2D from utama_core.entities.game import Game +from utama_core.global_utils.git_utils import git_revision from utama_core.motion_planning.src.common.motion_controller import MotionController from utama_core.run import StrategyRunner from utama_core.skills.src.utils.move_utils import move @@ -746,9 +748,11 @@ def _observe_command(self, command: tuple[Vector2D, float]) -> None: class BenchmarkManager(AbstractTestManager): n_episodes = 1 - def __init__(self, scenario: Scenario): + def __init__(self, scenario: Scenario, record_trace: bool = False): super().__init__() self.scenario = scenario + # Per-tick robot state for `--figures` only; `None` keeps the default run unchanged. + self.trace: dict | None = {"t": [], "robots": {}, "clearance": []} if record_trace else None self.start_ts: float | None = None self.last_ts: float | None = None self.last_positions: dict[RobotKey, Point] = {} @@ -888,13 +892,18 @@ def eval_status(self, game: Game) -> TestingStatus: new_colliding_pairs: set[tuple[RobotKey, RobotKey]] = set() robot_items = sorted(robots.items()) collision_distance = self.scenario.effective_collision_distance_m + tick_min_distance: float | None = None for index, (key_a, robot_a) in enumerate(robot_items): for key_b, robot_b in robot_items[index + 1 :]: distance = robot_a.p.distance_to(robot_b.p) if self.min_center_distance_m is None or distance < self.min_center_distance_m: self.min_center_distance_m = distance + if tick_min_distance is None or distance < tick_min_distance: + tick_min_distance = distance if distance < collision_distance: new_colliding_pairs.add((key_a, key_b)) + if self.trace is not None: + self._record_tick(elapsed, robots, tick_min_distance) self.collision_events += len(new_colliding_pairs - self.colliding_pairs) self.colliding_pairs = new_colliding_pairs @@ -911,6 +920,16 @@ def eval_status(self, game: Game) -> TestingStatus: return TestingStatus.FAILURE return TestingStatus.IN_PROGRESS + def _record_tick(self, elapsed: float, robots: dict[RobotKey, object], min_distance: float | None) -> None: + assert self.trace is not None + self.trace["t"].append(elapsed) + for key, robot in robots.items(): + entry = self.trace["robots"].setdefault(f"{key[0]}:{key[1]}", {"xy": [], "speed": []}) + entry["xy"].append((robot.p.x, robot.p.y)) + entry["speed"].append(math.hypot(robot.v.x, robot.v.y)) + # Same definition as `min_clearance_m` in `result()`: nearest centre pair minus both radii. + self.trace["clearance"].append(None if min_distance is None else min_distance - COLLISION_DISTANCE) + def result(self) -> dict: targets = self._targets() direct_distance = 0.0 @@ -971,21 +990,10 @@ def _percentile(values: list[float], fraction: float) -> float | None: return ordered[math.ceil(fraction * len(ordered)) - 1] -def _git_revision() -> str | None: - try: - result = subprocess.run( - ["git", "rev-parse", "--short", "HEAD"], - check=True, - capture_output=True, - text=True, - ) - except (OSError, subprocess.CalledProcessError): - return None - return result.stdout.strip() or None - - -def run_case(scheme: str, scenario: Scenario, repeat: int, timing_warmup_calls: int) -> dict: - manager = BenchmarkManager(scenario) +def run_case( + scheme: str, scenario: Scenario, repeat: int, timing_warmup_calls: int, record_trace: bool = False +) -> dict: + manager = BenchmarkManager(scenario, record_trace=record_trace) my_strategy = _go_to_targets_strategy(scenario.friendly_targets, scenario.friendly_target_trajectories) opp_strategy = ( _go_to_targets_strategy(scenario.enemy_targets, scenario.enemy_target_trajectories) @@ -1049,6 +1057,8 @@ def run_case(scheme: str, scenario: Scenario, repeat: int, timing_warmup_calls: command_direction_changes=timer.command_direction_changes, brake_events_proxy=timer.brake_events, ) + if manager.trace is not None: + result["_trace"] = manager.trace # popped by main() before the JSON is written return result @@ -1168,6 +1178,272 @@ def markdown_report(payload: dict) -> str: return "\n".join(lines) +# --- Figures (`--figures DIR`) ------------------------------------------------------------- +# Style copied from tools/signal_report.py. matplotlib is imported only when figures are written, +# so a default run neither loads it nor changes its output. +INK, INK_2, INK_3, SURFACE, GRID = "#0b0b0b", "#52514e", "#8a8984", "#fcfcfb", "#e4e3df" +# Categorical slots in fixed order. A scheme keeps the same slot in every figure; slots are never cycled. +# There are only three schemes, so no figure needs more than the first three slots. +SLOTS = ("#2a78d6", "#eb6834", "#1baf7a", "#eda100", "#e87ba4", "#008300", "#6250d6") +FIGURE_DPI = 130 +MOVING_MIN_DISPLACEMENT_M = 0.05 # a robot that never leaves this radius of its start is an obstacle +TRAJECTORY_MIN_SPAN_M = 2.0 # the pitch panel never shows less than this in either direction +# (metric key in the aggregates, panel title, number format) +SUMMARY_METRICS = ( + ("sim_time_mean_s", "completion time (s)", "{:.1f}"), + ("path_length_ratio_mean", "path / straight-line length", "{:.2f}"), + ("min_clearance_worst_m", "worst clearance (m)", "{:.2f}"), + ("collision_events_total", "collisions (total)", "{:.0f}"), + ("final_error_max_worst_m", "largest final error (m)", "{:.2f}"), + ("stopped_while_unreached_ratio_mean", "stopped while unreached", "{:.3f}"), + ("controller_mean_ms", "controller mean (ms)", "{:.2f}"), + ("controller_p95_ms_mean", "controller p95 (ms)", "{:.2f}"), + ("max_speed_worst_mps", "max speed (m/s)", "{:.2f}"), +) + + +def _pyplot(): + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + plt.rcParams.update( + { + "figure.facecolor": SURFACE, + "axes.facecolor": SURFACE, + "axes.edgecolor": INK_3, + "axes.labelcolor": INK_2, + "axes.titlesize": 10, + "axes.titlecolor": INK, + "axes.spines.top": False, + "axes.spines.right": False, + "xtick.color": INK_2, + "ytick.color": INK, + "font.size": 9, + } + ) + return plt + + +def _colour(scheme: str) -> str: + return SLOTS[SCHEMES.index(scheme)] + + +def _moving_labels(trace: dict) -> set[str]: + moving = set() + for label, data in trace["robots"].items(): + xy = data["xy"] + if xy and max(math.dist(xy[0], point) for point in xy) > MOVING_MIN_DISPLACEMENT_M: + moving.add(label) + return moving + + +def _friendly_index(label: str) -> int: + return int(label.split(":", 1)[1]) if label.startswith("friendly:") else -1 + + +def _trajectory_bounds(scenario: Scenario, traces: dict[str, dict]) -> tuple[tuple[float, float], tuple[float, float]]: + points = list(scenario.friendly_starts) + list(scenario.friendly_targets) + list(scenario.enemy_starts) + for trace in traces.values(): + for data in trace["robots"].values(): + points.extend(data["xy"]) + xs = [p[0] for p in points] + ys = [p[1] for p in points] + spans = [] + for low, high in ((min(xs), max(xs)), (min(ys), max(ys))): + centre, span = (low + high) / 2.0, max(high - low + 0.8, TRAJECTORY_MIN_SPAN_M) + spans.append((centre - span / 2.0, centre + span / 2.0)) + return spans[0], spans[1] + + +def _trajectory_figure(plt, scenario: Scenario, traces: dict[str, dict], schemes: list[str], path: Path) -> None: + from matplotlib.lines import Line2D + from matplotlib.patches import Circle + + # Sizes in inches from the top: the pitch takes its real aspect ratio, so no panel is left with a gap. + width, pitch_width = 7.5, 6.45 + (x0, x1), (y0, y1) = _trajectory_bounds(scenario, traces) + pitch_height = min(4.4, max(1.8, pitch_width * (y1 - y0) / (x1 - x0))) + panel_height, top_block, gap, bottom_block = 1.5, 1.15, 0.55, 0.6 + height = top_block + pitch_height + 2 * (gap + panel_height) + bottom_block + + def rect(top: float, box_height: float) -> list[float]: + return [0.11, 1.0 - (top + box_height) / height, pitch_width / width, box_height / height] + + fig = plt.figure(figsize=(width, height)) + pitch = fig.add_axes(rect(top_block, pitch_height)) + speed_ax = fig.add_axes(rect(top_block + pitch_height + gap, panel_height)) + clear_ax = fig.add_axes(rect(top_block + pitch_height + 2 * gap + panel_height, panel_height), sharex=speed_ax) + pitch.set_xlim(x0, x1) + pitch.set_ylim(y0, y1) + pitch.set_aspect("equal", adjustable="box") + pitch.set_xlabel("x (m)") + pitch.set_ylabel("y (m)") + pitch.grid(color=GRID, lw=0.8) + pitch.set_axisbelow(True) + + # Stationary robots are obstacles at their real radius. Their positions are the same in every scheme. + first = next(iter(traces.values())) + moving = _moving_labels(first) + for label, data in first["robots"].items(): + if label not in moving: + pitch.add_patch(Circle(data["xy"][0], ROBOT_RADIUS, facecolor=INK_3, alpha=0.35, edgecolor=INK_3, lw=0.8)) + + # Start and target of each of our robots (a moving target is a trajectory, so it is not marked). + for label, data in first["robots"].items(): + index = _friendly_index(label) + if index < 0: + continue + pitch.scatter([data["xy"][0][0]], [data["xy"][0][1]], s=34, facecolor=SURFACE, edgecolor=INK, lw=1.3, zorder=5) + if index < len(scenario.friendly_targets) and index not in scenario.friendly_target_trajectories: + tx, ty = scenario.friendly_targets[index] + pitch.scatter([tx], [ty], s=40, marker="x", color=INK, lw=1.4, zorder=5) + + for scheme in schemes: + colour = _colour(scheme) + trace = traces[scheme] + for label, data in trace["robots"].items(): + xs = [p[0] for p in data["xy"]] + ys = [p[1] for p in data["xy"]] + if _friendly_index(label) >= 0: + pitch.plot(xs, ys, color=colour, lw=1.5, solid_capstyle="round", zorder=3) + elif label in moving: # opponents that move are fainter and dashed + pitch.plot(xs, ys, color=colour, lw=1.2, ls=(0, (3, 2)), alpha=0.55, zorder=2) + else: + continue + pitch.scatter([xs[-1]], [ys[-1]], s=12, color=colour, zorder=4) + + # Speed and clearance over time, one axis each. Speed is the fastest of our robots at each tick. + for scheme in schemes: + colour = _colour(scheme) + trace = traces[scheme] + ours = [label for label in trace["robots"] if _friendly_index(label) >= 0] + speeds = [] + for k in range(len(trace["t"])): + values = [trace["robots"][label]["speed"][k] for label in ours if k < len(trace["robots"][label]["speed"])] + speeds.append(max(values, default=0.0)) + speed_ax.plot(trace["t"], speeds, color=colour, lw=1.2) + pairs = [(t, c) for t, c in zip(trace["t"], trace["clearance"]) if c is not None] + if pairs: + clear_ax.plot([t for t, _ in pairs], [c for _, c in pairs], color=colour, lw=1.2) + speed_ax.axhline(RSIM_PARAMS.MAX_VEL, color=INK_3, lw=0.8, ls="--") + speed_ax.set_ylabel("speed (m/s)") + speed_ax.set_title(f"our fastest robot; dashed: speed limit {RSIM_PARAMS.MAX_VEL:g} m/s", loc="left", fontsize=9) + speed_ax.set_ylim(bottom=0.0) + speed_ax.tick_params(labelbottom=False) + clear_ax.axhline(0.0, color=INK_3, lw=0.8, ls="--") + clear_ax.set_ylabel("clearance (m)") + clear_ax.set_xlabel("time (s)") + clear_ax.set_title("nearest pair of robots, minus both radii; dashed: touching (0 m)", loc="left", fontsize=9) + if all(c is None for trace in traces.values() for c in trace["clearance"]): + clear_ax.text( + 0.5, 0.5, "one robot in this scenario: no obstacles", transform=clear_ax.transAxes, ha="center", va="center" + ) + for ax in (speed_ax, clear_ax): + ax.grid(color=GRID, lw=0.8) + ax.set_axisbelow(True) + + handles = [Line2D([], [], color=_colour(s), lw=1.5, label=s) for s in schemes] + handles += [ + Line2D([], [], color=INK_2, lw=1.5, label="our robot"), + Line2D([], [], color=INK_2, lw=1.2, ls=(0, (3, 2)), alpha=0.55, label="opponent (moving)"), + Line2D([], [], marker="o", ls="", markerfacecolor=INK_3, markeredgecolor=INK_3, alpha=0.5, label="obstacle"), + Line2D([], [], marker="o", ls="", markerfacecolor=SURFACE, markeredgecolor=INK, label="start"), + Line2D([], [], marker="x", ls="", color=INK, label="target"), + ] + fig.legend(handles=handles, loc="upper left", bbox_to_anchor=(0.0, 0.94), ncol=4, frameon=False, fontsize=8) + description = textwrap.fill(scenario.description, 110) + fig.text(0.0, 0.995, f"{scenario.name}: {description}", ha="left", va="top", fontsize=10, color=INK) + fig.savefig(path, dpi=FIGURE_DPI, bbox_inches="tight") + plt.close(fig) + + +def _summary_figure(plt, payload: dict, path: Path) -> None: + from matplotlib.lines import Line2D + + names = list(payload["scenarios"]) + schemes = list(payload["schemes"]) + cells = {(row["scenario"], row["scheme"]): row for row in payload["aggregates"]} + cols = 3 + grows = -(-len(SUMMARY_METRICS) // cols) + fig, axes = plt.subplots( + grows, + cols, + figsize=(10.5, 0.9 + grows * (0.36 * len(names) + 0.9)), + sharey=True, + squeeze=False, + ) + spread = 0.22 + for k, ax in enumerate(axes.flat): + if k >= len(SUMMARY_METRICS): + ax.set_visible(False) + continue + key, title, fmt = SUMMARY_METRICS[k] + for i, scenario in enumerate(names): + for j, scheme in enumerate(schemes): + row = cells.get((scenario, scheme)) + if row is None or row.get(key) is None: + continue + y = i + (j - (len(schemes) - 1) / 2.0) * spread + colour = _colour(scheme) + ax.scatter( + [row[key]], + [y], + s=36, + color=colour, + facecolor=colour if row["passed"] else SURFACE, + lw=1.5, + zorder=3, + ) + ax.set_ylim(len(names) - 0.5, -0.5) + ax.set_yticks(range(len(names))) + ax.set_yticklabels(names) # shared y: hide the labels per panel rather than clearing them + ax.tick_params(axis="y", length=0, labelleft=k % cols == 0) + ax.spines["left"].set_visible(False) + ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda v, _, f=fmt: f.format(v))) + ax.xaxis.set_major_locator(plt.MaxNLocator(4, integer=fmt == "{:.0f}")) + ax.grid(axis="x", color=GRID, lw=0.8) + ax.set_axisbelow(True) + ax.set_title(title, loc="left", fontsize=9) + handles = [Line2D([], [], marker="o", ls="", color=_colour(s), label=s) for s in schemes] + handles.append( + Line2D([], [], marker="o", ls="", markerfacecolor=SURFACE, markeredgecolor=INK_2, label="cell failed (hollow)") + ) + fig.suptitle( + "Motion-planning benchmark: one dot per scheme and scenario, each metric on its own scale", + x=0.0, + ha="left", + fontsize=10, + color=INK, + ) + fig.legend(handles=handles, loc="lower center", ncol=len(handles), frameon=False, fontsize=8) + fig.tight_layout(rect=(0, 0.04, 1, 0.97)) + fig.savefig(path, dpi=FIGURE_DPI, bbox_inches="tight") + plt.close(fig) + + +def write_figures(payload: dict, traces: dict[tuple[str, str], dict], out_dir: Path, scenarios: dict) -> list[Path]: + """Writes `trajectories_.png` per scenario and `summary.png` to `out_dir`. + + `traces` maps (scenario, scheme) to the per-tick record of one run (see `BenchmarkManager`). + A scenario with no recorded traces is skipped.""" + plt = _pyplot() + out_dir.mkdir(parents=True, exist_ok=True) + written = [] + for name in payload["scenarios"]: + scheme_traces = {s: traces[(name, s)] for s in payload["schemes"] if (name, s) in traces} + if not scheme_traces: + continue + path = out_dir / f"trajectories_{name}.png" + _trajectory_figure(plt, scenarios[name], scheme_traces, list(scheme_traces), path) + written.append(path) + summary = out_dir / "summary.png" + _summary_figure(plt, payload, summary) + written.append(summary) + return written + + def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument("--schemes", nargs="+", choices=SCHEMES, default=list(SCHEMES)) @@ -1186,6 +1462,14 @@ def parse_args() -> argparse.Namespace: help="Controller calls excluded from latency metrics in each cell (default: 5).", ) parser.add_argument("--list-scenarios", action="store_true", help="Print scenario descriptions and exit.") + parser.add_argument( + "--figures", + type=Path, + default=None, + metavar="DIR", + help="Also write trajectories_.png and summary.png to DIR. Off by default; turning it on " + "records per-tick robot state during the runs. Figures show the first repeat of each cell.", + ) parser.add_argument( "--allow-failures", action="store_true", @@ -1207,6 +1491,7 @@ def main() -> int: return 0 results = [] + traces: dict[tuple[str, str], dict] = {} total = len(args.schemes) * len(args.scenarios) * args.repeats case_number = 0 for scenario_name in args.scenarios: @@ -1214,7 +1499,16 @@ def main() -> int: for repeat in range(1, args.repeats + 1): case_number += 1 print(f"[{case_number}/{total}] {scenario_name} / {scheme} / repeat {repeat}", flush=True) - result = run_case(scheme, SCENARIOS[scenario_name], repeat, args.timing_warmup_calls) + result = run_case( + scheme, + SCENARIOS[scenario_name], + repeat, + args.timing_warmup_calls, + record_trace=args.figures is not None, + ) + trace = result.pop("_trace", None) + if trace is not None: + traces.setdefault((scenario_name, scheme), trace) results.append(result) print( f" {'PASS' if result['passed'] else 'FAIL'} ({result['termination_reason']}), " @@ -1226,7 +1520,7 @@ def main() -> int: payload = { "schema_version": SCHEMA_VERSION, "generated_at_utc": generated_at.isoformat(), - "git_revision": _git_revision(), + "git_revision": git_revision(), "mode": "rsim", "headless": True, "schemes": list(args.schemes), @@ -1252,6 +1546,9 @@ def main() -> int: markdown_path.write_text(markdown_report(payload), encoding="utf-8") print(f"\nJSON: {json_path}") print(f"Markdown: {markdown_path}") + if args.figures is not None: + for figure_path in write_figures(payload, traces, args.figures, SCENARIOS): + print(f"Figure: {figure_path}") any_failures = any(not row["passed"] for row in payload["aggregates"]) return 0 if args.allow_failures or not any_failures else 1 diff --git a/tools/package_graph.py b/tools/package_graph.py new file mode 100644 index 00000000..15e519b7 --- /dev/null +++ b/tools/package_graph.py @@ -0,0 +1,123 @@ +"""Package dependency graph of `utama_core/`, as a Mermaid flowchart, and the stated import rules it breaks. + + pixi run python tools/package_graph.py + +Parses every `.py` under `utama_core/` with `ast` (nothing is imported) and counts imports between +first-level packages (`engine`, `replay`, ...). Test code is not scanned as a source, but an import +of `utama_core.tests` from other code is an edge, to the `tests` package. Imports inside functions +and `TYPE_CHECKING` blocks count too. Self-edges are skipped, and packages with no edges are left +out of the graph. Prints: + +- a Mermaid `flowchart LR` with one edge per package pair, labelled by its number of imports; +- the violations of `RULES`, each with its file and line, or `none`. + +The output is pasted into `docs/architecture.md`, section "Package dependencies". +""" + +from __future__ import annotations + +import ast +from collections import Counter +from dataclasses import dataclass +from pathlib import Path + +ROOT = Path(__file__).resolve().parent.parent +PACKAGE = "utama_core" +TESTS = "tests" + +# (importer, imported): a package that must never import another. +RULES: tuple[tuple[str, str], ...] = (("replay", "analysis"),) + + +@dataclass(frozen=True) +class Import: + src: str # importing first-level package + dst: str # imported first-level package + file: str # path relative to the repository root + line: int + + +def _absolute_target(node: ast.ImportFrom, module_parts: list[str]) -> list[str]: + """Dotted parts a `from ... import` names, with relative levels resolved. + + `module_parts` is the importing file's path below `utama_core/`, without `.py`, so `a/b.py` gives + [a, b]. Its package is everything but the last part (`a/__init__.py` gives [a, __init__]: same rule). + """ + if node.level == 0: + return (node.module or "").split(".") if node.module else [] + anchor = [PACKAGE] + module_parts[:-1] + anchor = anchor[: len(anchor) - (node.level - 1)] + return anchor + (node.module.split(".") if node.module else []) + + +def _imports_in(tree: ast.AST, module_parts: list[str], packages: set[str]) -> list[tuple[str, int]]: + """(imported first-level package, line) for each import of `utama_core` code in one module.""" + found: list[tuple[str, int]] = [] + for node in ast.walk(tree): + if isinstance(node, ast.Import): + for alias in node.names: + parts = alias.name.split(".") + if parts[0] == PACKAGE and len(parts) > 1: + found.append((parts[1], node.lineno)) + elif isinstance(node, ast.ImportFrom): + target = _absolute_target(node, module_parts) + if target and target[0] == PACKAGE and len(target) >= 2: + found.append((target[1], node.lineno)) + elif target == [PACKAGE]: + # `from utama_core import engine, replay`: each name is a package. + found.extend((alias.name, node.lineno) for alias in node.names if alias.name in packages) + return found + + +def scan(root: Path = ROOT) -> list[Import]: + """Every import between first-level packages under `root / utama_core`, skipping `tests/` sources.""" + base = root / PACKAGE + packages = {p.name for p in base.iterdir() if p.is_dir() and p.name != "__pycache__"} + found: list[Import] = [] + for path in sorted(base.rglob("*.py")): + rel = path.relative_to(base) + # Top-level modules (utama_core/__init__.py) belong to no package; tests are not sources. + if len(rel.parts) < 2 or rel.parts[0] == TESTS: + continue + module_parts = list(rel.with_suffix("").parts) + src = module_parts[0] + tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path)) + for dst, line in _imports_in(tree, module_parts, packages): + if dst != src: + found.append(Import(src, dst, f"{PACKAGE}/{rel.as_posix()}", line)) + return found + + +def edge_counts(imports: list[Import]) -> Counter[tuple[str, str]]: + return Counter((i.src, i.dst) for i in imports) + + +def violations(imports: list[Import], rules: tuple[tuple[str, str], ...] = RULES) -> list[Import]: + banned = set(rules) + return [i for i in imports if (i.src, i.dst) in banned] + + +def render_mermaid(counts: Counter[tuple[str, str]]) -> str: + """A `flowchart LR` with one labelled edge per package pair, sorted for a stable diff.""" + lines = ["flowchart LR"] + for (src, dst), n in sorted(counts.items()): + lines.append(f' p_{src}["{src}"] -->|{n}| p_{dst}["{dst}"]') + return "\n".join(lines) + + +def main() -> None: + imports = scan() + print("```mermaid") + print(render_mermaid(edge_counts(imports))) + print("```") + print() + bad = violations(imports) + print("Violations of the import rules (`replay/` never imports `analysis/`):") + if not bad: + print("none") + for i in bad: + print(f"- {i.src} -> {i.dst}: {i.file}:{i.line}") + + +if __name__ == "__main__": + main() diff --git a/tools/pitch_maps.py b/tools/pitch_maps.py new file mode 100644 index 00000000..683cfd4e --- /dev/null +++ b/tools/pitch_maps.py @@ -0,0 +1,511 @@ +"""On-pitch pictures of how each strategy plays, from one round-robin run. + + pixi run python tools/pitch_maps.py replays/tournament_ [--out docs/img/signals] + +Every strategy gets one panel per figure, in `signal_report`'s order (points per match). All +panels use one frame: the strategy attacks toward +x, its own goal is on the left (-x), and +the pitch is rotated 180 degrees for a team that attacks toward -x, so its left wing is +y in +every panel. Writes: + +- `positions_attack.png`: where the strategy's outfield robots stand while it has the ball + (share of live samples per cell, one scale for all panels); five dots mark each robot's mean; +- `positions_defence.png`: the same while the opponent has the ball; +- `shots.png`: every shot the strategy took (filled if it scored, hollow if not) at the spot it + was struck from, and the shots it conceded (AGAINST colour, faded) at the other end; +- `fouls_map.png`: where the strategy committed its fouls, coloured by rule: the four most + common rules in the run, then all other rules. + +Possession is `MatchStatsAccumulator`'s notion without its hysteresis: the nearest robot to the +ball, within `_POSSESSION_RADIUS_M`, while the ball is slower than `_PASS_RELEASE_MPS`. Positions +are sampled every `STEP` ticks in live play (NORMAL_START, FORCE_START); the goalkeeper is the +robot that spends the most time in its own box, and is left out. Shots come from +`turnover_breakdown.analyse_match` (the `chances` record), so each match is replayed once. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from collections import Counter +from concurrent.futures import ProcessPoolExecutor +from dataclasses import dataclass, field +from pathlib import Path +from typing import Optional + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +from matplotlib.colors import LinearSegmentedColormap # noqa: E402 +from matplotlib.patches import Circle, Rectangle # noqa: E402 + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from tools import signal_report as sr # noqa: E402 +from utama_core.analysis import turnover_breakdown # noqa: E402 +from utama_core.analysis.findings import Finding, write_jsonl # noqa: E402 +from utama_core.config.field_params import STANDARD_FIELD_DIMS as F # noqa: E402 +from utama_core.engine.match_stats import ( # noqa: E402 + _PASS_RELEASE_MPS, + _POSSESSION_RADIUS_M, +) +from utama_core.entities.referee.referee_command import RefereeCommand # noqa: E402 +from utama_core.replay.columnar_reader import ( # noqa: E402 + ColumnarReplay, + load_columnar_replay, +) + +L, W = F.full_field_half_length, F.full_field_half_width +BOX_D, BOX_W = 2 * F.half_defense_area_depth, F.half_defense_area_width # the 2 m x 1 m box, from the goal line +GOAL_D = F.goal_depth +BIN_M = 0.25 # occupancy cell size +STEP = 10 # sample every 10th tick +LIVE_IDS = np.array([RefereeCommand.NORMAL_START.value, RefereeCommand.FORCE_START.value]) +BALL_OUT = "out_of_bounds" +TOP_RULES = 4 +OTHER_SLOT = 4 # sr.SLOTS[4] for "other": the top four rules take slots 0-3 + +SHARE_CMAP = LinearSegmentedColormap.from_list("share", [sr.SURFACE, "#cde2fb", "#6da7ec", "#256abf", "#0d366b"]) + + +@dataclass +class MatchView: + """One strategy's view of one match, with every coordinate in the common frame.""" + + strategy: str + pos_x: np.ndarray # outfield robot samples in live play + pos_y: np.ndarray + pos_in: np.ndarray # bool: the strategy had the ball at the sample + pos_robot: np.ndarray # robot id of each sample + shots: list[dict] = field(default_factory=list) # {"x", "y", "scored", "own"} + fouls: list[dict] = field(default_factory=list) # {"x", "y", "rule"} + + +# --- frame and possession ------------------------------------------------------------------- + + +def attack_sign(defends_right: bool) -> float: + """+1 for a team that attacks toward +x (it defends the left goal), -1 for one that attacks + toward -x. Rotating the pitch by 180 degrees for the -1 case keeps the team's left on the + same side of every panel.""" + return -1.0 if defends_right else 1.0 + + +def to_attacking_frame(x: float, y: float, defends_right: bool) -> tuple[float, float]: + s = attack_sign(defends_right) + return s * x, s * y + + +def _nearest(players: np.ndarray, ball_xy: np.ndarray) -> np.ndarray: + """Distance from the ball to the nearest present robot at each tick (inf if none).""" + if players.shape[1] == 0: + return np.full(len(ball_xy), np.inf) + d = np.linalg.norm(players - ball_xy[:, None, :], axis=2) + return np.nan_to_num(d, nan=np.inf).min(axis=1) + + +def possession_codes( + friendly_p: np.ndarray, enemy_p: np.ndarray, ball_p: np.ndarray, ball_v: np.ndarray, live: np.ndarray +) -> np.ndarray: + """Per tick: 1 if the friendly team has the ball, -1 the enemy, 0 nobody (or not live).""" + ball_xy = ball_p[:, :2] + d_f = _nearest(friendly_p, ball_xy) + d_e = _nearest(enemy_p, ball_xy) + speed = np.linalg.norm(ball_v[:, :2], axis=1) + controlled = live & (np.minimum(d_f, d_e) <= _POSSESSION_RADIUS_M) & (speed < _PASS_RELEASE_MPS) + return np.where(controlled, np.where(d_f < d_e, 1, -1), 0).astype(np.int8) + + +def keeper_slot(norm_p: np.ndarray) -> int: + """Index of the robot that spends the most ticks in its own box. `norm_p` is (ticks, robots, 2) + in the common frame, where the team's own goal is at -x.""" + if norm_p.shape[1] == 0: + return -1 + in_box = (norm_p[:, :, 0] < -L + BOX_D) & (np.abs(norm_p[:, :, 1]) < BOX_W) + return int(in_box.sum(axis=0).argmax()) + + +def _tick(ts: np.ndarray, t: float) -> int: + return min(int(np.searchsorted(ts, t)), len(ts) - 1) + + +def build_views(replay: ColumnarReplay, names: dict[str, str], shots: list[dict], fouls: list[dict]) -> list[MatchView]: + """The two strategy views of one replay. `names` maps "friendly"/"enemy" to the strategy + (config_a / config_b); `shots` are `chances` shot records and `fouls` are the `stats.fouls` + records of the match, both in its own (friendly/enemy) terms.""" + n = replay.n_ticks + live = np.isin(replay.referee_command, LIVE_IDS) + defends_right = ( + replay.side_is_right.astype(bool) if replay.side_is_right is not None else np.full(n, replay.my_team_is_right) + ) + sign = {"friendly": np.where(defends_right, -1.0, 1.0), "enemy": np.where(defends_right, 1.0, -1.0)} + poss = possession_codes(replay.friendly_p, replay.enemy_p, replay.ball_p, replay.ball_v, live) + idx = np.arange(0, n, STEP) + views = [] + for side, opp in (("friendly", "enemy"), ("enemy", "friendly")): + players, ids = ( + (replay.friendly_p, replay.friendly_ids) if side == "friendly" else (replay.enemy_p, replay.enemy_ids) + ) + norm = players[idx] * sign[side][idx][:, None, None] + keeper = keeper_slot(norm) + code = 1 if side == "friendly" else -1 + xs, ys, ins, rids = [], [], [], [] + for j, rid in enumerate(ids): + if j == keeper: + continue + ok = live[idx] & ~np.isnan(norm[:, j, 0]) + xs.append(norm[ok, j, 0]) + ys.append(norm[ok, j, 1]) + ins.append(poss[idx][ok] == code) + rids.append(np.full(int(ok.sum()), int(rid))) + view = MatchView( + strategy=names[side], + pos_x=np.concatenate(xs) if xs else np.empty(0), + pos_y=np.concatenate(ys) if ys else np.empty(0), + pos_in=np.concatenate(ins) if ins else np.empty(0, dtype=bool), + pos_robot=np.concatenate(rids) if rids else np.empty(0, dtype=int), + ) + for s in shots: + if s["side"] not in (side, opp): + continue + tick = _tick(replay.ts, s["t"]) + x, y = s["xy"][0] * sign[side][tick], s["xy"][1] * sign[side][tick] + view.shots.append({"x": float(x), "y": float(y), "scored": bool(s["scored"]), "own": s["side"] == side}) + for f in fouls: + if f["side"] != side or f["rule"] == BALL_OUT: + continue + tick = _tick(replay.ts, f["sim_time"]) + x, y = f["ball_xy"][0] * sign[side][tick], f["ball_xy"][1] * sign[side][tick] + view.fouls.append({"x": float(x), "y": float(y), "rule": f["rule"]}) + views.append(view) + return views + + +# --- aggregation ----------------------------------------------------------------------------- + + +def _cat(views: list[MatchView], strategy: str, attr: str) -> np.ndarray: + parts = [getattr(v, attr) for v in views if v.strategy == strategy] + return np.concatenate(parts) if parts else np.empty(0) + + +def position_grid(views: list[MatchView], strategy: str, in_possession: bool) -> dict: + """Share of the strategy's outfield samples per cell (rows: y, columns: x), and each robot's + mean position, for the samples taken while it had the ball (or not).""" + x, y = _cat(views, strategy, "pos_x"), _cat(views, strategy, "pos_y") + inp = _cat(views, strategy, "pos_in").astype(bool) + rid = _cat(views, strategy, "pos_robot") + keep = inp if in_possession else ~inp + x, y, rid = x[keep], y[keep], rid[keep] + xe = np.linspace(-L, L, round(2 * L / BIN_M) + 1) + ye = np.linspace(-W, W, round(2 * W / BIN_M) + 1) + hist, _, _ = np.histogram2d(x, y, bins=[xe, ye]) + share = hist.T / max(1.0, hist.sum()) + means = [(float(x[rid == r].mean()), float(y[rid == r].mean())) for r in np.unique(rid)] + return {"share": share, "means": means} + + +def shot_marks(views: list[MatchView], strategy: str) -> dict: + """The strategy's own shots and the shots it conceded, as lists of (x, y, scored).""" + mine = [s for v in views if v.strategy == strategy for s in v.shots] + return { + "own": [(s["x"], s["y"], s["scored"]) for s in mine if s["own"]], + "faced": [(s["x"], s["y"], s["scored"]) for s in mine if not s["own"]], + } + + +def top_rules(views: list[MatchView], k: int = TOP_RULES) -> list[str]: + counts = Counter(f["rule"] for v in views for f in v.fouls) + return [rule for rule, _ in counts.most_common(k)] + + +def rule_colours(rules: list[str]) -> dict[str, str]: + """Fixed slots: the i-th top rule takes slot i, every other rule takes the 'other' slot.""" + return {rule: sr.SLOTS[i] for i, rule in enumerate(rules)} + + +# --- drawing --------------------------------------------------------------------------------- + + +def _pitch(ax) -> None: + ax.set_facecolor(sr.SURFACE) + ax.add_patch(Rectangle((-L, -W), 2 * L, 2 * W, fill=False, edgecolor=sr.INK_3, lw=0.8)) + ax.plot([0, 0], [-W, W], color=sr.INK_3, lw=0.6) + ax.add_patch(Circle((0, 0), F.center_circle_radius, fill=False, edgecolor=sr.INK_3, lw=0.6)) + for x0 in (-L, L - BOX_D): + ax.add_patch(Rectangle((x0, -BOX_W), BOX_D, 2 * BOX_W, fill=False, edgecolor=sr.INK_3, lw=0.6)) + ax.add_patch(Rectangle((-L - GOAL_D, -F.half_goal_width), GOAL_D, 2 * F.half_goal_width, color=sr.INK_3)) + ax.add_patch(Rectangle((L, -F.half_goal_width), GOAL_D, 2 * F.half_goal_width, color=sr.INK_3)) + ax.set_xlim(-L - GOAL_D - 0.2, L + GOAL_D + 0.2) + ax.set_ylim(-W - 0.2, W + 0.2) + ax.set_aspect("equal") + ax.axis("off") + + +def _panel_grid(n: int, cols: int = 3): + """A grid of pitch panels for `n` strategies; unused cells are hidden at the end of drawing.""" + cols = min(cols, n) # two strategies get one row of two panels, not a row with a hole + rows = -(-n // cols) + return plt.subplots(rows, cols, figsize=(3.6 * cols, 2.55 * rows + 0.8), squeeze=False) + + +def _hide_unused(axes, n: int) -> None: + for ax in list(axes.flat)[n:]: + ax.set_visible(False) + + +def plot_positions(views, order, labels, path: Path, in_possession: bool) -> None: + grids = {n: position_grid(views, n, in_possession) for n in order} + nonzero = np.concatenate([g["share"][g["share"] > 0] for g in grids.values() if (g["share"] > 0).any()]) + vmax = float(np.percentile(nonzero, 99)) if nonzero.size else 1.0 + fig, axes = _panel_grid(len(order)) + for k, name in enumerate(order): + ax = axes.flat[k] + _pitch(ax) + ax.imshow( + grids[name]["share"], + extent=[-L, L, -W, W], + origin="lower", + cmap=SHARE_CMAP, + vmin=0, + vmax=vmax, + interpolation="bilinear", + zorder=1, + ) + if grids[name]["means"]: + mx, my = zip(*grids[name]["means"]) + ax.scatter(mx, my, s=26, color=sr.INK, edgecolors=sr.SURFACE, linewidths=0.8, zorder=3) + ax.set_title(labels[name], loc="left", fontsize=9, color=sr.INK) + _hide_unused(axes, len(order)) + state = "with the ball" if in_possession else "without the ball" + fig.suptitle( + f"Where the outfield robots stand {state}: share of live samples per cell, one scale. " + "Every strategy attacks right; dots are each robot's mean position", + x=0.0, + ha="left", + fontsize=10, + color=sr.INK, + ) + sm = plt.cm.ScalarMappable(cmap=SHARE_CMAP, norm=plt.Normalize(0, vmax)) + fig.colorbar(sm, ax=axes.ravel().tolist(), shrink=0.6, pad=0.02, label="share of the samples in a cell") + fig.savefig(path, dpi=110, bbox_inches="tight") + plt.close(fig) + + +def plot_shots(views, order, labels, path: Path) -> None: + fig, axes = _panel_grid(len(order)) + for k, name in enumerate(order): + ax = axes.flat[k] + _pitch(ax) + marks = shot_marks(views, name) + for pts, colour, alpha in ((marks["faced"], sr.AGAINST, 0.35), (marks["own"], sr.FOR, 1.0)): + for x, y, scored in pts: + ax.scatter( + [x], + [y], + s=16, + color=colour if scored else sr.SURFACE, + edgecolors=colour, + linewidths=0.9, + alpha=alpha, + zorder=3 if colour == sr.FOR else 2, + ) + goals = sum(s for _, _, s in marks["own"]) + ax.set_title( + f"{labels[name]}\n{len(marks['own'])} shots, {goals} goals", loc="left", fontsize=8.5, color=sr.INK + ) + _hide_unused(axes, len(order)) + handles = [ + plt.Line2D([], [], marker="o", ls="", mfc=sr.FOR, mec=sr.FOR, ms=5, label="own shot, goal"), + plt.Line2D([], [], marker="o", ls="", mfc=sr.SURFACE, mec=sr.FOR, ms=5, label="own shot, no goal"), + plt.Line2D([], [], marker="o", ls="", mfc=sr.AGAINST, mec=sr.AGAINST, ms=5, alpha=0.35, label="conceded, goal"), + plt.Line2D( + [], [], marker="o", ls="", mfc=sr.SURFACE, mec=sr.AGAINST, ms=5, alpha=0.35, label="conceded, no goal" + ), + ] + fig.legend(handles=handles, loc="upper left", ncol=4, frameon=False, bbox_to_anchor=(0.0, 1.0)) + fig.suptitle( + "Where each strategy shoots from, and where it concedes: shots at the spot struck from, " + "attacking right; conceded shots faded", + x=0.0, + ha="left", + y=1.04, + fontsize=10, + color=sr.INK, + ) + fig.tight_layout() + fig.savefig(path, dpi=110, bbox_inches="tight") + plt.close(fig) + + +def plot_fouls(views, order, labels, path: Path) -> None: + rules = top_rules(views) + colours = rule_colours(rules) + other = sr.SLOTS[OTHER_SLOT] + fig, axes = _panel_grid(len(order)) + for k, name in enumerate(order): + ax = axes.flat[k] + _pitch(ax) + fouls = [f for v in views if v.strategy == name for f in v.fouls] + for rule in rules + [None]: + pts = [f for f in fouls if (f["rule"] == rule if rule else f["rule"] not in colours)] + if pts: + ax.scatter( + [f["x"] for f in pts], + [f["y"] for f in pts], + s=18, + color=colours[rule] if rule else other, + edgecolors=sr.SURFACE, + linewidths=0.4, + zorder=3, + ) + ax.set_title(f"{labels[name]}\n{len(fouls)} fouls", loc="left", fontsize=8.5, color=sr.INK) + _hide_unused(axes, len(order)) + handles = [ + plt.Line2D([], [], marker="o", ls="", mfc=colours[r], mec=sr.SURFACE, ms=6, label=r.replace("_", " ")) + for r in rules + ] + handles.append(plt.Line2D([], [], marker="o", ls="", mfc=other, mec=sr.SURFACE, ms=6, label="other rules")) + fig.legend(handles=handles, loc="upper left", ncol=len(handles), frameon=False, bbox_to_anchor=(0.0, 1.0)) + fig.suptitle( + "Where each strategy commits its fouls, by rule (attacking right; the ball's spot at the foul)", + x=0.0, + ha="left", + y=1.04, + fontsize=10, + color=sr.INK, + ) + fig.tight_layout() + fig.savefig(path, dpi=110, bbox_inches="tight") + plt.close(fig) + + +def zone_findings(views: list[MatchView], order: list[str], images: Optional[dict[str, str]] = None) -> list[Finding]: + """Six descriptive `zone_share` findings per strategy: the share of its in-possession and of its + out-of-possession outfield samples in each third (docs/pitch_zones.md). No threshold: they describe.""" + third = L / 3 # the thirds' boundaries in the common frame, at x = +-1.5 m + out: list[Finding] = [] + for name in order: + x = _cat(views, name, "pos_x") + inp = _cat(views, name, "pos_in").astype(bool) + for label, mask in (("in possession", inp), ("out of possession", ~inp)): + xs = x[mask] + if xs.size == 0: + continue + zones = ( + ("defensive third", xs < -third), + ("middle third", np.abs(xs) <= third), + ("attacking third", xs > third), + ) + for zone, inside in zones: + share = float(np.mean(inside)) + out.append( + Finding( + tool="pitch_maps", + kind="zone_share", + replay=None, + side=None, + t0=None, + t1=None, + robots=(), + value=share, + threshold=None, + unit="share of samples", + message=( + f"{sr.short(name)} had {share:.0%} of its {label} samples in the {zone} " + f"({xs.size} samples)." + ), + image=(images or {}).get(label), + ) + ) + return out + + +def summary_rows(views: list[MatchView], order: list[str]) -> list[dict]: + """The numbers the figures show, one row per strategy in `order`: the share of its in-possession + samples in the attacking half, the share of its out-of-possession samples in its own half, the + share of its shots struck from the attacking third and of its fouls in its defensive third.""" + third = -L + 2 * L / 3 # the defensive third's far edge, in the common frame + rows = [] + for name in order: + x, y = _cat(views, name, "pos_x"), _cat(views, name, "pos_y") + inp = _cat(views, name, "pos_in").astype(bool) + shots = shot_marks(views, name)["own"] + fouls = [f for v in views if v.strategy == name for f in v.fouls] + rows.append( + { + "strategy": sr.short(name), + "in_possession_attacking_half": float(np.mean(x[inp] > 0)) if inp.any() else None, + "in_possession_mean_abs_y": float(np.mean(np.abs(y[inp]))) if inp.any() else None, + "out_possession_own_half": float(np.mean(x[~inp] < 0)) if (~inp).any() else None, + "shots_from_attacking_third": float(np.mean([sx > -third for sx, _, _ in shots])) if shots else None, + "fouls_in_defensive_third": float(np.mean([f["x"] < third for f in fouls])) if fouls else None, + } + ) + return rows + + +# --- run ------------------------------------------------------------------------------------ + + +def _match(job: tuple[str, str, str, list[dict]]) -> list[MatchView]: + npz, config_a, config_b, fouls = job + names = {"friendly": config_a, "enemy": config_b} + shots = turnover_breakdown.analyse_match(npz)["chances"]["shots"] + return build_views(load_columnar_replay(npz), names, shots, fouls) + + +def views_for_matches(jobs: list[tuple[str, str, str, list[dict]]], workers: int) -> list[MatchView]: + """The views of any list of matches, one job per match: (replay .npz path, config_a, config_b, + the match's `stats.fouls` records). `run_views` and `explain.py` both build their jobs here.""" + with ProcessPoolExecutor(max_workers=workers) as pool: + return [v for views in pool.map(_match, jobs) for v in views] + + +def run_views(run_dir: Path, workers: int) -> list[MatchView]: + summary = json.loads((run_dir / "summary.json").read_text()) + jobs = [] + for r in summary["results"]: + stem = f"{sr.short(r['config_a'])}_vs_{sr.short(r['config_b'])}" + npz = run_dir / f"{stem}.npz" + if npz.exists(): + jobs.append((str(npz), r["config_a"], r["config_b"], (r.get("stats") or {}).get("fouls") or [])) + return views_for_matches(jobs, workers) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("run_dir", type=Path) + parser.add_argument("--out", type=Path, default=REPO_ROOT / "docs" / "img" / "signals") + parser.add_argument("--workers", type=int, default=2) + parser.add_argument( + "--findings", metavar="PATH", help="also write the zone shares per strategy as JSON lines (- for stdout)" + ) + args = parser.parse_args() + summary = json.loads((args.run_dir / "summary.json").read_text()) + order = sr._order(summary["strategies"]) + labels = {n: f"{sr.short(n)} {sr.points(summary['strategies'][n]):.2f}" for n in order} + views = run_views(args.run_dir, args.workers) + args.out.mkdir(parents=True, exist_ok=True) + attack_png = args.out / "positions_attack.png" + defence_png = args.out / "positions_defence.png" + plot_positions(views, order, labels, attack_png, in_possession=True) + plot_positions(views, order, labels, defence_png, in_possession=False) + plot_shots(views, order, labels, args.out / "shots.png") + plot_fouls(views, order, labels, args.out / "fouls_map.png") + # With `--findings -` the JSON owns stdout, so the text goes to stderr. + human = sys.stderr if args.findings == "-" else sys.stdout + for row in summary_rows(views, order): + print(json.dumps({k: (round(v, 3) if isinstance(v, float) else v) for k, v in row.items()}), file=human) + print(f"{len(views)} strategy-match views; figures in {args.out}", file=human) + if args.findings is not None: + images = {"in possession": str(attack_png), "out of possession": str(defence_png)} + write_jsonl(zone_findings(views, order, images), args.findings) + + +if __name__ == "__main__": + main() diff --git a/tools/plan_snapshot.py b/tools/plan_snapshot.py new file mode 100644 index 00000000..5efcf5d1 --- /dev/null +++ b/tools/plan_snapshot.py @@ -0,0 +1,100 @@ +"""Picture of what the fast-path planner decided for one robot on one tick. + + pixi run python tools/plan_snapshot.py REPLAY.npz --t 260 --robot 4 --target -1.0 -0.3 --side enemy + +Rebuilds the field state at `--t` from the replay (`utama_core.replay.scenario`), runs the planner's +`_path_to` for the robot toward `--target`, and writes a PNG: the pitch around the robot, every robot +at its real radius with its id, the ball and its keep-out circle while the planner keeps out of it, +each robot's clearance ring, the target, the carrot the planner returned, and a text box with the +referee command, distances and whether the robot is inside the keep-out circle. Read-only; the +planner is not changed. `--side enemy` plans the other strategy's robot (the replay's enemy). With +no `--target`, the robot's own position 0.5 s later stands in for it, and the text box says so. + +Use it when a robot does something odd near an obstacle or a keep-out zone (see +docs/STRATEGY_DEVELOPMENT.md, "Debugging a foul or failure"). The picture is one tick: pair it with +`tools/replay_trace.py` for the timeline. +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from utama_core.analysis.plan_snapshot import ( # noqa: E402 + game_from_scenario, + plan_snapshot, + scenario_robot_position, + title_for, +) +from utama_core.replay.scenario import scenario_from_replay # noqa: E402 + +STAND_IN_DELAY_S = 0.5 + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("replay", type=Path, help="a replay .npz (or .pkl) from a run") + parser.add_argument("--t", type=float, required=True, help="time in seconds from the replay's first frame") + parser.add_argument("--robot", type=int, required=True, help="robot id on the planned side") + parser.add_argument( + "--target", + type=float, + nargs=2, + metavar=("X", "Y"), + help="target in the replay's pitch coordinates (metres); default: the robot's position 0.5 s later", + ) + parser.add_argument( + "--side", + choices=["friendly", "enemy"], + default="friendly", + help="which strategy's robot to plan: friendly is the replay's config_a (default), enemy its config_b", + ) + parser.add_argument( + "--out", type=Path, help="PNG path (default: replays/plan_snapshot/_t_.png)" + ) + parser.add_argument("--half-width", type=float, default=1.5, help="window half-width around the robot, metres") + parser.add_argument("--full-pitch", action="store_true", help="draw the whole pitch instead of the window") + args = parser.parse_args() + + scenario = scenario_from_replay(args.replay, args.t) + game = game_from_scenario(scenario, args.side) + planned = scenario.friendly_robots if args.side == "friendly" else scenario.enemy_robots + if not any(s.id == args.robot for s in planned): + ids = sorted(s.id for s in planned) + parser.error(f"no {args.side} robot {args.robot} at t={args.t} s (ids: {ids})") + + note = None + if args.target is not None: + target = tuple(args.target) + else: + target = scenario_robot_position(args.replay, args.t + STAND_IN_DELAY_S, args.side, args.robot) + note = f"target: stand-in, robot at t+{STAND_IN_DELAY_S:g} s" + + out = args.out or ( + REPO_ROOT / "replays" / "plan_snapshot" / f"{args.replay.stem}_t{args.t:g}_{args.side}{args.robot}.png" + ) + snap = plan_snapshot( + game, + args.robot, + target, + out, + half_width=args.half_width, + full_pitch=args.full_pitch, + title=title_for(scenario, args.side, args.robot), + target_note=note, + ) + + print(f"wrote {snap.path}") + print(f"carrot: ({snap.carrot[0]:.3f}, {snap.carrot[1]:.3f}) robot -> carrot {snap.robot_to_carrot:.3f} m") + if snap.robot_to_ball is not None: + print(f"robot -> ball {snap.robot_to_ball:.3f} m inside keep-out: {snap.inside_keep_out}") + print(f"keep-out active: {snap.keeps_off_ball} referee: {scenario.referee_command}") + + +if __name__ == "__main__": + main() diff --git a/tools/plan_trace.py b/tools/plan_trace.py new file mode 100644 index 00000000..13540669 --- /dev/null +++ b/tools/plan_trace.py @@ -0,0 +1,130 @@ +"""The fast-path planner over a time window: one robot's plan, tick by tick. + + pixi run python tools/plan_trace.py REPLAY.npz --t0 250 --t1 262 --robot 4 --side enemy --target -1.0 -0.3 + +For each sampled time in [t0, t1] (every `--step` s) it rebuilds the field state from the replay +(`utama_core.replay.scenario`), runs the planner's `_path_to` for the robot, and records the robot's +position, the carrot returned, the referee command, the distance to the ball, the clearance and the +speed. It writes one PNG: a pitch panel with the robot's path coloured by time and each sample's +carrot joined to the robot, above time panels for robot-to-ball distance (with the keep-out +distance), robot-to-carrot length, speed and the referee command. Read-only; the planner is not +changed. Without `--target` each tick's target is the robot's position `--lookahead` s later, the +stand-in `tools/plan_snapshot.py` uses; the figure says so. `--json` prints the per-sample rows. + +Use it when a robot's behaviour near an obstacle or keep-out zone unfolds over time, where one +`plan_snapshot` tick cannot show it (see docs/STRATEGY_DEVELOPMENT.md, "Debugging a foul or failure"). +""" + +from __future__ import annotations + +import argparse +import dataclasses +import json +import os +import sys +from pathlib import Path + +os.environ.setdefault("PYGAME_HIDE_SUPPORT_PROMPT", "1") # keep pygame's banner off stdout (--findings -) + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from utama_core.analysis.findings import write_jsonl # noqa: E402 +from utama_core.analysis.plan_snapshot import game_from_scenario # noqa: E402 +from utama_core.analysis.plan_trace import plan_findings, plan_trace # noqa: E402 +from utama_core.replay.scenario import scenario_from_replay # noqa: E402 + + +def replay_game_at(replay: Path, side: str): + """`game_at(t)` for `plan_trace`: the `Game` the planned side sees in the replay at `t` seconds.""" + + def game_at(t: float): + return game_from_scenario(scenario_from_replay(replay, t), side) + + return game_at + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("replay", type=Path, help="a replay .npz (or .pkl) from a run") + parser.add_argument("--t0", type=float, required=True, help="window start, seconds from the replay's first frame") + parser.add_argument("--t1", type=float, required=True, help="window end, seconds from the replay's first frame") + parser.add_argument("--robot", type=int, required=True, help="robot id on the planned side") + parser.add_argument( + "--side", + choices=["friendly", "enemy"], + default="friendly", + help="which strategy's robot to plan: friendly is the replay's config_a (default), enemy its config_b", + ) + parser.add_argument( + "--target", + type=float, + nargs=2, + metavar=("X", "Y"), + help="fixed target in the replay's pitch coordinates (metres); default: the robot's position --lookahead s later", + ) + parser.add_argument("--step", type=float, default=0.1, help="sampling interval, seconds (default 0.1)") + parser.add_argument( + "--lookahead", type=float, default=0.5, help="stand-in delay when no --target is given, seconds (default 0.5)" + ) + parser.add_argument( + "--out", type=Path, help="PNG path (default: replays/plan_trace/_-_.png)" + ) + parser.add_argument("--json", action="store_true", help="print the per-sample rows as JSON") + parser.add_argument( + "--findings", metavar="PATH", help="also write the parked / pushed-out spans as JSON lines (- for stdout)" + ) + args = parser.parse_args() + + if args.t1 < args.t0: + parser.error("--t1 is before --t0") + if args.step <= 0: + parser.error("--step must be positive") + + first = scenario_from_replay(args.replay, args.t0) + planned = first.friendly_robots if args.side == "friendly" else first.enemy_robots + if not any(s.id == args.robot for s in planned): + parser.error(f"no {args.side} robot {args.robot} at t={args.t0} s (ids: {sorted(s.id for s in planned)})") + + out = args.out or ( + REPO_ROOT / "replays" / "plan_trace" / f"{args.replay.stem}_{args.t0:g}-{args.t1:g}_{args.side}{args.robot}.png" + ) + config = first.config_a_name if args.side == "friendly" else first.config_b_name + short = (config or "?").removeprefix("build_").removesuffix("_kernel_strategy") + title = f"planned {args.side} {short}, robot {args.robot}, t = {args.t0:g} to {args.t1:g} s" + + trace = plan_trace( + replay_game_at(args.replay, args.side), + args.robot, + args.t0, + args.t1, + out, + step=args.step, + target=tuple(args.target) if args.target is not None else None, + lookahead=args.lookahead, + title=title, + ) + + if args.findings is not None: + findings = plan_findings(trace.rows, args.robot, replay=str(args.replay), side=args.side, image=str(trace.path)) + write_jsonl(findings, args.findings) + + # With `--findings -` the JSON owns stdout, so the text goes to stderr. + human = sys.stderr if args.findings == "-" else sys.stdout + if args.json: + print(json.dumps([dataclasses.asdict(r) for r in trace.rows], indent=1), file=human) + else: + print(f"wrote {trace.path}", file=human) + print(f"{len(trace.rows)} samples, {trace.target_note}", file=human) + max_offset = max(trace.rows, key=lambda r: r.carrot_offset) + min_carrot = min(trace.rows, key=lambda r: r.robot_to_carrot) + print( + f"max carrot offset from the robot-to-target line: {max_offset.carrot_offset:.3f} m at t={max_offset.t:g}", + file=human, + ) + print(f"min robot-to-carrot: {min_carrot.robot_to_carrot:.3f} m at t={min_carrot.t:g}", file=human) + + +if __name__ == "__main__": + main() diff --git a/tools/realism_scorecard.py b/tools/realism_scorecard.py new file mode 100644 index 00000000..83f22abc --- /dev/null +++ b/tools/realism_scorecard.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python3 +# ruff: noqa: E402 +"""Realism scorecard: a round-robin's matches against real Division B matches, component by +component (attempts, chance quality, keeper saves, misses, ball slow-down, kick speed ...). + + # the real side, from converted official logs (`tools/convert_ssl_log.py`); rewrites the + # committed reference (xG model + real rows) + pixi run python tools/realism_scorecard.py --real ~/dev/ssl/official-logs/2025 ~/dev/ssl/official-logs/2026 + + # a round-robin against the reference + pixi run python tools/realism_scorecard.py --sim replays/tournament_ + +Per-match shot records are cached: the real ones in `--real-cache`, the sim ones in +`/realism_shots.jsonl`; a cache is reused unless `--refresh`. What each row means and +why goal totals are not one: `utama_core/analysis/realism/scorecard.py`, `docs/sim_realism.md`. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from concurrent.futures import ProcessPoolExecutor +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parent.parent +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from utama_core.analysis.realism import scorecard + +SIM_CACHE_NAME = "realism_shots.jsonl" +DEFAULT_REAL_CACHE = Path.home() / "dev/ssl/official-logs/realism_shots.jsonl" + + +def _replays(paths: list[Path]) -> list[Path]: + files = [] + for p in paths: + files += sorted(p.rglob("*.npz")) if p.is_dir() else [p] + return files + + +def _records(paths: list[Path], cache: Path, workers: int, refresh: bool) -> list[dict]: + if cache.exists() and not refresh: + print(f"reusing {cache}") + return [json.loads(line) for line in cache.open()] + files = _replays(paths) + out = [] + cache.parent.mkdir(parents=True, exist_ok=True) + with ProcessPoolExecutor(workers) as ex, cache.open("w") as f: + for i, rec in enumerate(ex.map(scorecard.analyse_replay, files)): + f.write(json.dumps(rec) + "\n") + f.flush() + out.append(rec) + print(f"[{i + 1}/{len(files)}] {rec['match']} shots={len(rec.get('shots', []))} {rec.get('error', '')}") + return out + + +def main(argv=None) -> int: + p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + p.add_argument("--real", nargs="+", type=Path, help="converted real logs (dirs or .npz): rebuild the reference") + p.add_argument("--real-cache", type=Path, default=DEFAULT_REAL_CACHE) + p.add_argument("--sim-cache", type=Path, help=f"sim shot records (default /{SIM_CACHE_NAME})") + p.add_argument("--sim", type=Path, help="a round-robin run dir to score against the reference") + p.add_argument("--reference", type=Path, default=scorecard.REFERENCE_PATH) + p.add_argument("--workers", type=int, default=1, help="processes for the replay pass (each holds a match)") + p.add_argument("--refresh", action="store_true", help="ignore cached shot records") + p.add_argument("--json", type=Path, help="also write the compared rows here") + a = p.parse_args(argv) + if not a.real and not a.sim: + p.error("give --real, --sim or both") + + if a.real: + records = _records(a.real, a.real_cache, a.workers, a.refresh) + ref = scorecard.reference(records, ["/".join(x.resolve().parts[-2:]) for x in a.real]) + a.reference.write_text(json.dumps(ref, indent=1) + "\n") + c = ref["xg_check"] + print( + f"reference: {ref['matches']} matches, {ref['attempts']} attempts, {ref['goals_from_attempts']} scored; " + f"held-out AUC {c['auc']:.3f}, log loss {c['log_loss']:.4f} vs {c['log_loss_constant']:.4f} constant" + f" -> {a.reference}" + ) + if a.sim: + ref = json.loads(a.reference.read_text()) + records = _records([a.sim], a.sim_cache or a.sim / SIM_CACHE_NAME, a.workers, a.refresh) + lines = scorecard.compare(ref, records) + print(f"\n{a.sim.name} vs real ({ref['matches']} matches)\n") + print(scorecard.format_table(lines)) + if a.json: + a.json.write_text(json.dumps(lines, indent=1) + "\n") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tools/replay_trace.py b/tools/replay_trace.py index f3b85f4b..a7da13d4 100644 --- a/tools/replay_trace.py +++ b/tools/replay_trace.py @@ -7,6 +7,20 @@ pixi run python tools/replay_trace.py replays/tournament_/_vs_.npz 20 40 +`--events` prints the events in the window instead of the periodic rows, one line each: + KICK the ball's speed rises by >= 0.5 m/s in one tick, or leaves rest (< 0.5 to >= 1.0 m/s); + counted once per 0.25 s. The kicker is the robot nearest the ball at that tick. + HOLD possession changes (F = friendly, E = enemy, - = nobody), as the `held=` column. + REF the referee command changes, with the status message when there is one. + FOUL a foul from the replay's `.stats.json` (ours = friendly, theirs = enemy). +At most 40 lines; the rest are counted as "... N more". + +`--json` writes the rows (or the events, with `--events`) as a JSON list instead of text. +Each row: t, referee (command name), status, ball [x, y], ball_speed, held (F/E/-), near_F and +near_E ({id, dist} or null). Each event: t, kind (KICK/HOLD/REF/FOUL), text, and kind-specific +fields (KICK: side, robot_id, dist, ball_speed; HOLD: from, to; REF: command, status; FOUL: +side, rule, robot_id, tactic). + Times are seconds from the replay's first frame, the same clock as a `StallEvent`'s `sim_time`. The status message (why play stopped, e.g. "Keep-out circle violation") comes from the `.sparse_referee.pkl` beside the replay when there is one. @@ -14,7 +28,11 @@ from __future__ import annotations +import os + +os.environ.setdefault("PYGAME_HIDE_SUPPORT_PROMPT", "1") # before any import: hides pygame's banner import argparse +import json import pickle import sys from pathlib import Path @@ -28,6 +46,12 @@ from utama_core.entities.referee.referee_command import RefereeCommand +MAX_EVENT_LINES = 40 +KICK_RISE_MS = 0.5 # m/s rise in one tick +KICK_FROM_REST_MS = 0.5 # below this, then >= KICK_TO_MS, counts as a kick +KICK_TO_MS = 1.0 +KICK_COOLDOWN_S = 0.25 + def _statuses(npz_path: Path, n: int) -> list[Optional[str]]: pkl = npz_path.with_name(npz_path.name[: -len(".npz")] + ".sparse_referee.pkl") @@ -40,46 +64,209 @@ def _statuses(npz_path: Path, n: int) -> list[Optional[str]]: return out -def _nearest(positions: np.ndarray, ids: np.ndarray, ball: np.ndarray) -> str: +def _fouls(npz_path: Path) -> list[dict]: + """Fouls from the `.stats.json` beside the replay (`sim_time`, `side`: friendly or enemy).""" + stats = npz_path.with_name(npz_path.name[: -len(".npz")] + ".stats.json") + if not stats.exists(): + return [] + return json.loads(stats.read_text()).get("fouls") or [] + + +def _nearest(positions: np.ndarray, ids: np.ndarray, ball: np.ndarray) -> Optional[tuple[int, float]]: + """(robot id, distance) of the robot nearest `ball`, or None if no robot has a position.""" d = np.hypot(*(positions - ball).T) if not np.isfinite(d).any(): - return "-" + return None k = int(np.nanargmin(d)) - return f"{int(ids[k])}@{d[k]:.2f}" + return int(ids[k]), float(d[k]) -def trace(npz_path, t_start: float, t_end: float, every: int = 15) -> list[str]: - npz_path = Path(npz_path) - z = np.load(npz_path) - ts = z["ts"] - z["ts"][0] - statuses = _statuses(npz_path, len(ts)) - lines = [] +def _near_text(near: Optional[dict]) -> str: + return "-" if near is None else f"{near['id']}@{near['dist']:.2f}" + + +def _state(z, statuses: list[Optional[str]], i: int) -> dict: + """The row for tick `i` of a replay (`z` is the npz, or a dict with the same arrays).""" + cmd = RefereeCommand(int(z["referee_command"][i])).name if z["has_referee"][i] else "-" + ball = z["ball_p"][i, :2] + speed = float(np.hypot(*z["ball_v"][i, :2])) + holder = "F" if z["friendly_has_ball"][i].any() else "E" if z["enemy_has_ball"][i].any() else "-" + near = {} + for side in ("friendly", "enemy"): + n = _nearest(z[f"{side}_p"][i], z[f"{side}_ids"], ball) + near[side] = None if n is None else {"id": n[0], "dist": n[1]} + return { + "t": float(z["ts"][i] - z["ts"][0]), + "referee": cmd, + "status": statuses[i], + "ball": [float(ball[0]), float(ball[1])], + "ball_speed": speed, + "held": holder, + "near_F": near["friendly"], + "near_E": near["enemy"], + } + + +def _row_text(r: dict, status_changed: bool) -> str: + x, y = r["ball"] + return ( + f"{r['t']:7.2f} {r['referee']:<24} ball ({x:+.2f},{y:+.2f}) {r['ball_speed']:4.2f}m/s held={r['held']} " + f"near F {_near_text(r['near_F']):<8} E {_near_text(r['near_E']):<8}" + + (f" | {r['status']}" if status_changed and r["status"] else "") + ) + + +def rows(z, statuses: list[Optional[str]], t_start: float, t_end: float, every: int = 15) -> list[dict]: + """The periodic rows: one at each referee or status change and every `every` ticks, in the window.""" + out = [] prev = None + ts = z["ts"] - z["ts"][0] for i, t in enumerate(ts): - cmd = RefereeCommand(int(z["referee_command"][i])).name if z["has_referee"][i] else "-" - key = (cmd, statuses[i]) + key = ( + RefereeCommand(int(z["referee_command"][i])).name if z["has_referee"][i] else "-", + statuses[i], + ) if t_start <= t <= t_end and (key != prev or i % every == 0): - ball = z["ball_p"][i, :2] - speed = float(np.hypot(*z["ball_v"][i, :2])) - holder = "F" if z["friendly_has_ball"][i].any() else "E" if z["enemy_has_ball"][i].any() else "-" - lines.append( - f"{t:7.2f} {cmd:<24} ball ({ball[0]:+.2f},{ball[1]:+.2f}) {speed:4.2f}m/s held={holder} " - f"near F {_nearest(z['friendly_p'][i], z['friendly_ids'], ball):<8} " - f"E {_nearest(z['enemy_p'][i], z['enemy_ids'], ball):<8}" - + (f" | {statuses[i]}" if key != prev and statuses[i] else "") - ) + r = _state(z, statuses, i) + r["status_changed"] = key != prev + out.append(r) prev = key + return out + + +def events(z, statuses: list[Optional[str]], fouls: list[dict], t_start: float, t_end: float) -> list[dict]: + """The KICK, HOLD, REF and FOUL events with t in [t_start, t_end], in time order.""" + ts = z["ts"] - z["ts"][0] + speed = np.hypot(z["ball_v"][:, 0], z["ball_v"][:, 1]) + out: list[dict] = [] + first = max(1, int(np.searchsorted(ts, t_start, side="left"))) + if first < len(ts): + prev = _state(z, statuses, first - 1) + last_kick = -np.inf + for i in range(first, len(ts)): + t = float(ts[i]) + if t > t_end: + break + cur = _state(z, statuses, i) + rise = speed[i] - speed[i - 1] + is_kick = (speed[i - 1] < KICK_FROM_REST_MS and speed[i] >= KICK_TO_MS) or rise >= KICK_RISE_MS + if is_kick and t - last_kick >= KICK_COOLDOWN_S: + last_kick = t + kick = {"side": None, "robot_id": None, "dist": None} + near = [(s, cur[k]) for s, k in (("F", "near_F"), ("E", "near_E")) if cur[k] is not None] + if near: + side, n = min(near, key=lambda sn: sn[1]["dist"]) + kick = {"side": "ours" if side == "F" else "theirs", "robot_id": n["id"], "dist": n["dist"]} + who = f"{side}{n['id']} at {n['dist']:.2f} m" + else: + who = "no robot" + out.append( + { + "t": t, + "kind": "KICK", + **kick, + "ball_speed": float(speed[i]), + "text": f"{t:7.2f} KICK by {who}, ball {speed[i - 1]:.2f} -> {speed[i]:.2f} m/s", + } + ) + if cur["held"] != prev["held"]: + out.append( + { + "t": t, + "kind": "HOLD", + "from": prev["held"], + "to": cur["held"], + "text": f"{t:7.2f} HOLD {prev['held']} -> {cur['held']}", + } + ) + if cur["referee"] != prev["referee"] or (cur["status"] != prev["status"] and cur["status"]): + status = f" | {cur['status']}" if cur["status"] else "" + out.append( + { + "t": t, + "kind": "REF", + "command": cur["referee"], + "status": cur["status"], + "text": f"{t:7.2f} REF {prev['referee']} -> {cur['referee']}{status}", + } + ) + prev = cur + for f in fouls: + ft = float(f["sim_time"]) + if t_start <= ft <= t_end: + side = "ours" if f.get("side") == "friendly" else "theirs" + out.append( + { + "t": ft, + "kind": "FOUL", + "side": side, + "rule": f["rule"], + "robot_id": f.get("robot_id"), + "tactic": f.get("tactic"), + "text": f"{ft:7.2f} FOUL {side} {f['rule']} robot {f.get('robot_id')} {f.get('tactic') or ''}".rstrip(), + } + ) + out.sort(key=lambda e: e["t"]) + return out + + +def _load(npz_path: Path): + z = dict(np.load(npz_path)) # NpzFile re-reads an array on every access; load each once + return z, _statuses(npz_path, len(z["ts"])), _fouls(npz_path) + + +def trace(npz_path, t_start: float, t_end: float, every: int = 15) -> list[str]: + """The periodic rows of the window as text lines.""" + z, statuses, _ = _load(Path(npz_path)) + return [_row_text(r, r["status_changed"]) for r in rows(z, statuses, t_start, t_end, every)] + + +def event_lines(npz_path, t_start: float, t_end: float) -> list[str]: + """The events of the window as text lines, capped at MAX_EVENT_LINES.""" + z, statuses, fouls = _load(Path(npz_path)) + evs = events(z, statuses, fouls, t_start, t_end) + lines = [e["text"] for e in evs[:MAX_EVENT_LINES]] + if len(evs) > MAX_EVENT_LINES: + lines.append(f"... {len(evs) - MAX_EVENT_LINES} more") return lines +def _jsonable(x): + """Floats to 3 decimals and event texts stripped, so the JSON is short and stable.""" + if isinstance(x, dict): + return {k: _jsonable(v) for k, v in x.items()} + if isinstance(x, list): + return [_jsonable(v) for v in x] + if isinstance(x, float): + return round(x, 3) + if isinstance(x, str): + return x.strip() + return x + + def main() -> None: parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument("replay", type=Path, help="a tournament match's .npz") parser.add_argument("t_start", type=float) parser.add_argument("t_end", type=float) parser.add_argument("--every", type=int, default=15, help="also print every N ticks (default 15 = 0.25 s)") + parser.add_argument("--events", action="store_true", help="print the events (kicks, possession, referee, fouls)") + parser.add_argument("--json", action="store_true", help="write JSON (rows, or events with --events)") args = parser.parse_args() - print("\n".join(trace(args.replay, args.t_start, args.t_end, args.every))) + if args.json: + z, statuses, fouls = _load(args.replay) + if args.events: + data = events(z, statuses, fouls, args.t_start, args.t_end) + else: + data = [ + {k: v for k, v in r.items() if k != "status_changed"} + for r in rows(z, statuses, args.t_start, args.t_end, args.every) + ] + print(json.dumps(_jsonable(data), indent=1)) + elif args.events: + print("\n".join(event_lines(args.replay, args.t_start, args.t_end))) + else: + print("\n".join(trace(args.replay, args.t_start, args.t_end, args.every))) if __name__ == "__main__": diff --git a/tools/repro_from_replay.py b/tools/repro_from_replay.py index 47813e6b..71b80fb5 100644 --- a/tools/repro_from_replay.py +++ b/tools/repro_from_replay.py @@ -21,6 +21,11 @@ build the runner the same way `tournament.run_match` does, apply the scenario, tick, print a summary. +Sim noise: by default it plays with the replay's own `SimNoise` (its seed and settings, stored in +the .npz), so latency, kick offsets and motion limits redraw the match's values; `--sim-noise PATH` +uses another settings file, `--sim-noise none` plays noiseless. Vision and kick-speed noise are not +replayed (their streams start afresh at `--t`). + Known limitations (see `utama_core/replay/scenario.py`'s module docstring for the full rationale): - Tactic `mem` (each `Tactic`'s internal per-slot state — phase, committed @@ -36,14 +41,47 @@ starts with whatever the freshly-seeded `CustomReferee` defaults to. Usage: - pixi run python tools/repro_from_replay.py replays/tournament_.../match.pkl \\ + pixi run python tools/repro_from_replay.py replays/tournament_.../match.npz \\ --t 260 --duration 15 --control-scheme trajsample \\ --trace-out /tmp/repro_trace.jsonl + +`--strategy` / `--opponent` take the short name of a `build_*_kernel_strategy` factory in +`utama_core/strategy/` (e.g. `clear_press_plus`, `counter_flow`), the same names as the +replay's `summary.json` config names. The trace shows the tactics those strategies use, not +the strategy names (e.g. `decoy_and_overload.*` keys come from a strategy's DecoyOverloadTactic). + +`--trace-out` (default /tmp/repro_from_replay.intentions.jsonl; write under a scratch dir instead) +is a JSON Lines file, one object per line, written when the run closes. Field `event` names the +kind; every row has `tick` (the strategy's tick count) and `sim_time` (seconds, the replay's clock): + "intention": `tactic_id` (the kernel slot, e.g. "overload", "block", "attack"), `robot_ids` (the + robots the slot now holds; [] = released), `tag`, `note`: on an assignment the tactic + class name (e.g. "DecoyOverloadTactic"), on a release why, else null. Written when a + slot's robot set changes, not every tick. No `key`. + "trace": `key` and `value`. Keys are `highlights.` (value: {robot_id: label}), + `committed_robot_ids`, and tactic points such as `give_and_go.pass_target` + ({receiver_id, x, y}), `switch_of_play.runner_target` ({runner_id, x, y}), + `clear_ball.clear_target` ({from, to}), `dribble.segment_target` ({x, y}), + `go_to_ball[].approach`. Written only when the value changes. No `tactic_id`. + "referee": `command`, `stage`, scores, `designated`, `note`. Written on referee-state changes. + No `key` and no `robot_ids`. +Read it by checking `event` first; `key` exists only on "trace" rows. + +`--summary ROBOT_IDS` (our side, e.g. `3` or `1,3`) prints, every 0.25 s of the run and at most +40 lines, per listed robot: `t`, `robot`, `tactic` (the tactic class holding it, from the intention +rows up to that time; the slot id if the class is unknown; `-` if none), `pos` (its position), `dist_ball` (to the ball), `ref` (the referee +command at that tick) and `target`: the latest traced point (a "trace" row with a `*_id` field +equal to the robot's id, e.g. give_and_go.pass_target's receiver_id), else `-`. Tactics that trace +points without a robot id (clear_ball, dribble, decoy_and_overload) show `-`. """ from __future__ import annotations +import os + +os.environ.setdefault("PYGAME_HIDE_SUPPORT_PROMPT", "1") # before any import: hides pygame's banner + import argparse +import json import math import sys from pathlib import Path @@ -58,7 +96,9 @@ from utama_core.engine.abstract_strategy import AbstractStrategy from utama_core.engine.match_log import load_jsonl from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.replay.columnar_reader import load_sim_noise from utama_core.replay.scenario import Scenario, apply_scenario, scenario_from_replay +from utama_core.rsoccer_simulator.src.Utils.sim_noise import SimNoise from utama_core.run import StrategyRunner from utama_core.strategy import kernel_strategy @@ -85,6 +125,118 @@ def _resolve_config_name(explicit: Optional[str], from_scenario: Optional[str], return name +def resolve_sim_noise(override: Optional[str], replay_noise: Optional[dict]) -> tuple[Optional[SimNoise], str]: + """The `SimNoise` a repro plays with, and the line that says so. + + `override` is `--sim-noise`: "none" for noiseless, a path to a `SimNoise` JSON file, or None for + the replay's own. `replay_noise` is what `load_sim_noise` returned for the replay (`{"settings": + ..., "draw": ...}`), or None when it had none. The replay's settings carry its seed, so + `SimNoise.draw` gives the same per-robot values the match drew. + """ + if override == "none": + return None, "sim noise: none (--sim-noise none)" + if override is not None: + noise = SimNoise.from_dict(json.loads(Path(override).read_text())) + return noise, f"sim noise: {override} (--sim-noise, seed {noise.seed})" + if replay_noise is None: + return None, "sim noise: none (the replay stores none)" + noise = SimNoise.from_dict(replay_noise["settings"]) + return noise, f"sim noise: the replay's own (seed {noise.seed})" + + +SUMMARY_EVERY_S = 0.25 +SUMMARY_MAX_LINES = 40 + + +def _parse_ids(spec: str) -> list[int]: + return [int(x) for x in spec.split(",") if x.strip()] + + +def _snapshot(runner, robot_ids: list[int]) -> dict: + """Our robots in `robot_ids` and the ball at the runner's current frame.""" + frame = runner.my.current_game_frame + ref_data = runner.my.game.referee + ball = (frame.ball.p.x, frame.ball.p.y) if frame.ball is not None else None + robots = { + rid: (frame.friendly_robots[rid].p.x, frame.friendly_robots[rid].p.y) + for rid in robot_ids + if rid in frame.friendly_robots + } + return { + "t": frame.ts, + "referee": ref_data.referee_command.name if ref_data is not None else None, + "ball": ball, + "robots": robots, + } + + +def summary_rows(samples: list[dict], robot_ids: list[int], events: list, every: float = SUMMARY_EVERY_S) -> list[dict]: + """One row per listed robot every `every` seconds. `samples`: dicts with t, referee, ball (x, y) or + None, and robots {id: (x, y)}, in time order. `events`: IntentionEvent-like (sim_time, tactic_id, + robot_ids) and TraceEvent-like (sim_time, key, value) objects, any order; applied up to each row's t.""" + intentions = sorted((e for e in events if hasattr(e, "tactic_id")), key=lambda e: e.sim_time) + traces = sorted((e for e in events if hasattr(e, "key")), key=lambda e: e.sim_time) + holder: dict[int, str] = {} # robot -> slot id + classes: dict[str, str] = {} # slot id -> tactic class, from assignment notes + target: dict[int, str] = {} + ii = ti = 0 + last_t: Optional[float] = None + rows: list[dict] = [] + for s in samples: + t = s["t"] + if last_t is not None and t - last_t < every - 1e-9: + continue + last_t = t + while ii < len(intentions) and intentions[ii].sim_time <= t: + e = intentions[ii] + ii += 1 + if e.robot_ids and e.note and not e.note.startswith(("released", "deadline")): + classes[e.tactic_id] = e.note + # The event says which robots the tactic now holds: drop the ones it lost, add the new ones. + for rid in [r for r, tid in holder.items() if tid == e.tactic_id and r not in e.robot_ids]: + del holder[rid] + for rid in e.robot_ids: + holder[rid] = e.tactic_id + while ti < len(traces) and traces[ti].sim_time <= t: + e = traces[ti] + ti += 1 + value = e.value + if e.key.endswith("target") and isinstance(value, dict) and "x" in value and "y" in value: + for field, rid in value.items(): + if field.endswith("_id"): + target[rid] = f"{e.key} ({value['x']:.2f},{value['y']:.2f})" + for rid in robot_ids: + pos = s["robots"].get(rid) + ball = s["ball"] + rows.append( + { + "t": t, + "robot": rid, + "tactic": classes.get(holder[rid], holder[rid]) if rid in holder else "-", + "pos": pos, + "dist_ball": math.hypot(pos[0] - ball[0], pos[1] - ball[1]) if pos and ball else None, + "ref": s["referee"], + "target": target.get(rid, "-"), + } + ) + return rows + + +def format_summary(rows: list[dict], max_lines: int = SUMMARY_MAX_LINES) -> list[str]: + """`summary_rows` as text lines, at most `max_lines` and then "... N more".""" + lines = [] + for r in rows[:max_lines]: + pos = "( - , - )" if r["pos"] is None else f"({r['pos'][0]:+.2f},{r['pos'][1]:+.2f})" + dist = " -" if r["dist_ball"] is None else f"{r['dist_ball']:.2f}" + lines.append( + f"{r['t']:8.2f}s robot {r['robot']} {r['tactic']:<24} at {pos} ball {dist} m " + f"ref {r['ref'] or '-':<22} target {r['target']}" + ) + if len(rows) > max_lines: + lines.append(f"... {len(rows) - max_lines} more") + return lines + + def main() -> None: parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument("replay_path", type=Path, help="path to a .pkl or .npz replay") @@ -98,18 +250,38 @@ def main() -> None: parser.add_argument( "--strategy", default=None, - help="override the friendly (config_a) strategy, e.g. build_split_shape_kernel_strategy", + help="override the friendly (config_a) strategy: the short name (e.g. clear_press_plus) or the factory " + "name (e.g. build_clear_press_plus_kernel_strategy); default: the replay's summary.json", ) parser.add_argument( - "--opponent", default=None, help="override the enemy (config_b) strategy, e.g. build_high_press_kernel_strategy" + "--opponent", + default=None, + help="override the enemy (config_b) strategy, same two name forms (e.g. counter_flow); default: the " + "replay's summary.json", ) parser.add_argument( "--trace-out", default="/tmp/repro_from_replay.intentions.jsonl", help="where to write the match_log JSONL" ) parser.add_argument("--print-trace", action="store_true", help="print every TraceEvent as it's written back") + parser.add_argument( + "--sim-noise", + default=None, + metavar="PATH|none", + help="sim noise to play with: none (noiseless), or a SimNoise JSON file; default: the replay's own " + "(see the module docstring)", + ) + parser.add_argument( + "--summary", + default=None, + metavar="ROBOT_IDS", + help="print tactic, position, distance to ball, referee command and traced target every 0.25 s for " + "these of our robots, e.g. 3 or 1,3 (see the module docstring)", + ) args = parser.parse_args() scenario: Scenario = scenario_from_replay(args.replay_path, args.t) + replay_noise = load_sim_noise(args.replay_path) if args.replay_path.suffix == ".npz" else None + sim_noise, noise_line = resolve_sim_noise(args.sim_noise, replay_noise) strategy_a_name = _resolve_config_name(args.strategy, scenario.config_a_name, "friendly") strategy_b_name = _resolve_config_name(args.opponent, scenario.config_b_name, "enemy") @@ -137,8 +309,14 @@ def main() -> None: referee_initial_command=RefereeCommand.PREPARE_KICKOFF_YELLOW, control_scheme=args.control_scheme, match_log_path=args.trace_out, + sim_noise=sim_noise, ) + print(noise_line) + if sim_noise is not None and replay_noise is not None and args.sim_noise is None: + drawn = runner.rsim_env.sim_draw.to_dict() + same = json.loads(json.dumps(drawn)) == replay_noise["draw"] + print(f" per-robot draws {'match' if same else 'DO NOT match'} the replay's stored draw") print(f"Loaded scenario from {args.replay_path} at t={args.t}s (nearest frame ts={scenario.frame_ts:.3f}s)") print(f" friendly={strategy_a_name} enemy={strategy_b_name}") print(f" referee_command at load: {scenario.referee_command}") @@ -156,6 +334,10 @@ def main() -> None: } referee_commands_seen: list[str] = [] last_command = None + summary_ids = _parse_ids(args.summary) if args.summary else [] + samples: list[dict] = [] + if summary_ids: + samples.append(_snapshot(runner, summary_ids)) for _ in range(n_ticks): runner.step_once() @@ -164,6 +346,8 @@ def main() -> None: ball_positions.append((frame.ball.p.x, frame.ball.p.y)) ref_data = runner.my.game.referee command = ref_data.referee_command.name if ref_data is not None else None + if summary_ids: + samples.append(_snapshot(runner, summary_ids)) if command != last_command: referee_commands_seen.append(command) last_command = command @@ -208,6 +392,11 @@ def main() -> None: finally: runner.close() + if summary_ids: + lines = format_summary(summary_rows(samples, summary_ids, load_jsonl(args.trace_out))) + print(f"\nSummary for robot(s) {summary_ids} (every {SUMMARY_EVERY_S}s, at most {SUMMARY_MAX_LINES} lines):") + print("\n".join(lines)) + if args.print_trace and runner.match_log is not None: last_value: dict[str, object] = {} for event in load_jsonl(args.trace_out): diff --git a/tools/restart_picture.py b/tools/restart_picture.py new file mode 100644 index 00000000..46693d25 --- /dev/null +++ b/tools/restart_picture.py @@ -0,0 +1,154 @@ +"""Picture of where the referee override sends every robot for one restart. + + pixi run python tools/restart_picture.py REPLAY.npz --t 260 [--side enemy] [--out PATH] + pixi run python tools/restart_picture.py REPLAY.npz --all-restarts [--side enemy] [--out PATH] + pixi run python tools/restart_picture.py REPLAY.npz --findings - [--side enemy] + +Rebuilds the field state at `--t` from the replay (`utama_core.replay.scenario`), runs +`RefereeOverride.tick` for the replay's referee command there, exactly as the strategy does, and draws +the target each friendly robot is commanded to: the full pitch, the ball and its 0.8 m keep-out circle, +the placement or free-kick spot, each robot with its id, role (kicker, placer, keeper) and an arrow to its +commanded target, and a text box with the command, which team it is for, and a red flag on any target +inside the keep-out circle or a defence area it must avoid. `--all-restarts` draws one small panel per +distinct restart command in the replay's referee sidecar (the first occurrence of each, at most 8). +`--side enemy` shows the other team's override. Read-only; the planner and the referee are not changed. + +`--findings PATH` writes one JSON line per flag (`utama_core/analysis/findings.py`) for every restart in the +match, each occurrence, without drawing: kinds `target_in_keep_out`, `keep_out_short` (the 1 cm stop deadband) +and `target_in_defense_area`. Give `--t` or `--all-restarts` too to set `image` on the findings whose +restart was drawn. `--findings -` writes the JSON to stdout and the text to stderr. + +Use it when a restart goes wrong: robots inside the keep-out, a kicker that never reaches the ball, a +placer or defender parked in the wrong place. Filter on the findings first, open the picture to understand +them. See docs/STRATEGY_DEVELOPMENT.md, "Referee handling". +""" + +from __future__ import annotations + +import argparse +import contextlib +import dataclasses +import os +import sys +from pathlib import Path + +os.environ.setdefault("PYGAME_HIDE_SUPPORT_PROMPT", "1") # keep pygame's banner off stdout (--findings -) + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from utama_core.analysis.findings import Finding, write_jsonl # noqa: E402 +from utama_core.analysis.restart_picture import ( # noqa: E402 + plan_restart, + render_restart, + render_restart_grid, + restart_commands, + restart_findings, + restart_game, +) +from utama_core.engine.referee_override import is_override_command # noqa: E402 +from utama_core.replay.scenario import scenario_from_replay # noqa: E402 + +MAX_PANELS = 8 + + +def _run(args: argparse.Namespace, parser: argparse.ArgumentParser) -> list[Finding]: + """Draw what the flags ask for and return the findings of the whole match (when `--findings` is given).""" + stem = args.replay.stem + if args.all_restarts: + commands = restart_commands(args.replay) + if not commands: + parser.error("no restart command in the replay's referee sidecar") + if len(commands) > MAX_PANELS: + print(f"{len(commands)} restart commands; drawing the first {MAX_PANELS}") + commands = commands[:MAX_PANELS] + plans = [] + drawn_t: list[float] = [] + for t, command in commands: + try: + scenario = scenario_from_replay(args.replay, t, showing=command) + except ValueError as err: + print(f"skipped {command.name} at t={t:g} s: {err}") + continue + game = restart_game(args.replay, scenario, args.side) + plans.append(plan_restart(game, command, sim_time=scenario.sim_time)) + drawn_t.append(t) + if not plans: + parser.error("no restart could be rebuilt from the replay") + out = args.out or REPO_ROOT / "replays" / "restart_picture" / f"{stem}_restarts_{args.side}.png" + render_restart_grid(plans, out, title=f"{stem}: referee override for each restart ({args.side} side)") + for plan in plans: + print(f"{plan.command.name:<26} {plan.side:<7} t={plan.sim_time:g} s flags: {len(plan.flagged)}") + print(f"wrote {out}") + return _with_images(args, {t: out for t in drawn_t}) + + scenario = scenario_from_replay(args.replay, args.t) + command = scenario.referee_command + if command is None or not is_override_command(command): + name = command.name if command is not None else "none" + parser.error(f"referee command at t={args.t:g} s is {name}, not a restart the override handles") + game = restart_game(args.replay, scenario, args.side) + plan = plan_restart(game, command, sim_time=scenario.sim_time) + out = args.out or REPO_ROOT / "replays" / "restart_picture" / f"{stem}_t{args.t:g}_{args.side}.png" + render_restart(plan, out, title=f"{stem}: {args.side} side's override") + print(f"wrote {out}") + for rp in plan.robots: + target = "-" if rp.target is None else f"({rp.target[0]:+.2f}, {rp.target[1]:+.2f})" + flags = "; ".join(rp.flags) or "ok" + print(f" robot {rp.robot_id:>2} {rp.role or '':<8} {rp.kind:<4} {target:<18} {flags}") + return _with_images(args, {args.t: out}) + + +def _with_images(args: argparse.Namespace, drawn_at: dict) -> list[Finding]: + """The whole match's findings (only when `--findings` is given), with `image` set where a picture was drawn.""" + if args.findings is None: + return [] + findings = restart_findings(args.replay, args.side) + return [ + dataclasses.replace(f, image=str(drawn_at[f.t0])) if drawn_at.get(f.t0) is not None else f for f in findings + ] + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("replay", type=Path, help="a replay .npz from a run") + parser.add_argument("--t", type=float, help="time in seconds from the replay's first frame") + parser.add_argument( + "--all-restarts", + action="store_true", + help="one small panel per distinct restart command in the replay's referee sidecar (at most 8)", + ) + parser.add_argument( + "--side", + choices=["friendly", "enemy"], + default="friendly", + help="whose override to draw: friendly is the replay's config_a (default), enemy its config_b", + ) + parser.add_argument("--out", type=Path, help="PNG path (default: replays/restart_picture/...)") + parser.add_argument( + "--findings", + metavar="PATH", + help="also write the flagged targets of every restart in the match as JSON lines (- for stdout)", + ) + args = parser.parse_args() + if args.findings is None and args.all_restarts == (args.t is not None): + parser.error("give exactly one of --t or --all-restarts") + if args.findings is not None and args.t is None and not args.all_restarts: + # findings only: no picture, the match's findings are all there is to write + write_jsonl(restart_findings(args.replay, args.side), args.findings) + return + + if args.findings == "-": + # stdout carries only the JSON; the picture's text goes to stderr + with contextlib.redirect_stdout(sys.stderr): + findings = _run(args, parser) + write_jsonl(findings, "-") + else: + findings = _run(args, parser) + if args.findings is not None: + write_jsonl(findings, args.findings) + + +if __name__ == "__main__": + main() diff --git a/tools/scenario_bench.py b/tools/scenario_bench.py deleted file mode 100644 index 0ecaccc2..00000000 --- a/tools/scenario_bench.py +++ /dev/null @@ -1,1164 +0,0 @@ -#!/usr/bin/env python3 -# ruff: noqa: E402 -"""Situation bench: how a candidate strategy changes goals per match, estimated from short -paired replays of the situations goals come from. - - effect = sum over situation types of (how often it happens per match) - x (candidate's P(goal) from it - baseline's) - -Each start of the bank is a situation of the goal ledger (`utama_core.analysis.goal_ledger`) -from the candidate's view: own or opponent ball won or free kick in each third, own or -opponent kickoff or penalty, force start. Its outcome is the first goal within `--horizon` -(default 10 s, the ledger's window): for, against, or none. P(for) and P(against) per type -are means over the type's starts, and the change is paired: the candidate and the baseline -play the same starts. How often each type happens comes from a `--weights` file of situation -rates (`goal_ledger --rates`): real Division B matches (default) or our own round-robin. - -What the effect is not: the bank's starts are fixed, so a candidate that changes how often -situations happen (wins the ball higher up, concedes fewer free kicks) is credited only for -what it does from each one. A goal counts for every situation that started in the 10 s -before it, so the effect over-counts goals by about 1.2x under sim rates and 1.6x under -real ones (`docs/STRATEGY_DEVELOPMENT.md`). It is a screen; round-robins confirm. - -This is the "outer loop, fast half" from `docs/roadmap.md` item 14 — a -benchmark like `tools/motion_planning_benchmark.py`, non-blocking, reporting -numbers rather than gating a merge. - -Bank sources, in the order this tool can use them today: - - Hand-authored anchors (`utama_core.scenario_bench.hand_authored_scenarios`) — - loaded when no bank is given, no dependency on any replay data; they have no - situation type or opponent (pass `--opponent`), and count only in the per-start table. - - Harvested starts (`--harvest-from RUN_DIR`): every restart and ball won of - a tagged, trustworthy tournament run (`.stats.json` with zero - `stall_events`, see `utama_core.scenario_bench.scenario_harvester`'s module - docstring for why this gate exists), up to `--per-situation N` of each type. - NOT run by this tool — point it at an already-completed `round_robin.py` run - directory. - -For each start, both `--candidate` and `--baseline` play the SAME start from config_a's -side, against the opponent it came from (its source match's config_b), so the bank's -opponents are the round-robin's; `--opponent` plays every start against one config -instead. A start whose opponent does not exist on this branch is skipped and counted. - -rsim is deterministic, so a scenario can also be played from `--repeats` starts -(`start.jittered`: robots away from the ball nudged a few cm; seed 0 -is the scenario as authored), and both sides get the same starts. The default -is 1: more scenarios detect a change better than more repeats of each (every -start from one round-robin, once each, finds passes aimed 10 degrees off at -t = -7.6). The standard error of the mean delta is across scenarios. - -The baseline is either a `--baseline` config run in this process, or -`--against-results FILE`: the candidate outcomes of an earlier run's JSON. The -second is how to A/B a code change (shared tactics, planner): run the bench at -commit A, then at commit B with `--against-results` pointing at A's JSON. The -opponent, horizon and repeats must match. With neither, it only records the -candidate's outcomes, e.g. to serve as a later run's `--against-results`. - -Each start's record keeps the scorer's ordinal outcome (GOAL_AGAINST=-3 ... GOAL_FOR=+3, -see `utama_core.scenario_bench.scenario_scorer`), its signals, fouls and stalls; the -per-start `delta` and `--stop-at-t` are on that ordinal. - -`--save-bank PATH` persists the currently-loaded scenario set (hand-authored, or -`--harvest-from`, after `--families` and `--where` filtering) to a single JSON -file via `start.save_bank` — a few KB even for hundreds of -scenarios, since a scenario is field state only, no trajectories. `--load-bank -PATH` loads scenarios from a previously-saved bank instead of hand-authored/ -`--harvest-from` (the two are mutually exclusive as *sources*: a loaded bank -is meant to be the frozen set a prior save produced, not a starting point to -silently merge fresh sources into — see item 14's "Immutable per bank version" -note). A bank is every harvested start, near-duplicates dropped; there is no -play-forward screen (one was tried: it kept starts where the OPPONENT changed -the outcome, and threw away as many starts where the candidate did). To grow a -bank from a new round-robin, `--merge-into` the current bank: harvested -scenarios that duplicate it are dropped, and the new bank is the old one plus -the rest. `--workers N` scores (and harvests) N scenarios at a time. - -`--reuse` takes a start's outcome from `replays/match_cache/` instead of playing it -when nothing that start runs has changed since it was stored -(`utama_core.replay.fingerprint.bench_key`: the jittered start, both configs' code, the -shared code and environment, the horizon). A baseline config whose code is unchanged -then costs nothing, and only the candidate plays. `--spot-check F` (default 0.05) also -replays that fraction of the reusable starts and compares them with their records; a -difference means the fingerprint missed a dependency, is printed loudly, evicts the -records this run used, and makes the run exit non-zero. -`--play ID [ID ...]` plays the given starts once each (per `--repeats` seed) for -`--candidate` and shows what happened, instead of scoring a bank: -its outcome and signals, and a timeline of the referee, the ball, the robot of each side -nearest it and every change in the candidate's tactic assignments. Each start is played -against its own opponent unless `--opponent` is given. The replay -(`.npz`) and the candidate's match log (`.intentions.jsonl`) are kept under -`replays/scenario_play//`, with the timeline as `.trace.txt` and, with -`--render png|mp4`, a picture of the whole window. Use it to test a change in a -situation the round-robin rarely reaches, or to watch a start a bench run flagged. - -Still not built: lifecycle promotion (candidate -> validated -> active) or a -ladder (slow half). - -Run from the repository root, for example: - - # A candidate against the baseline on the current bank, real Division B weights: - pixi run python tools/scenario_bench.py --load-bank utama_core/scenario_bench/banks/bank_v8.json \\ - --candidate build_counter_flow_kernel_strategy --baseline build_split_shape_kernel_strategy --workers 15 - - # The same, weighted by our own round-robin's situation rates: - pixi run python tools/scenario_bench.py --load-bank utama_core/scenario_bench/banks/bank_v8.json \\ - --candidate counter_flow --baseline split_shape --workers 15 \\ - --weights utama_core/scenario_bench/banks/situation_rates_sim.json - - pixi run python tools/scenario_bench.py --load-bank utama_core/scenario_bench/banks/bank_v8.json \\ - --list-scenarios --where third=attacking - - # Harvest and freeze a bank (up to 40 starts per situation type, kickoffs 20): - pixi run python tools/scenario_bench.py --harvest-from replays/tournament_20261011_230119 \\ - --per-situation 40 --workers 15 --save-bank utama_core/scenario_bench/banks/bank_v8.json --list-scenarios - - # Play a start and watch it (timeline printed, replay and picture kept): - pixi run python tools/scenario_bench.py --load-bank utama_core/scenario_bench/banks/bank_v8.json \\ - --play counter_flow_vs_split_shape_t426.3_own_ball_won_attacking --candidate high_press --render png - - # A/B a code change: record at commit A, compare at commit B. - pixi run python tools/scenario_bench.py --load-bank utama_core/scenario_bench/banks/bank_v8.json \\ - --candidate build_split_shape_kernel_strategy --workers 15 - pixi run python tools/scenario_bench.py --load-bank utama_core/scenario_bench/banks/bank_v8.json \\ - --candidate build_split_shape_kernel_strategy --workers 15 \\ - --against-results scenario_bench_results/scenario_bench_.json -""" - -from __future__ import annotations - -import argparse -import functools -import json -import math -import random -import statistics -import sys -from concurrent.futures import ProcessPoolExecutor -from datetime import datetime, timezone -from pathlib import Path -from typing import Callable, Iterable, Iterator, Optional - -import numpy as np - -REPO_ROOT = Path(__file__).resolve().parents[1] -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -from tools.replay_trace import trace -from utama_core.analysis.goal_ledger import TYPES, WINDOW_S, per_match -from utama_core.config.settings import REPLAY_BASE_PATH -from utama_core.replay import match_cache -from utama_core.replay.fingerprint import CodeGraph, bench_key -from utama_core.rsoccer_simulator.src.Simulators.robosim.robosim_wrapper import ( - enable_sim_reuse, -) -from utama_core.scenario_bench.hand_authored_scenarios import ( - all_hand_authored_scenarios, -) -from utama_core.scenario_bench.scenario_harvester import harvest_run_dir -from utama_core.scenario_bench.scenario_scorer import ( - SIGNALS, - ScenarioOutcome, - _resolve_config_name, - score_scenario, -) -from utama_core.scenario_bench.start import ( - TAGS, - BenchScenario, - drop_near_duplicates, - jittered, - load_bank, - matches_where, - save_bank, - start_tags, -) - -SCHEMA_VERSION = 1 -BANKS = REPO_ROOT / "utama_core" / "scenario_bench" / "banks" -DEFAULT_WEIGHTS = BANKS / "situation_rates_real.json" - - -def _git_revision() -> str | None: - import subprocess - - try: - result = subprocess.run(["git", "rev-parse", "--short", "HEAD"], check=True, capture_output=True, text=True) - except (OSError, subprocess.CalledProcessError): - return None - return result.stdout.strip() or None - - -def _parallel_map(fn: Callable, items: Iterable, workers: int) -> Iterator: - """`map`, over `workers` processes when more than one (results stay in order).""" - if workers <= 1: - yield from map(fn, items) - return - with ProcessPoolExecutor(max_workers=workers) as pool: - yield from pool.map(fn, items) - - -def _load_bank(args: argparse.Namespace) -> tuple[list[BenchScenario], list[BenchScenario], dict]: - """(scenarios to score, the `--merge-into` bank's scenarios, harvest report).""" - harvest_report: dict = {} - merged: list[BenchScenario] = [] - - if args.load_bank is not None: - # A persisted bank replaces the hand-authored + harvest sources - # entirely rather than merging with them — it's meant to be the - # frozen set a prior `--save-bank` produced (see - # `start.save_bank`'s "immutable per bank version" note), - # not a starting point to silently mix fresh sources into. - _bank_id, scenarios = load_bank(args.load_bank) - elif args.harvest_from is not None: - scenarios, harvest_report = harvest_run_dir( - args.harvest_from, - evaluator_version=_git_revision() or "unknown", - per_situation=args.per_situation, - workers=args.workers, - ) - else: - scenarios = list(all_hand_authored_scenarios()) - - if args.families: - wanted = set(args.families) - scenarios = [s for s in scenarios if s.provenance.family.value in wanted] - if args.where: - scenarios = [s for s in scenarios if matches_where(start_tags(s), args.where)] - - if args.merge_into is not None: - _bank_id, merged = load_bank(args.merge_into) - if args.harvest_from is not None: - before = len(scenarios) - scenarios = drop_near_duplicates(scenarios, keep=merged) - against = f"{args.merge_into} or each other" if args.merge_into else "each other" - print(f"{before - len(scenarios)} of {before} scenarios duplicate {against}; dropped") - - return scenarios, merged, harvest_report - - -def _save(args: argparse.Namespace, scenarios: list[BenchScenario]) -> None: - if args.save_bank is not None: - bank_id = args.bank_id or args.save_bank.stem - save_bank(scenarios, args.save_bank, bank_id=bank_id) - print(f"Saved {len(scenarios)} scenarios to {args.save_bank} (bank_id={bank_id})") - - -def start_opponent(bench_scenario: BenchScenario, override: Optional[str] = None) -> Optional[str]: - """Who a start is played against: `override` (`--opponent`), else the opponent its situation - came from (its source match's config_b). None when that config does not exist here (a - start harvested from another branch's strategies) or the start has no source match.""" - name = override or bench_scenario.scenario.config_b_name - if name is None: - return None - try: - return _resolve_config_name(name) - except ValueError: - return None - - -PLAY_DIR = "scenario_play" # under REPLAY_BASE_PATH: where --play keeps its replays - - -def _intention_lines(records: Iterable[dict], t0: float) -> list[tuple[float, str]]: - """The candidate's tactic assignments from its match log, one line per change, on - the replay's clock (seconds from its first frame, `t0` in sim time).""" - out = [] - for r in records: - if r.get("event") == "intention": - t = r["sim_time"] - t0 - out.append((t, f"{t:7.2f} tactic {r['tactic_id']} = {r.get('note') or '?'} {r['robot_ids']}")) - return out - - -def merge_timeline(trace_lines: list[str], intention_lines: list[tuple[float, str]]) -> list[str]: - """`replay_trace.trace` lines and `_intention_lines` in time order; at the same time, - the trace line (the state) comes before the tactic change it led to.""" - rows = [(float(line.split()[0]), 0, line) for line in trace_lines] - rows += [(t, 1, line) for t, line in intention_lines] - return [line for _t, _k, line in sorted(rows, key=lambda r: (r[0], r[1]))] - - -def play_start( - bench_scenario: BenchScenario, - *, - candidate: str, - opponent: str, - horizon_s: float, - seed: int = 0, - render: Optional[str] = None, - every: int = 15, -) -> dict: - """Play one start (`jittered` by `seed`) with a replay and match log recorded, and - return its result, the timeline and where the files are.""" - tag = f"{_resolve_config_name(candidate)[6:-16]}_vs_{_resolve_config_name(opponent)[6:-16]}_s{seed}" - name = f"{PLAY_DIR}/{bench_scenario.scenario_id}/{tag}" - r = score_scenario( - jittered(bench_scenario, seed), - candidate_config=candidate, - opponent_config=opponent, - horizon_s=horizon_s, - record=name, - ) - out = {"scenario_id": bench_scenario.scenario_id, "seed": seed, "result": _result_dict(r), "timeline": []} - npz = REPLAY_BASE_PATH / f"{name}.npz" - if r.error is not None or not npz.exists(): - return out - log = npz.with_suffix(".intentions.jsonl") - ts = np.load(npz)["ts"] - records = [json.loads(line) for line in log.read_text().splitlines()] if log.exists() else [] - timeline = merge_timeline(trace(npz, 0.0, math.inf, every), _intention_lines(records, float(ts[0]))) - trace_path = npz.with_suffix(".trace.txt") - trace_path.write_text("\n".join(timeline) + "\n") - out.update(timeline=timeline, replay=str(npz), log=str(log), trace=str(trace_path)) - # imported here: matplotlib and pygame's drawing aren't needed to score a bank - from utama_core.analysis.render_clip import render_clip - from utama_core.analysis.render_window import render_window - - if render == "png": - out["render"] = render_window(npz, float(ts[0]), float(ts[-1]), npz.with_suffix(".png")) - elif render == "mp4": - out["render"] = render_clip(npz, float(ts[0]), float(ts[-1]), npz.with_suffix(".mp4")) - return out - - -def _print_play(play: dict, lead_in_s: float) -> None: - r = play["result"] - print(f"\n=== {play['scenario_id']} (seed {play['seed']}) ===") - if r.get("error"): - print(f"error: {r['error']}") - return - print( - f"outcome {r['outcome']}{' (stalled)' if r.get('stalled') else ''}{' (foul)' if r.get('foul') else ''}; " - f"scored after a {lead_in_s:g} s lead-in" - ) - print("signals: " + ", ".join(f"{k} {v:g}" for k, v in (r.get("signals") or {}).items())) - print("\n".join(play["timeline"])) - for key in ("replay", "log", "trace", "render"): - if key in play: - print(f"{key}: {play[key]}") - - -def _start_result(bench_scenario: BenchScenario, config: str, opponent: str, horizon_s: float) -> dict: - return _result_dict( - score_scenario(bench_scenario, candidate_config=config, opponent_config=opponent, horizon_s=horizon_s) - ) - - -def _result_dict(r) -> dict: - return { - "outcome": int(r.outcome), - "foul": bool(r.foul), - "stalled": bool(r.stalled), - "error": r.error, - "signals": r.signals, - } - - -def _runs( - bench_scenario: BenchScenario, - config: str, - opponent: str, - horizon_s: float, - repeats: int, - plan: Optional[dict] = None, - fresh: Optional[dict] = None, -) -> dict: - """`config` vs `opponent` from `repeats` jittered starts (seed 0 = as authored). With - `--reuse`, `plan` maps `(config, seed)` to `(key, stored record or None)`: a start with - a record is not played, and every start played goes into `fresh` under its key.""" - results = [] - for seed in range(repeats): - key, record = (plan or {}).get((config, seed), (None, None)) - if record is None: - record = {"result": _start_result(jittered(bench_scenario, seed), config, opponent, horizon_s)} - if key is not None and fresh is not None: - fresh[key] = record - results.append(record["result"]) - return { - "outcomes": [r["outcome"] for r in results], - "signals": [r.get("signals") for r in results], - "fouls": sum(r["foul"] for r in results), - "stalls": sum(r["stalled"] for r in results), - "errors": [r["error"] for r in results if r["error"]], - } - - -def _load_against(path: Path, *, opponent: str, horizon_s: float, repeats: int) -> tuple[dict[str, dict], dict]: - """Baseline outcomes and signals from an earlier run's JSON (typically another commit), - keyed by scenario id. Refuses a file scored against a different opponent/horizon/repeats: - those outcomes are not comparable.""" - payload = json.loads(path.read_text()) - for key, want in (("opponent", opponent), ("horizon_s", horizon_s), ("repeats", repeats)): - if payload.get(key) != want: - raise SystemExit(f"--against-results {path}: {key} is {payload.get(key)!r}, this run uses {want!r}") - runs = { - row["scenario_id"]: {"outcomes": row["candidate_outcomes"], "signals": row.get("candidate_signals")} - for row in payload["results"] - if not row.get("candidate_errors") - } - source = {"path": str(path), "git_revision": payload.get("git_revision"), "candidate": payload.get("candidate")} - return runs, source - - -def _score_one( - item: tuple[BenchScenario, Optional[dict]], - *, - candidate: str, - opponent: str, - horizon_s: float, - repeats: int, - baseline, -) -> tuple[dict, Optional[dict], dict]: - bench_scenario, plan = item - enable_sim_reuse() # a worker plays many starts: keep its sim subprocess between them - fresh: dict = {} - opponent = start_opponent(bench_scenario, opponent) - cand = _runs(bench_scenario, candidate, opponent, horizon_s, repeats, plan, fresh) - base = _runs(bench_scenario, baseline, opponent, horizon_s, repeats, plan, fresh) if baseline else None - return cand, base, fresh - - -class _Reuse: - """`--reuse`'s bookkeeping for one bench run: every start's key, the records it may - reuse, the ones it replays to check, and what it played.""" - - def __init__(self, scenarios, configs, opponent: Optional[str], horizon_s: float, repeats: int, spot_check: float): - self.args = (scenarios, configs, opponent, horizon_s, repeats) - self.cache = match_cache.MatchCache() - self.keys = self._keys() - self.stored = {k: rec for k in self.keys.values() if (rec := self.cache.get(k)) is not None} - self.spot_checked = match_cache.spot_check_sample(self.stored, spot_check) - self.used: set[str] = set() # reused records, for eviction after a mismatch - self.fresh: dict[str, dict] = {} - - def _keys(self) -> dict[tuple[str, str, int], str]: - scenarios, configs, opponent, horizon_s, repeats = self.args - graph = CodeGraph() - return { - (bs.scenario_id, config, seed): bench_key( - graph, - jittered(bs, seed).to_dict(), - _resolve_config_name(config), - start_opponent(bs, opponent), - horizon_s=horizon_s, - ) - for bs in scenarios - for config in configs - for seed in range(repeats) - } - - def plan(self, bench_scenario: BenchScenario) -> dict: - out = {} - for (sid, config, seed), key in self.keys.items(): - if sid != bench_scenario.scenario_id: - continue - record = None if key in self.spot_checked else self.stored.get(key) - out[(config, seed)] = (key, record) - if record is not None: - self.used.add(key) - return out - - def finish(self) -> dict: - """Compare the spot-checks, store what was played, and report counts.""" - mismatches = [ - k for k in self.spot_checked if k in self.fresh and match_cache.differences(self.stored[k], self.fresh[k]) - ] - if mismatches: - for k in mismatches + sorted(self.used): - self.cache.evict(k) - print( - f"\n*** --reuse SPOT-CHECK MISMATCH: {len(mismatches)} replayed start(s) differ from their stored " - f"records. The fingerprint missed something they depend on; those records and the {len(self.used)} " - "this run reused are evicted, and its result can't be trusted: rerun without --reuse. ***", - flush=True, - ) - elif self._keys() != self.keys: - print("\n--reuse: the code changed while this run played; storing nothing", flush=True) - else: - for k, record in self.fresh.items(): - if not record["result"]["error"] and k not in self.spot_checked: - self.cache.put(k, record) - checked = len([k for k in self.spot_checked if k in self.fresh]) - print( - f"--reuse: {len(self.used)} start(s) reused, {len(self.fresh)} played, spot-check " - f"{checked - len(mismatches)}/{checked} identical", - flush=True, - ) - return { - "reused": len(self.used), - "played": len(self.fresh), - "spot_checked": checked, - "mismatches": len(mismatches), - } - - -def _score( - scenarios: list[BenchScenario], - *, - candidate: str, - opponent: str, - horizon_s: float, - repeats: int, - baseline: Optional[str] = None, - against: Optional[dict[str, dict]] = None, - workers: int = 1, - stop_at_t: Optional[float] = None, - check_every: int = 100, - reuse: Optional[_Reuse] = None, -) -> tuple[list[dict], Optional[str]]: - """Candidate outcomes per scenario, paired with the baseline's on the same jittered - starts: a `baseline` config run here, or `against` outcomes from an earlier run. - - With `stop_at_t`, scenarios are scored in a shuffled order (a bank is ordered by match - and family, so its first starts are not a fair sample), `check_every` at a time, and - scoring stops at a check where `_stop_reason` gives one; that reason is returned with - the rows (None when every scenario was scored).""" - rows = [] - total = len(scenarios) - play = functools.partial( - _score_one, candidate=candidate, opponent=opponent, horizon_s=horizon_s, repeats=repeats, baseline=baseline - ) - if stop_at_t is not None: - scenarios = random.Random(0).sample(scenarios, len(scenarios)) - batches = [scenarios[i : i + check_every] for i in range(0, len(scenarios), check_every)] - else: - batches = [scenarios] - for batch in batches: - items = [(bs, reuse.plan(bs) if reuse else None) for bs in batch] - - def results(items=items): - # a generator, so `_score_batch` prints each start as it comes back - for cand, base, fresh in _parallel_map(play, items, workers): - if reuse: - reuse.fresh.update(fresh) - yield cand, base - - _score_batch(rows, batch, results(), against, total) - if stop_at_t is not None and len(rows) < total: - deltas = [r["delta"] for r in rows if r["delta"] is not None] - stopped = _stop_reason(deltas, stop_at_t) - if stopped: - print(f"Stopped after {len(rows)} of {total} scenarios ({stopped}): {_overall(rows)}", flush=True) - return rows, stopped - return rows, None - - -# Futility: stop once the mean delta is confidently below FUTILE_BELOW, i.e. its upper -# bound |mean| + FUTILE_Z * stderr is. 0.15 is about the smallest mean delta a full -# bank_v5 pass detects at |t| >= 4 (4 * 1.02 / sqrt(846) = 0.14, 1.02 the spread of -# per-start deltas), so a candidate stopped as futile would not have been detected by -# the full pass either. 2.5 is a one-sided 5% bound split over the 8 checks of a bank_v5 -# pass (0.05 / 8). Calibration: docs/STRATEGY_DEVELOPMENT.md, "--stop-at-t". -FUTILE_BELOW = 0.15 -FUTILE_Z = 2.5 - - -def _stop_reason(deltas: list[float], stop_at_t: float) -> Optional[str]: - """Why to stop scoring: "detected" once |t| of `deltas` reaches `stop_at_t`, "futile" - once the difference is confidently smaller than FUTILE_BELOW, else None.""" - if len(deltas) < 2: - return None - if abs(_t(deltas)) >= stop_at_t: - return "detected" - stderr = statistics.stdev(deltas) / math.sqrt(len(deltas)) - if abs(statistics.fmean(deltas)) + FUTILE_Z * stderr < FUTILE_BELOW: - return "futile" - return None - - -def _t(deltas: list[float]) -> float: - """mean / stderr of `deltas`; infinite when every delta is the same nonzero value.""" - if len(deltas) < 2: - return 0.0 - mean = statistics.fmean(deltas) - stderr = statistics.stdev(deltas) / math.sqrt(len(deltas)) - if stderr == 0: - return 0.0 if mean == 0 else math.copysign(math.inf, mean) - return mean / stderr - - -def _score_batch(rows: list[dict], batch: list[BenchScenario], results: Iterable, against, total: int) -> None: - for bench_scenario, (cand, base) in zip(batch, results): - index = len(rows) + 1 - sid = bench_scenario.scenario_id - print(f"[{index}/{total}] {sid} ({bench_scenario.provenance.family.value})", flush=True) - base_run = base or (against or {}).get(sid) - base_outcomes = base_run["outcomes"] if base_run else None - delta = signal_deltas = None - # a run that errored (e.g. the start could not be set up) is not an outcome - if base_outcomes is not None and not cand["errors"] and not (base and base["errors"]): - delta = statistics.fmean(cand["outcomes"]) - statistics.fmean(base_outcomes) - signal_deltas = _signal_deltas(cand.get("signals"), base_run.get("signals")) - print( - f" candidate={cand['outcomes']} baseline={base_outcomes}" - + (f" delta={delta:+.2f}" if delta is not None else "") - + (f" stalls={cand['stalls']}" if cand["stalls"] else ""), - flush=True, - ) - rows.append( - { - "scenario_id": sid, - "family": bench_scenario.provenance.family.value, - "situation": bench_scenario.provenance.situation, - "trigger": bench_scenario.provenance.trigger.value, - "perspective": bench_scenario.provenance.perspective, - "candidate_outcomes": cand["outcomes"], - "baseline_outcomes": base_outcomes, - "delta": delta, - "candidate_signals": cand.get("signals"), - "baseline_signals": base_run.get("signals") if base_run else None, - "signal_deltas": signal_deltas, - "candidate_fouls": cand["fouls"], - "candidate_stalls": cand["stalls"], - "baseline_fouls": base["fouls"] if base else None, - "baseline_stalls": base["stalls"] if base else None, - "candidate_errors": cand["errors"], - "baseline_errors": base["errors"] if base else [], - } - ) - - -def _signal_deltas(cand: Optional[list], base: Optional[list]) -> Optional[dict[str, float]]: - """Per signal, the candidate's mean over the jittered starts minus the baseline's; None - when either side has no signals (an older results file).""" - if not cand or not base or any(s is None for s in cand + base): - return None - return {name: statistics.fmean(s[name] for s in cand) - statistics.fmean(s[name] for s in base) for name in SIGNALS} - - -def _signal_summary(rows: list[dict]) -> dict[str, dict]: - """Per signal: mean paired delta over the scenarios that have one, its t, and how many - scenarios it changed. Like the outcome delta, |t| under about 2 is within chance.""" - paired = [r["signal_deltas"] for r in rows if r.get("signal_deltas")] - summary = {} - for name in SIGNALS: - deltas = [d[name] for d in paired] - summary[name] = { - "mean_delta": statistics.fmean(deltas) if deltas else None, - "t": _t(deltas) if len(deltas) > 1 else None, - "changed": sum(1 for d in deltas if d != 0), - "n_paired": len(deltas), - } - return summary - - -def _type_order(situation: str) -> int: - return TYPES.index(situation) if situation in TYPES else len(TYPES) - - -def _goal_rates(outcomes: list[int]) -> tuple[float, float]: - """(P(for), P(against)) over one start's runs: the first goal within the horizon.""" - return ( - statistics.fmean(o == ScenarioOutcome.GOAL_FOR for o in outcomes), - statistics.fmean(o == ScenarioOutcome.GOAL_AGAINST for o in outcomes), - ) - - -def _mean_se(values: list[float]) -> tuple[float, Optional[float]]: - """Mean and its standard error; no standard error from one value.""" - return statistics.fmean(values), statistics.stdev(values) / math.sqrt(len(values)) if len(values) > 1 else None - - -def situation_effect(rows: list[dict], weights: dict) -> dict: - """The candidate's effect on goals per match (module docstring), from the paired starts of - `rows` that have a situation type. Per type: n starts; the candidate's and the baseline's - P(for) and P(against); the mean paired difference of each, with its standard error across - the type's starts; how often the type happens per match under `weights` - (`goal_ledger.situation_rates`); and what it adds to goals scored, conceded and the goal - difference per match. The total's standard error takes the types as independent, and is - None when a type has a single start.""" - by_type: dict[str, list[dict]] = {} - for r in rows: - if ( - r.get("situation") - and r["baseline_outcomes"] is not None - and not (r["candidate_errors"] or r["baseline_errors"]) - ): - by_type.setdefault(r["situation"], []).append(r) - types = {} - for typ in sorted(by_type, key=_type_order): - cand = [_goal_rates(r["candidate_outcomes"]) for r in by_type[typ]] - base = [_goal_rates(r["baseline_outcomes"]) for r in by_type[typ]] - d_for, se_for = _mean_se([c[0] - b[0] for c, b in zip(cand, base)]) - d_against, se_against = _mean_se([c[1] - b[1] for c, b in zip(cand, base)]) - d_net, se_net = _mean_se([(c[0] - c[1]) - (b[0] - b[1]) for c, b in zip(cand, base)]) - f = per_match(weights, typ) - types[typ] = { - "n": len(cand), - "per_match": f, - "candidate_p_for": statistics.fmean(c[0] for c in cand), - "candidate_p_against": statistics.fmean(c[1] for c in cand), - "baseline_p_for": statistics.fmean(b[0] for b in base), - "baseline_p_against": statistics.fmean(b[1] for b in base), - "delta_p_for": d_for, - "delta_p_for_se": se_for, - "delta_p_against": d_against, - "delta_p_against_se": se_against, - "scored_per_match": f * d_for, - "conceded_per_match": f * d_against, - "net_per_match": f * d_net, - "net_per_match_se": None if se_net is None else f * se_net, - } - ses = [t["net_per_match_se"] for t in types.values()] - total = { - key: sum(t[key] for t in types.values()) for key in ("scored_per_match", "conceded_per_match", "net_per_match") - } - total["net_per_match_se"] = None if None in ses else math.sqrt(sum(se * se for se in ses)) - return {"types": types, "total": total} - - -def load_weights(path: Path) -> dict: - """A situation rates file (`goal_ledger --rates`), with the `path` it came from.""" - return {"path": str(path), **json.loads(Path(path).read_text())} - - -def _effect_line(effect: dict, weights: dict) -> Optional[str]: - """One line: the effect on goal difference per match, its standard error, and the weights.""" - if not effect["types"]: - return None - total = effect["total"] - se = total["net_per_match_se"] - return ( - f"Effect on goal difference per match: {total['net_per_match']:+.2f}" - + (f" ± {se:.2f}" if se is not None else "") - + f" (scored {total['scored_per_match']:+.2f}, conceded {total['conceded_per_match']:+.2f}); " - f"weights {Path(weights['path']).name}, a match being {weights['live_min_per_match']:.1f} live minutes" - ) - - -def _aggregate_by_situation(rows: list[dict]) -> list[dict]: - """Stalls and signals per situation type (per family for a start with none).""" - grouped: dict[str, list[dict]] = {} - for row in rows: - grouped.setdefault(row.get("situation") or row["family"], []).append(row) - return [ - { - "situation": key, - "n_scenarios": len(grouped[key]), - "candidate_stalls": sum(r["candidate_stalls"] for r in grouped[key]), - "signals": _signal_summary(grouped[key]), - } - for key in sorted(grouped, key=_type_order) - ] - - -def _overall(rows: list[dict]) -> Optional[str]: - """One line: mean delta over scenarios, its standard error, and t = mean / stderr. - |t| under about 2 is within chance; the sign says which side was better.""" - deltas = [r["delta"] for r in rows if r["delta"] is not None] - if not deltas: - return None - mean = statistics.fmean(deltas) - if len(deltas) < 2: - return f"Overall mean delta {mean:+.3f} over 1 scenario" - stderr = statistics.stdev(deltas) / math.sqrt(len(deltas)) - if stderr == 0: - return f"Overall mean delta {mean:+.3f}, the same on all {len(deltas)} scenarios" - return ( - f"Overall mean delta {mean:+.3f} (stderr {stderr:.3f}, t {mean / stderr:+.2f}) over {len(deltas)} " - f"scenarios; |t| under ~2 is within chance" - ) - - -def _fmt(x: Optional[float], spec: str = "+.2f") -> str: - return "-" if x is None else format(x, spec) - - -def _signal_cell(summary: dict) -> str: - if summary["mean_delta"] is None: - return "-" - t = summary["t"] - if t is None or not math.isfinite(t): - return _fmt(summary["mean_delta"]) - return f"{_fmt(summary['mean_delta'])} (t {t:+.1f})" - - -def _signal_report(payload: dict) -> list[str]: - """The signals table: per signal, the mean paired delta per start and its t, overall and - per situation.""" - if not payload.get("signals") or not any(s["n_paired"] for s in payload["signals"].values()): - return [] - groups = [("all", payload["signals"])] + [(a["situation"], a["signals"]) for a in payload["aggregates"]] - lines = [ - "", - "## Signals", - "", - "Mean paired delta per start (candidate minus baseline) of the candidate's signals over the " - "scored window, see `docs/signals.md`. More is usually better for " - + ", ".join(n for n, d in SIGNALS.items() if d > 0) - + "; less for " - + ", ".join(n for n, d in SIGNALS.items() if d < 0) - + ".", - "", - "| Situation | " + " | ".join(SIGNALS) + " |", - "|---|" + "---:|" * len(SIGNALS), - ] - for label, signals in groups: - lines.append(f"| {label} | " + " | ".join(_signal_cell(signals[name]) for name in SIGNALS) + " |") - return lines - - -def _markdown_report(payload: dict) -> str: - baseline = payload["baseline"] or ( - f"results of `{payload['against']['candidate']}` at `{payload['against']['git_revision']}` " - f"({payload['against']['path']})" - if payload.get("against") - else None - ) - lines = [ - "# Scenario bench report", - "", - f"Generated: {payload['generated_at_utc']} ", - f"Git revision: `{payload['git_revision'] or 'unknown'}` ", - f"Candidate: `{payload['candidate']}` ", - f"Baseline: {baseline or 'none'} ", - "Opponent: " - + (f"`{payload['opponent']}`" if payload["opponent"] else "each start's own (its source match's config_b)") - + " ", - f"Horizon: {payload['horizon_s']}s, {payload.get('repeats', 1)} jittered starts per scenario ", - f"Weights: `{payload['weights']['path']}` ({payload['weights']['matches']} matches; a match is " - f"{payload['weights']['live_min_per_match']:.1f} live minutes) ", - f"Scenarios scored: {payload.get('n_scored', payload['n_scenarios'])} of {payload['n_scenarios']}" - + (f" (stopped early: {payload['stopped']})" if payload.get("stopped") else "") - + ( - f"; {payload['skipped_no_opponent']} more skipped, their opponent not on this branch" - if payload.get("skipped_no_opponent") - else "" - ), - "", - "A start's outcome is the first goal within the horizon: for the candidate, against it, or none. " - "Per situation type, P(for) and P(against) are means over its starts, and the change is paired " - "(candidate minus baseline on the same starts, ± one standard error across them). Per match, the " - "change is weighted by how often the type happens in a match under the weights. A goal counts for " - "every situation in the 10 s before it, so goals per match are over-counted (about 1.2x under sim " - "weights, 1.6x under real ones). Stalls are counted, not ranked. A screen, not a ranking: " - "round-robins confirm.", - "", - *([f"**{_effect_line(payload['effect'], payload['weights'])}**", ""] if payload["effect"]["types"] else []), - "## By situation", - "", - "| Situation | N | Per match | Candidate P(for) / P(against) | Baseline P(for) / P(against) | " - "ΔP(for) | ΔP(against) | Scored /match | Conceded /match | Difference /match |", - "|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|", - ] - for typ, t in payload["effect"]["types"].items(): - lines.append( - f"| {typ} | {t['n']} | {t['per_match']:.2f} | {t['candidate_p_for']:.2f} / {t['candidate_p_against']:.2f} | " - f"{t['baseline_p_for']:.2f} / {t['baseline_p_against']:.2f} | " - f"{_fmt(t['delta_p_for'])} ± {_fmt(t['delta_p_for_se'], '.2f')} | " - f"{_fmt(t['delta_p_against'])} ± {_fmt(t['delta_p_against_se'], '.2f')} | " - f"{_fmt(t['scored_per_match'])} | {_fmt(t['conceded_per_match'])} | " - f"{_fmt(t['net_per_match'])} ± {_fmt(t['net_per_match_se'], '.2f')} |" - ) - stalls = [f"{a['situation']} {a['candidate_stalls']}" for a in payload["aggregates"] if a["candidate_stalls"]] - lines += ["", f"Candidate stalls: {', '.join(stalls) if stalls else 'none'}."] - - lines.extend(_signal_report(payload)) - lines.extend( - [ - "", - "## Per-scenario", - "", - "Outcomes on the scorer's ordinal scale (GOAL_AGAINST=-3 ... NEUTRAL=0 ... GOAL_FOR=+3).", - "", - "| Scenario | Situation | Candidate | Baseline | Delta | Stalls |", - "|---|---|---|---|---:|---:|", - ] - ) - for row in payload["results"]: - lines.append( - f"| {row['scenario_id']} | {row.get('situation') or row['family']} | {row['candidate_outcomes']} | " - f"{row['baseline_outcomes'] if row['baseline_outcomes'] is not None else '-'} | {_fmt(row['delta'])} | " - f"{row['candidate_stalls']} |" - ) - - errors = [r for r in payload["results"] if r["candidate_errors"] or r["baseline_errors"]] - lines.extend(["", "## Errors", ""]) - if not errors: - lines.append("None.") - else: - for row in errors: - lines.append( - f"- `{row['scenario_id']}`: candidate={row['candidate_errors']} baseline={row['baseline_errors']}" - ) - - if payload.get("harvest_report"): - hr = payload["harvest_report"] - lines.extend( - [ - "", - "## Harvest", - "", - f"Matches seen: {hr.get('matches_seen', 0)}, trusted: {hr.get('matches_trusted', 0)}, " - f"untrusted (dropped): {hr.get('matches_untrusted', 0)}. " - f"Scenarios harvested: {hr.get('scenarios_harvested', 0)}.", - ] - ) - - return "\n".join(lines) - - -def _parse_where(parser: argparse.ArgumentParser, terms: Optional[list[str]]) -> dict[str, set[str]]: - where: dict[str, set[str]] = {} - for term in terms or []: - key, sep, values = term.partition("=") - if not sep or key not in TAGS or not values: - parser.error(f"--where {term!r}: expected TAG=VALUE[,VALUE] with TAG one of {', '.join(TAGS)}") - where[key] = set(values.split(",")) - return where - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - parser.add_argument("--candidate", help="candidate strategy config, e.g. build_split_shape_kernel_strategy") - parser.add_argument("--baseline", help="baseline strategy config to diff against, run in this process") - parser.add_argument( - "--against-results", - type=Path, - default=None, - help="diff against the candidate outcomes in an earlier run's JSON instead of a --baseline config " - "(A/B across commits: run once at each commit, pass the first run's JSON to the second)", - ) - parser.add_argument( - "--repeats", - type=int, - default=1, - help="jittered starts per scenario (seed 0 = as authored; default 1: more scenarios beat more repeats)", - ) - parser.add_argument( - "--stop-at-t", - type=float, - default=None, - help="score a shuffled sample 100 scenarios at a time and stop once |t| reaches this (4 is a safe choice: " - "t is looked at repeatedly, so 2 would give false alarms), or once the mean delta is confidently under " - f"{FUTILE_BELOW} (futile); for screening candidates, not for a baseline", - ) - parser.add_argument( - "--opponent", - help="play every start against this config (default: each start's own opponent, its source match's config_b)", - ) - parser.add_argument( - "--horizon", - type=float, - default=WINDOW_S, - help=f"sim seconds ticked per scenario (default {WINDOW_S:g}, the goal ledger's window)", - ) - parser.add_argument( - "--weights", - type=Path, - default=DEFAULT_WEIGHTS, - help="situation rates to weight the effect with (goal_ledger --rates; default: real Division B, " - f"{DEFAULT_WEIGHTS.name}; ours: situation_rates_sim.json)", - ) - parser.add_argument( - "--harvest-from", - type=Path, - default=None, - help="a completed round_robin.py run dir to harvest restart scenarios from", - ) - parser.add_argument( - "--per-situation", - type=int, - default=40, - help="with --harvest-from, keep up to this many starts of each situation type, kickoffs half as many " - "(scenario_harvester.sample_by_situation; default 40)", - ) - parser.add_argument("--families", nargs="+", default=None, help="restrict to these ScenarioFamily values") - parser.add_argument( - "--where", - nargs="+", - default=None, - metavar="TAG=VALUE[,VALUE]", - help="keep only starts whose situation tags match, e.g. third=defensive ball=theirs,loose " - f"(tags: {', '.join(TAGS)}; see start.start_tags and docs/pitch_zones.md)", - ) - parser.add_argument( - "--list-scenarios", action="store_true", help="print the loaded bank with each start's tags, and exit" - ) - parser.add_argument( - "--play", - nargs="+", - default=None, - metavar="ID", - help="play these starts of the loaded bank (one per --repeats seed) for --candidate against its opponent, " - "print each one's outcome, signals and timeline, and keep its replay under replays/scenario_play/", - ) - parser.add_argument( - "--render", - choices=("png", "mp4"), - default=None, - help="with --play, also draw each start's whole window: trails (png) or a clip (mp4, needs ffmpeg)", - ) - parser.add_argument( - "--load-bank", - type=Path, - default=None, - help="load scenarios from a persisted bank JSON (see start.save_bank) " - "instead of hand-authored/--harvest-from", - ) - parser.add_argument( - "--save-bank", - type=Path, - default=None, - help="write the loaded scenario set (after --families filtering) to this path as a persisted bank JSON", - ) - parser.add_argument( - "--merge-into", - type=Path, - default=None, - help="an existing bank: drop harvested scenarios that duplicate it (start.is_near_duplicate), " - "and --save-bank writes it plus the new ones, as a new bank", - ) - parser.add_argument("--workers", type=int, default=1, help="processes to score scenarios on (default 1)") - parser.add_argument( - "--reuse", - action="store_true", - help="take each start's outcome from replays/match_cache/ when the code it runs is unchanged (see above)", - ) - parser.add_argument( - "--spot-check", - type=float, - default=0.05, - help="with --reuse, the fraction of reusable starts to replay and compare (default 0.05, at least one)", - ) - parser.add_argument( - "--bank-id", - default=None, - help="bank_id to record when using --save-bank (default: the output filename's stem)", - ) - parser.add_argument("--output-dir", type=Path, default=Path("scenario_bench_results")) - args = parser.parse_args() - - if not args.list_scenarios: - if args.candidate is None: - parser.error("--candidate required unless --list-scenarios") - if args.baseline and args.against_results: - parser.error("--baseline and --against-results are alternatives") - if args.play is not None and (args.baseline or args.against_results or args.stop_at_t is not None): - parser.error("--play shows starts for --candidate alone; it takes no baseline or --stop-at-t") - if args.render is not None and args.play is None: - parser.error("--render needs --play") - args.where = _parse_where(parser, args.where) - if args.merge_into is not None and (args.harvest_from is None or args.save_bank is None): - parser.error("--merge-into needs --harvest-from and --save-bank") - - return args - - -def main() -> int: - args = parse_args() - scenarios, merged, harvest_report = _load_bank(args) - _save(args, merged + scenarios) - - if args.list_scenarios: - for bs in scenarios: - tags = " ".join(f"{k}={v}" for k, v in start_tags(bs).items()) - print(f"{bs.scenario_id:<58} {bs.provenance.situation or bs.provenance.family.value:<36} {tags}") - print(f"{len(scenarios)} starts") - if harvest_report: - print(f"\nHarvest: {harvest_report}") - return 0 - - if not scenarios: - print("No scenarios loaded (check --families / --harvest-from).", file=sys.stderr) - return 1 - - if args.play is not None: - by_id = {bs.scenario_id: bs for bs in scenarios} - unknown = [sid for sid in args.play if sid not in by_id] - if unknown: - print(f"Not in the loaded bank: {', '.join(unknown)} (see --list-scenarios)", file=sys.stderr) - return 1 - failed = False - for sid in args.play: - opponent = start_opponent(by_id[sid], args.opponent) - if opponent is None: - print(f"{sid}: its opponent is not on this branch; pass --opponent", file=sys.stderr) - failed = True - continue - for seed in range(args.repeats): - play = play_start( - by_id[sid], - candidate=args.candidate, - opponent=opponent, - horizon_s=args.horizon, - seed=seed, - render=args.render, - ) - _print_play(play, by_id[sid].lead_in_s) - failed |= play["result"]["error"] is not None - return 1 if failed else 0 - - generated_at = datetime.now(timezone.utc) - args.output_dir.mkdir(parents=True, exist_ok=True) - timestamp = generated_at.strftime("%Y%m%d_%H%M%S") - - n_loaded = len(scenarios) - scenarios = [bs for bs in scenarios if start_opponent(bs, args.opponent) is not None] - if len(scenarios) < n_loaded: - print(f"{n_loaded - len(scenarios)} of {n_loaded} starts skipped: their opponent is not on this branch") - weights = load_weights(args.weights) - - against, against_source = None, None - if args.against_results: - against, against_source = _load_against( - args.against_results, opponent=args.opponent, horizon_s=args.horizon, repeats=args.repeats - ) - configs = [args.candidate] + ([args.baseline] if args.baseline else []) - reuse = ( - _Reuse(scenarios, configs, args.opponent, args.horizon, args.repeats, args.spot_check) if args.reuse else None - ) - rows, stopped = _score( - scenarios, - candidate=args.candidate, - opponent=args.opponent, - horizon_s=args.horizon, - repeats=args.repeats, - baseline=args.baseline, - against=against, - workers=args.workers, - stop_at_t=args.stop_at_t, - reuse=reuse, - ) - reuse_report = reuse.finish() if reuse else None - effect = situation_effect(rows, weights) - - payload = { - "schema_version": SCHEMA_VERSION, - "generated_at_utc": generated_at.isoformat(), - "git_revision": _git_revision(), - "candidate": args.candidate, - "baseline": args.baseline, - "against": against_source, - "opponent": args.opponent, - "horizon_s": args.horizon, - "repeats": args.repeats, - "weights": weights, - "n_scenarios": len(scenarios), - "n_scored": len(rows), - "skipped_no_opponent": n_loaded - len(scenarios), - "stop_at_t": args.stop_at_t, - # why scoring ended early: "detected" (|t| reached stop_at_t) or "futile" (see - # FUTILE_BELOW); None when every scenario was scored - "stopped": stopped, - "reuse": reuse_report, - "effect": effect, - "aggregates": _aggregate_by_situation(rows), - "signals": _signal_summary(rows), - "results": rows, - "harvest_report": harvest_report, - } - - json_path = args.output_dir / f"scenario_bench_{timestamp}.json" - json_path.write_text(json.dumps(payload, indent=2)) - md_path = args.output_dir / f"scenario_bench_{timestamp}.md" - md_path.write_text(_markdown_report(payload)) - - print(f"\nWrote {json_path}\nWrote {md_path}") - if _effect_line(effect, weights): - print(_effect_line(effect, weights) + (f" (stopped early: {stopped})" if stopped else "")) - return 1 if reuse_report and reuse_report["mismatches"] else 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/tools/setup_worktree.sh b/tools/setup_worktree.sh new file mode 100755 index 00000000..3f086783 --- /dev/null +++ b/tools/setup_worktree.sh @@ -0,0 +1,47 @@ +#!/usr/bin/env bash +# setup_worktree.sh — prepare a git worktree of this repo to run tests and tools. +# +# Run inside the worktree (not the main checkout). It makes the worktree usable without a +# fresh `pixi install` or planner build: +# - symlinks .pixi to the main checkout's .pixi (the environment); +# - copies the gitignored compiled extensions (_fastpathplanner, _robosim .so files) from +# the main checkout. Without them the planner falls back to Python and results differ. +# Idempotent: re-running changes nothing that is already in place. Prints one line per action. +# +# Usage: tools/setup_worktree.sh +set -euo pipefail + +wt_root="$(git rev-parse --show-toplevel)" +main_root="$(git worktree list --porcelain | sed -n '1s/^worktree //p')" + +if [ "$wt_root" = "$main_root" ]; then + echo "skip: $wt_root is the main checkout; run this inside a worktree" + exit 0 +fi + +link="$wt_root/.pixi" +target="$main_root/.pixi" +if [ -L "$link" ] && [ "$(readlink "$link")" = "$target" ]; then + echo "ok: .pixi already links to $target" +elif [ -e "$link" ]; then + echo "skip: $link exists and is not the link to $target (remove it to relink)" +else + ln -s "$target" "$link" + echo "linked: .pixi -> $target" +fi + +for rel in \ + utama_core/motion_planning/src/fastpathplanning \ + utama_core/rsoccer_simulator/src/Simulators/robosim; do + for src in "$main_root/$rel"/_*.cpython-*.so; do + [ -e "$src" ] || continue + name="$(basename "$src")" + dst="$wt_root/$rel/$name" + if [ -e "$dst" ] && cmp -s "$src" "$dst"; then + echo "ok: $rel/$name" + else + cp -p "$src" "$dst" + echo "copied: $rel/$name" + fi + done +done diff --git a/tools/signal_report.py b/tools/signal_report.py index 9276cadb..15249ba5 100644 --- a/tools/signal_report.py +++ b/tools/signal_report.py @@ -4,15 +4,21 @@ Replays the run's saved matches through `turnover_breakdown.analyse_match` (which carries `chances`), so it works on runs recorded before chances were measured; it covers the matches -whose replay is in the run directory. Writes: +whose replay is in the run directory. Every figure lists the strategies in one order, by points +per match, top first. Writes: -- `signal_heatmap.png`: strategies (by points per match) x the main signals, each column coloured - by rank among the strategies, green where the value usually helps, with the value printed; -- `attack_defense.png`: shots created against danger conceded, and the openness of the shots a - strategy faces against its save rate, marker size by points per match; +- `results.png`: goals, shots and goals per shot, each for and against: goals per match is shots + times goals per shot, so it shows whether a strategy wins on volume or on quality; +- `signals.png`: one panel per main signal, every strategy a dot on the signal's own scale, the + median dashed; - `shot_quality.png`: goals per shot by distance and open goal mouth, over the whole run; -- `ball_losses.png`: real ball losses per match by kind (config_a matches only); -- `fouls.png`: fouls per match by rule, and stalled matches. +- `fouls.png`: fouls per match by rule (the ball going out is not a foul: its own panel), with + the stalled matches; +- `ball_losses.png`: real ball losses per match by kind, for strategies with at least + `MIN_LOSS_MATCHES` matches as config_a (losses are measured for config_a only). + +It also prints the figures' numbers as a markdown table, for the report and for reading without +the images. """ from __future__ import annotations @@ -33,8 +39,30 @@ if str(REPO_ROOT) not in sys.path: sys.path.insert(0, str(REPO_ROOT)) -from tools.evaluation.round_robin import strategy_table # noqa: E402 from utama_core.analysis import turnover_breakdown # noqa: E402 +from utama_core.analysis.strategy_table import strategy_table # noqa: E402 + +INK, INK_2, INK_3, SURFACE, GRID = "#0b0b0b", "#52514e", "#8a8984", "#fcfcfb", "#e4e3df" +FOR, AGAINST = "#2a78d6", "#eb6834" # categorical slots 1 and 2 +# Categorical slots in fixed order (validated adjacent-pair for stacks); never cycled. +SLOTS = ["#2a78d6", "#eb6834", "#1baf7a", "#eda100", "#e87ba4", "#008300", "#6250d6"] +MIN_LOSS_MATCHES = 5 + +plt.rcParams.update( + { + "figure.facecolor": SURFACE, + "axes.facecolor": SURFACE, + "axes.edgecolor": INK_3, + "axes.labelcolor": INK_2, + "axes.titlesize": 10, + "axes.titlecolor": INK, + "axes.spines.top": False, + "axes.spines.right": False, + "xtick.color": INK_2, + "ytick.color": INK, + "font.size": 9, + } +) def short(name: str) -> str: @@ -45,6 +73,10 @@ def points(row: dict) -> float: return (3 * row["wins"] + row["draws"]) / max(1, row["matches"]) +def _order(table: dict) -> list[str]: + return sorted(table, key=lambda n: -points(table[n])) + + def _per_match(key: str) -> Callable[[dict], Optional[float]]: return lambda r: r[key] / r["matches"] if r["matches"] else None @@ -53,133 +85,126 @@ def _chance(key: str) -> Callable[[dict], Optional[float]]: return lambda r: r["chances"].get(key) -def _per_chance_match(key: str) -> Callable[[dict], Optional[float]]: - return lambda r: r["chances"][key] / r["chances"]["matches"] if r["chances"]["matches"] else None - - -# (group, label, value, +1 higher usually helps / -1 lower does / 0 neither, format) -COLUMNS = [ - ("Attack", "shots", _per_match("shots"), 1, "{:.1f}"), - ("Attack", "entries", _per_match("attacking_third_entries"), 1, "{:.1f}"), - ("Attack", "regain>shot", _chance("regain_to_shot"), 1, "{:.0%}"), - ("Attack", "regain>shot s", _chance("regain_to_shot_s"), -1, "{:.1f}"), - ("Attack", "fk>shot", _chance("free_kick_to_shot"), 1, "{:.0%}"), - ("Attack", "shot dist m", _chance("shot_distance_m"), -1, "{:.1f}"), - ("Attack", "open goal", _chance("shot_open_goal"), 1, "{:.0%}"), - ("Ball", "passes", _per_match("completed_passes"), 0, "{:.0f}"), - ("Ball", "progress m", lambda r: r["pass_progress_m"], 1, "{:.2f}"), - ("Ball", "forward", lambda r: r["forward_pass_share"], 1, "{:.0%}"), - ( - "Ball", - "real losses", - lambda r: r["real_losses_as_a"] / r["matches_as_a"] if r["matches_as_a"] else None, - -1, - "{:.0f}", - ), - ("Defense", "GA", _per_match("goals_against"), -1, "{:.1f}"), - ("Defense", "danger s", _chance("danger_s_per_match"), -1, "{:.0f}"), - ("Defense", "open faced", _chance("faced_open_goal"), -1, "{:.0%}"), - ("Defense", "save", _chance("save_rate"), 1, "{:.0%}"), - ("Defense", "regains", _per_chance_match("regains"), 1, "{:.0f}"), - ("Health", "fouls", _per_match("fouls"), -1, "{:.1f}"), - ("Health", "stalled", lambda r: r["stalled"], -1, "{:.0f}"), +def _ratio(num: str, den: str) -> Callable[[dict], Optional[float]]: + return lambda r: r[num] / r[den] if r[den] else None + + +# (label, value, which way usually helps: "higher" / "lower" / "", format) +SIGNALS = [ + ("attacking-third entries / match", _per_match("attacking_third_entries"), "higher", "{:.1f}"), + ("regains followed by a shot", _chance("regain_to_shot"), "higher", "{:.0%}"), + ("free kicks followed by a shot", _chance("free_kick_to_shot"), "higher", "{:.0%}"), + ("shot distance (m)", _chance("shot_distance_m"), "lower", "{:.1f}"), + ("goal mouth open, own shots", _chance("shot_open_goal"), "higher", "{:.0%}"), + ("completed passes / match", _per_match("completed_passes"), "", "{:.0f}"), + ("metres gained per pass", lambda r: r["pass_progress_m"], "higher", "{:.2f}"), + ("danger conceded (s / match)", _chance("danger_s_per_match"), "lower", "{:.0f}"), + ("goal mouth open, shots faced", _chance("faced_open_goal"), "lower", "{:.0%}"), + ("save rate", _chance("save_rate"), "higher", "{:.0%}"), ] -def _ranked(values: list[Optional[float]], direction: int) -> list[float]: - """0..1 by rank among the non-missing values, 1 where it usually helps; 0.5 if missing.""" - present = sorted(v for v in values if v is not None) - out = [] - for v in values: - if v is None or len(present) < 2: - out.append(0.5) - continue - below = sum(p < v for p in present) + 0.5 * (sum(p == v for p in present) - 1) - r = below / (len(present) - 1) - out.append(r if direction >= 0 else 1 - r) - return out +def _ylabels(ax, names: list[str], table: dict, show: bool = True) -> None: + ax.set_yticks(range(len(names))) + if show: # shared y: clearing one panel's labels would clear them all, so hide them instead + ax.set_yticklabels([f"{short(n)} {points(table[n]):.2f}" for n in names]) + ax.set_ylim(len(names) - 0.5, -0.5) + ax.tick_params(axis="y", length=0, labelleft=show) + ax.spines["left"].set_visible(False) + ax.grid(axis="x", color=GRID, lw=0.8) + ax.set_axisbelow(True) -def heatmap(table: dict, path: Path) -> None: - names = sorted(table, key=lambda n: -points(table[n])) - rows = [table[n] for n in names] - cells = np.array([_ranked([c[2](r) for r in rows], c[3]) for c in COLUMNS]).T - fig, ax = plt.subplots(figsize=(1.0 * len(COLUMNS) + 3.5, 0.42 * len(rows) + 2.2)) - for j, col in enumerate(COLUMNS): - cmap = plt.get_cmap("Blues" if col[3] == 0 else "RdYlGn") - for i, r in enumerate(rows): - v = col[2](r) - shade = cells[i, j] * 0.6 + 0.2 if col[3] == 0 else cells[i, j] - ax.add_patch(plt.Rectangle((j, i), 1, 1, color=cmap(shade))) - ax.text(j + 0.5, i + 0.5, "-" if v is None else col[4].format(v), ha="center", va="center", fontsize=8) - ax.set_xlim(0, len(COLUMNS)) - ax.set_ylim(len(rows), 0) - ax.set_xticks(np.arange(len(COLUMNS)) + 0.5) - ax.set_xticklabels([c[1] for c in COLUMNS], rotation=40, ha="right", fontsize=9) - ax.set_yticks(np.arange(len(rows)) + 0.5) - ax.set_yticklabels([f"{short(n)} {points(table[n]):.2f}" for n in names], fontsize=9) - groups: dict[str, list[int]] = {} - for j, c in enumerate(COLUMNS): - groups.setdefault(c[0], []).append(j) - for g, js in groups.items(): - ax.text((js[0] + js[-1] + 1) / 2, -0.4, g, ha="center", va="bottom", fontsize=10, weight="bold") - ax.axvline(js[0], color="white", lw=3) - ax.tick_params(length=0) - for s in ax.spines.values(): - s.set_visible(False) - ax.set_title( - "Signals per strategy (rows by points per match; colour = rank among strategies, green where it usually " - "helps, blue = neither)", +def _dumbbell(ax, names, table, value_for, value_against, fmt) -> None: + for i, n in enumerate(names): + a, b = value_for(table[n]), value_against(table[n]) + if a is None or b is None: + continue + ax.plot([a, b], [i, i], color=INK_3, lw=2, solid_capstyle="round", zorder=1) + ax.scatter([b], [i], s=64, color=AGAINST, edgecolors=SURFACE, linewidths=2, zorder=2) + ax.scatter([a], [i], s=64, color=FOR, edgecolors=SURFACE, linewidths=2, zorder=3) + ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda v, _: fmt.format(v))) + + +def results(table: dict, path: Path) -> None: + names = _order(table) + fig, axes = plt.subplots(1, 3, figsize=(13, 0.36 * len(names) + 1.9), sharey=True) + panels = [ + ("goals per match", _per_match("goals_for"), _per_match("goals_against"), "{:.0f}"), + ("shots per match", _per_match("shots"), _per_match("shots_faced"), "{:.0f}"), + ("goals per shot", _ratio("goals_for", "shots"), _ratio("goals_against", "shots_faced"), "{:.0%}"), + ] + for k, (ax, (title, f, a, fmt)) in enumerate(zip(axes, panels)): + _dumbbell(ax, names, table, f, a, fmt) + _ylabels(ax, names, table, show=k == 0) + ax.set_title(title, loc="left") + axes[0].set_xlim(left=0) + axes[1].set_xlim(left=0) + axes[2].set_xlim(left=0) + handles = [ + plt.Line2D([], [], marker="o", ls="", color=FOR, markersize=8, label="for (scored, shots made)"), + plt.Line2D([], [], marker="o", ls="", color=AGAINST, markersize=8, label="against (conceded, shots faced)"), + ] + fig.legend(handles=handles, loc="upper left", ncol=2, frameon=False, bbox_to_anchor=(0.0, 1.0)) + fig.suptitle( + "Why each strategy gets its results: goals per match = shots x goals per shot " + "(strategies by points per match, the number after the name)", + x=0.0, + ha="left", + y=1.07, fontsize=10, - pad=40, + color=INK, ) fig.tight_layout() - fig.savefig(path, dpi=130) + fig.savefig(path, dpi=130, bbox_inches="tight") plt.close(fig) -def _scatter(ax, table, x, y, xlabel, ylabel, title): - names = [n for n in table if x(table[n]) is not None and y(table[n]) is not None] - xs, ys = [x(table[n]) for n in names], [y(table[n]) for n in names] - pts = [points(table[n]) for n in names] - sc = ax.scatter(xs, ys, s=[40 + 90 * p for p in pts], c=pts, cmap="viridis", alpha=0.8, edgecolors="k") - for n, a, b in zip(names, xs, ys): - ax.annotate(short(n), (a, b), fontsize=7, xytext=(4, 3), textcoords="offset points") - ax.axvline(float(np.median(xs)), color="grey", lw=0.8, ls="--") - ax.axhline(float(np.median(ys)), color="grey", lw=0.8, ls="--") - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - ax.set_title(title, fontsize=10) - return sc - - -def attack_defense(table: dict, path: Path) -> None: - fig, (a, b) = plt.subplots(1, 2, figsize=(14, 6)) - _scatter( - a, - table, - _chance("danger_s_per_match"), - _per_match("shots"), - "danger conceded (s per match the enemy holds the ball in our defensive third)", - "shots per match", - "Attack vs defense (top left: creates a lot, concedes little)", - ) - sc = _scatter( - b, - table, - _chance("faced_open_goal"), - _chance("save_rate"), - "open goal mouth on the shots faced", - "save rate", - "Shots allowed (left: the defense blocks the lane)", +def signals(table: dict, path: Path, cols: int = 5) -> None: + names = _order(table) + rows = -(-len(SIGNALS) // cols) + fig, axes = plt.subplots(rows, cols, figsize=(3.0 * cols + 2.2, (0.3 * len(names) + 1.1) * rows), sharey=True) + for k, (ax, (label, value, helps, fmt)) in enumerate(zip(axes.flat, SIGNALS)): + vals = [value(table[n]) for n in names] + present = [(i, v) for i, v in enumerate(vals) if v is not None] + ax.scatter( + [v for _, v in present], [i for i, _ in present], s=56, color=FOR, edgecolors=SURFACE, lw=2, zorder=3 + ) + if present: + ax.axvline(float(np.median([v for _, v in present])), color=INK_3, lw=1, ls="--", zorder=1) + lo = min(present, key=lambda p: p[1]) + hi = max(present, key=lambda p: p[1]) + for i, v in (lo, hi): # label only the extremes; the table has every value + ax.annotate( + fmt.format(v), + (v, i), + xytext=(0, 7), + textcoords="offset points", + ha="center", + fontsize=8, + color=INK_2, + ) + ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda v, _, f=fmt: f.format(v))) + ax.xaxis.set_major_locator(plt.MaxNLocator(4)) + _ylabels(ax, names, table, show=k % cols == 0) + ax.set_title(label + (f" ({helps} helps)" if helps else ""), loc="left", fontsize=9) + for ax in list(axes.flat)[len(SIGNALS) :]: + ax.set_visible(False) + fig.suptitle( + "Signals per strategy, each on its own scale (dashed: median of the 12; labels: lowest and highest)", + x=0.0, + ha="left", + fontsize=10, + color=INK, ) - fig.colorbar(sc, ax=[a, b], label="points per match", fraction=0.025) + fig.tight_layout() fig.savefig(path, dpi=130, bbox_inches="tight") plt.close(fig) DIST_BINS = [0.0, 1.5, 2.0, 2.5, 3.0, 3.5, 10.0] OPEN_BINS = [0.0, 0.25, 0.5, 0.75, 1.01] +MIN_CELL_SHOTS = 15 def shot_quality(shots: list[dict], path: Path) -> None: @@ -190,30 +215,58 @@ def shot_quality(shots: list[dict], path: Path) -> None: j = np.searchsorted(DIST_BINS, s["distance_m"], side="right") - 1 count[i, j] += 1 goals[i, j] += s["scored"] - rate = np.divide(goals, count, out=np.full_like(goals, np.nan), where=count > 0) - fig, ax = plt.subplots(figsize=(8, 4.5)) - im = ax.imshow(rate, cmap="RdYlGn", vmin=0, vmax=1, origin="lower", aspect="auto") + rate = np.divide(goals, count, out=np.full_like(goals, np.nan), where=count >= MIN_CELL_SHOTS) + fig, ax = plt.subplots(figsize=(8, 4.2)) + cmap = matplotlib.colors.LinearSegmentedColormap.from_list("blue", ["#cde2fb", "#6da7ec", "#256abf", "#0d366b"]) + cmap.set_bad(GRID) + im = ax.imshow(np.ma.masked_invalid(rate), cmap=cmap, vmin=0, vmax=1, origin="lower", aspect="auto") for i in range(rate.shape[0]): for j in range(rate.shape[1]): - if count[i, j]: - ax.text(j, i, f"{rate[i, j]:.0%}\nn={int(count[i, j])}", ha="center", va="center", fontsize=8) + n = int(count[i, j]) + if n >= MIN_CELL_SHOTS: + ax.text( + j, + i, + f"{rate[i, j]:.0%}\n{n} shots", + ha="center", + va="center", + fontsize=8, + color=SURFACE if rate[i, j] > 0.45 else INK, + ) + elif n: + ax.text(j, i, f"{n} shots", ha="center", va="center", fontsize=7, color=INK_2) ax.set_xticks(range(len(DIST_BINS) - 1)) ax.set_xticklabels([f"{a:g}-{b:g}" if b < 10 else f">{a:g}" for a, b in zip(DIST_BINS, DIST_BINS[1:])]) ax.set_yticks(range(len(OPEN_BINS) - 1)) ax.set_yticklabels([f"{a:.0%}-{min(b, 1):.0%}" for a, b in zip(OPEN_BINS, OPEN_BINS[1:])]) ax.set_xlabel("shot distance to goal centre (m)") ax.set_ylabel("goal mouth open at the shot") - ax.set_title(f"Goals per shot, all {len(shots)} shots of the run", fontsize=10) - fig.colorbar(im, ax=ax, label="goals per shot") + ax.tick_params(length=0) + for sp in ax.spines.values(): + sp.set_visible(False) + ax.set_title( + f"Goals per shot, all {len(shots)} shots of the run (grey: under {MIN_CELL_SHOTS} shots, no rate)", loc="left" + ) + fig.colorbar(im, ax=ax, label="goals per shot", format=plt.FuncFormatter(lambda v, _: f"{v:.0%}")) fig.tight_layout() fig.savefig(path, dpi=130) plt.close(fig) +def _stacked(ax, names, parts: list[tuple[str, list[float]]]) -> None: + left = np.zeros(len(names)) + for k, (label, vals) in enumerate(parts): + v = np.array(vals, dtype=float) + ax.barh( + range(len(names)), v, left=left, height=0.62, color=SLOTS[k], label=label, edgecolor=SURFACE, linewidth=2 + ) + left += v + + LOSS_KINDS = [ "tackled", - "shot_saved_or_blocked", "ball_out_after_kick", + "shot_saved_or_blocked", "pass_intercepted", "loose_ball_lost", "foul", @@ -222,65 +275,120 @@ def shot_quality(shots: list[dict], path: Path) -> None: def ball_losses(table: dict, path: Path) -> None: - names = sorted((n for n in table if table[n]["matches_as_a"]), key=lambda n: -points(table[n]))[::-1] - fig, ax = plt.subplots(figsize=(10, 0.35 * len(names) + 1.5)) - left = np.zeros(len(names)) - colors = plt.get_cmap("tab10") - for k, kind in enumerate(LOSS_KINDS): - vals = np.array( - [table[n]["real_loss_kinds_as_a"].get(kind, 0) / table[n]["matches_as_a"] for n in names], dtype=float + names = [n for n in _order(table) if table[n]["matches_as_a"] >= MIN_LOSS_MATCHES] + fig, ax = plt.subplots(figsize=(10, 0.36 * len(names) + 1.8)) + parts = [ + ( + kind.replace("_", " "), + [table[n]["real_loss_kinds_as_a"].get(kind, 0) / table[n]["matches_as_a"] for n in names], ) - ax.barh(range(len(names)), vals, left=left, color=colors(k), label=kind) - left += vals + for kind in LOSS_KINDS + ] + _stacked(ax, names, parts) ax.set_yticks(range(len(names))) - ax.set_yticklabels([f"{short(n)} (n={table[n]['matches_as_a']})" for n in names], fontsize=8) - ax.set_xlabel("real ball losses per match, as config_a (n = matches as config_a)") - ax.set_title("How each strategy loses the ball (top = most points)", fontsize=10) - ax.legend(fontsize=8, loc="lower right") + ax.set_yticklabels([f"{short(n)} ({table[n]['matches_as_a']} matches)" for n in names]) + ax.set_ylim(len(names) - 0.5, -0.5) + ax.tick_params(axis="y", length=0) + ax.spines["left"].set_visible(False) + ax.grid(axis="x", color=GRID, lw=0.8) + ax.set_axisbelow(True) + ax.set_xlabel("real ball losses per match (not won back within 1 s)") + ax.set_title( + f"How each strategy loses the ball (as config_a only; strategies with {MIN_LOSS_MATCHES}+ such matches)", + loc="left", + ) + ax.legend(ncol=4, frameon=False, loc="upper left", bbox_to_anchor=(0, -0.12 - 0.3 / max(1, len(names)))) fig.tight_layout() - fig.savefig(path, dpi=130) + fig.savefig(path, dpi=130, bbox_inches="tight") plt.close(fig) -def fouls(summary: dict, table: dict, path: Path, top: int = 7) -> None: - rules = list(summary["fouls"])[:top] - names = sorted(table, key=lambda n: -points(table[n])) - m = np.zeros((len(names), len(rules) + 1)) - for j, rule in enumerate(rules): - for key, n in summary["fouls"][rule]["by_tactic"].items(): +BALL_OUT = "out_of_bounds" + + +def _fouls_per_match(summary: dict, table: dict) -> dict[str, dict[str, float]]: + out: dict[str, dict[str, float]] = {n: {} for n in table} + for rule, info in summary["fouls"].items(): + for key, count in info["by_tactic"].items(): strategy = f"build_{key.split('/')[0]}_kernel_strategy" if strategy in table: - m[names.index(strategy), j] += n / table[strategy]["matches"] + out[strategy][rule] = out[strategy].get(rule, 0.0) + count / table[strategy]["matches"] + return out + + +def fouls(summary: dict, table: dict, path: Path, top: int = 5) -> None: + names = _order(table) + per = _fouls_per_match(summary, table) + rules = [r for r in summary["fouls"] if r != BALL_OUT] + shown = sorted(rules, key=lambda r: -summary["fouls"][r]["total"])[:top] + parts = [(r.replace("_", " "), [per[n].get(r, 0.0) for n in names]) for r in shown] + parts.append(("other", [sum(v for r, v in per[n].items() if r not in shown and r != BALL_OUT) for n in names])) + fig, (a, b) = plt.subplots( + 1, 2, figsize=(13, 0.36 * len(names) + 2.0), sharey=True, gridspec_kw={"width_ratios": [3, 1]} + ) + _stacked(a, names, parts) for i, n in enumerate(names): - m[i, -1] = table[n]["stalled"] - fig, ax = plt.subplots(figsize=(1.0 * len(rules) + 4, 0.38 * len(names) + 1.8)) - norm = m / np.maximum(m.max(axis=0), 1e-9) - ax.imshow(norm, cmap="Reds", vmin=0, vmax=1.2, aspect="auto") - for i in range(m.shape[0]): - for j in range(m.shape[1]): - v = m[i, j] - text = f"{int(v)}" if j == len(rules) else f"{v:.1f}" if v >= 1 or v == 0 else f"{v:.2f}" - ax.text(j, i, text, ha="center", va="center", fontsize=8) - ax.set_xticks(range(len(rules) + 1)) - ax.set_xticklabels(rules + ["stalled matches"], rotation=35, ha="right", fontsize=9) - ax.set_yticks(range(len(names))) - ax.set_yticklabels([short(n) for n in names], fontsize=9) - ax.set_title("Fouls per match by rule (both sides, the offending strategy), and stalled matches", fontsize=10) + total = sum(v for _, vals in parts for v in [vals[i]]) + stalled = table[n]["stalled"] + if stalled: + a.text(total + 0.1, i, f"{stalled} stalled", va="center", fontsize=8, color=INK_2) + _ylabels(a, names, table) + a.set_title("fouls per match, by rule", loc="left") + a.legend(ncol=6, frameon=False, loc="upper left", bbox_to_anchor=(0, -0.08 - 0.3 / len(names))) + b.barh(range(len(names)), [per[n].get(BALL_OUT, 0.0) for n in names], height=0.62, color=INK_3) + _ylabels(b, names, table, show=False) + b.set_title("ball out of play per match (not a foul)", loc="left") + fig.suptitle( + "Fouls by the offending strategy, and the matches it stalled in (each stall counts for both teams)", + x=0.0, + ha="left", + fontsize=10, + color=INK, + ) fig.tight_layout() - fig.savefig(path, dpi=130) + fig.savefig(path, dpi=130, bbox_inches="tight") plt.close(fig) +def markdown(table: dict) -> str: + """The figures' numbers, one row per strategy in the figures' order.""" + cols = [ + ("points / match", points, "{:.2f}"), + ("GF", _per_match("goals_for"), "{:.1f}"), + ("GA", _per_match("goals_against"), "{:.1f}"), + ("shots", _per_match("shots"), "{:.1f}"), + ("shots faced", _per_match("shots_faced"), "{:.1f}"), + ("goals / shot", _ratio("goals_for", "shots"), "{:.0%}"), + ("conceded / shot faced", _ratio("goals_against", "shots_faced"), "{:.0%}"), + ] + [(label, value, fmt) for label, value, _, fmt in SIGNALS] + lines = ["| strategy | " + " | ".join(c[0] for c in cols) + " |", "|---" * (len(cols) + 1) + "|"] + for n in _order(table): + cells = [] + for _, value, fmt in cols: + v = value(table[n]) + cells.append("-" if v is None else fmt.format(v)) + lines.append(f"| `{short(n)}` | " + " | ".join(cells) + " |") + return "\n".join(lines) + + def build(summary: dict, losses: list[dict], out: Path) -> dict: """Write every figure for `summary` (a round-robin's summary.json) and its replays' - `turnover_breakdown.analyse_match` records. Returns the strategy table plotted.""" + `turnover_breakdown.analyse_match` records. Returns the strategy table plotted, each row + with `shots_faced` added.""" out.mkdir(parents=True, exist_ok=True) real = {r["match"]: turnover_breakdown.real_loss_kinds(r) for r in losses} chances_by_match = {r["match"]: r["chances"] for r in losses if "chances" in r} table = strategy_table(summary["results"], real, chances_by_match) + for row in table.values(): + row["shots_faced"] = 0 + for r in summary["results"]: + shots = (r.get("stats") or {}).get("shots") or {} + for name, theirs in ((r["config_a"], "enemy"), (r["config_b"], "friendly")): + if name in table: + table[name]["shots_faced"] += shots.get(theirs, 0) shots = [s for c in chances_by_match.values() for s in c["shots"]] - heatmap(table, out / "signal_heatmap.png") - attack_defense(table, out / "attack_defense.png") + results(table, out / "results.png") + signals(table, out / "signals.png") shot_quality(shots, out / "shot_quality.png") ball_losses(table, out / "ball_losses.png") fouls(summary, table, out / "fouls.png") @@ -295,8 +403,9 @@ def main() -> None: args = parser.parse_args() summary = json.loads((args.run_dir / "summary.json").read_text()) losses = turnover_breakdown.analyse_run(args.run_dir, args.workers) - build(summary, losses, args.out) - print(f"{len(losses)} of {len(summary['results'])} matches had a replay; figures in {args.out}") + table = build(summary, losses, args.out) + print(f"{len(losses)} of {len(summary['results'])} matches had a replay; figures in {args.out}\n") + print(markdown(table)) if __name__ == "__main__": diff --git a/tools/triage.py b/tools/triage.py new file mode 100644 index 00000000..2556f68f --- /dev/null +++ b/tools/triage.py @@ -0,0 +1,420 @@ +#!/usr/bin/env python3 +# ruff: noqa: E402 +"""Triage a run dir: which problems happened in which matches, with the numbers, and a picture for the worst. + + pixi run python tools/triage.py REPLAYS_OR_RUN_DIR [--strategy NAME ...] [--matches 3] [--out DIR] + [--max-pictures 8] [--workers 2] [--restarts] + +Start here when a strategy plays badly. For each strategy the friendly side (config_a, the only side the +intentions log covers) of its `--matches` worst matches by goal difference is analysed: + +- `tactic_thrash` (`match_timeline.thrash_findings`): a robot reverting a tactic within a second, over the whole match; +- `stall` (`stuck_detector.find_stuck_windows`): a window with the ball frozen while a friendly robot oscillates, + or a restart held far too long (`restart_stall`). No `plan_trace` here: it needs the robot's target, which + the replay does not record, and a guessed target draws carrots that mean nothing; trace a stalled robot by hand + with `tools/plan_trace.py --target X Y` once you know where it was going; +- `target_in_keep_out`, `keep_out_short`, `target_in_defense_area` (`restart_picture.restart_findings`): only + with `--restarts`, which is slow (about 45 s a match). + +Every finding is written to `OUT/findings.jsonl`. The `--max-pictures` (default 8) findings that have a picture +source are ranked by kind (stall, target_in_keep_out, target_in_defense_area, tactic_thrash, keep_out_short), +then by value; those get a picture: a tactic_thrash gets `match_timeline` over 40 s from 5 s before the robot's +first flip-back, a stall `match_timeline` from 10 s before to 10 s after it, a restart finding (or a +restart_stall) `restart_picture` at its restart. `OUT/index.md` is the report to read first: per strategy, its matches with +their scores, the finding counts by kind, then each pictured finding with its message and picture path. + +`OUT` defaults to `RUN_DIR/triage/`. Read-only: nothing is played or changed. +""" + +from __future__ import annotations + +import argparse +import dataclasses +import functools +import multiprocessing +import os +import shutil +import sys +from collections import Counter, defaultdict +from dataclasses import dataclass +from pathlib import Path +from typing import Optional, Union + +os.environ.setdefault("PYGAME_HIDE_SUPPORT_PROMPT", "1") + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from tools import match_timeline +from tools.evaluation.match import _short_name +from tools.plan_trace import replay_game_at +from utama_core.analysis.findings import Finding, write_jsonl +from utama_core.analysis.matches import STEM, Match, load_match +from utama_core.analysis.plan_trace import plan_findings, plan_trace +from utama_core.analysis.restart_picture import ( + plan_restart, + render_restart, + restart_findings, + restart_game, + restart_occurrences, +) +from utama_core.analysis.stuck_detector import find_stuck_windows +from utama_core.engine.referee_override import is_override_command +from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.replay.scenario import scenario_from_replay + +SIDE = "friendly" +# The match_timeline picture for a tactic_thrash: this much before the robot's first flip-back, this long. +THRASH_LEAD_S = 5.0 +THRASH_WINDOW_S = 40.0 +# The match_timeline picture for a stall: this much either side of the stuck window. +STALL_MARGIN_S = 10.0 +# Picture priority, most useful first. +KIND_PRIORITY = ( + "stall", + "target_in_keep_out", + "target_in_defense_area", + "tactic_thrash", + "keep_out_short", +) + + +@dataclass(frozen=True) +class TimelinePicture: + """A match_timeline picture of one window.""" + + npz: Path + t0: float + t1: float + + +@dataclass(frozen=True) +class RestartPicture: + """A restart_picture of the friendly override for one restart command.""" + + npz: Path + t: float + command: RefereeCommand + + +@dataclass +class Item: + """One finding and the picture it would get (`request`, drawn only for the top-ranked findings).""" + + finding: Finding + request: Optional[Union[TimelinePicture, RestartPicture]] = None + window: Optional[tuple[float, float]] = ( + None # the seconds the picture shows (the finding's own t0-t1 may be the match) + ) + image: Optional[Path] = None # set by draw_pictures for the findings that were drawn + note: Optional[str] = None # why a picture was not drawn, when one was asked for + + @property + def pictureable(self) -> bool: + return self.request is not None + + +@dataclass +class MatchReport: + match: Match + items: list[Item] + notes: list[str] + + +# --- match selection ------------------------------------------------------------------------------- + + +def select_matches(run_dir: Path, strategies: Optional[set[str]], n: int) -> list[Match]: + """Each strategy's `n` worst matches as config_a (friendly), by its goal difference, worst first. + + `strategies` (short or full config names) limits the selection to those strategies; None means every + strategy in the dir. A match with no score sorts last.""" + wanted = None if strategies is None else {_short_name(s) for s in strategies} + by_strategy: dict[str, list[Match]] = defaultdict(list) + for npz in sorted(run_dir.glob("*.npz")): + m = STEM.match(npz.name.removesuffix(".npz")) + if m is None or (wanted is not None and m["a"] not in wanted): + continue + match = load_match(npz) + if match is not None: + by_strategy[match.a].append(match) + chosen: list[Match] = [] + for name in sorted(by_strategy): + ranked = sorted( + by_strategy[name], + key=lambda m: (m.goal_difference(name) is None, m.goal_difference(name) or 0, m.npz.name), + ) + chosen.extend(ranked[:n]) + return chosen + + +# --- per-match analysis (runs in a worker) --------------------------------------------------------- + + +def _stall_items(npz: Path, window, end: float) -> list[Item]: + """The stall finding for one stuck window, with its picture: the restart for a restart_stall, else the + match timeline around it.""" + dur = window.t_end - window.t_start + if window.kind == "restart_stall": + commands = restart_occurrences(npz) + held = [c for t, c in commands if t <= window.t_start] + command = held[-1] if held else None + name = command.name if command is not None else "a restart command" + finding = Finding( + tool="stuck_detector", + kind="stall", + replay=str(npz), + side=SIDE, + t0=window.t_start, + t1=window.t_end, + robots=(), + value=dur, + threshold=None, + unit="s", + message=f"{name} held for {dur:.1f} s ({window.t_start:.1f}-{window.t_end:.1f} s) with the ball frozen: " + "no restart ceremony takes that long.", + ) + ask = RestartPicture(npz, window.t_start, command) if command and is_override_command(command) else None + return [Item(finding, request=ask, window=(window.t_start, window.t_start))] + + robots = tuple(window.oscillating_robot_ids) + finding = Finding( + tool="stuck_detector", + kind="stall", + replay=str(npz), + side=SIDE, + t0=window.t_start, + t1=window.t_end, + robots=robots, + value=dur, + threshold=None, + unit="s", + message=f"friendly robots {list(robots)} oscillate with the ball frozen (ball std {window.ball_std:.3f} m) " + f"for {dur:.1f} s ({window.t_start:.1f}-{window.t_end:.1f} s).", + ) + t0 = max(0.0, window.t_start - STALL_MARGIN_S) + t1 = min(end, window.t_end + STALL_MARGIN_S) + return [Item(finding, request=TimelinePicture(npz, t0, t1), window=(t0, t1))] + + +def analyse_match(match: Match, restarts: bool) -> MatchReport: + """Every finding of one match's friendly side, each with the picture it would get (drawn later).""" + npz = match.npz + notes: list[str] = [] + items: list[Item] = [] + + window = match_timeline.load_window(npz, None, None, SIDE) + end = float(window.tw[-1]) + for finding in match_timeline.thrash_findings(window, replay=str(npz), side=SIDE): + rid = finding.robots[0] + flips = match_timeline.flip_backs(window.lanes[window.robot_ids.index(rid)]) + t0 = max(0.0, flips[0] - THRASH_LEAD_S) + t1 = min(end, t0 + THRASH_WINDOW_S) + items.append(Item(finding, request=TimelinePicture(npz, t0, t1) if t1 > t0 else None, window=(t0, t1))) + + for stuck in find_stuck_windows(npz): + items.extend(_stall_items(npz, stuck, end)) + + if restarts: + commands = dict(restart_occurrences(npz)) + for finding in restart_findings(npz, SIDE): + command = commands.get(finding.t0) + items.append( + Item( + finding, + request=RestartPicture(npz, finding.t0, command) if command else None, + window=(finding.t0, finding.t0), + ) + ) + return MatchReport(match, items, notes) + + +# --- pictures (main process) ----------------------------------------------------------------------- + + +def _priority(kind: str) -> int: + return KIND_PRIORITY.index(kind) if kind in KIND_PRIORITY else len(KIND_PRIORITY) + + +def rank(items: list[Item]) -> list[Item]: + """The items that have a picture source, most important first: kind priority, then value (high first).""" + return sorted( + (it for it in items if it.pictureable), + key=lambda it: (_priority(it.finding.kind), -it.finding.value), + ) + + +def _draw_restart(picture: RestartPicture, out: Path) -> Path: + scenario = scenario_from_replay(picture.npz, picture.t, showing=picture.command) + game = restart_game(picture.npz, scenario, SIDE) + plan = plan_restart(game, picture.command, sim_time=scenario.sim_time) + title = f"{picture.npz.stem}: friendly override for {picture.command.name} at t={picture.t:g} s" + return render_restart(plan, out, title=title) + + +def draw_pictures(items: list[Item], pictures: Path, max_pictures: int) -> list[Item]: + """Give the top `max_pictures` pictureable items an `image` (drawn into `pictures`), and return them. + Items that share a picture source share one file.""" + drawn: dict[tuple, Path] = {} + shown = rank(items)[:max_pictures] + for rank_no, item in enumerate(shown, start=1): + r = item.request + if isinstance(r, TimelinePicture): + key: tuple = ("timeline", str(r.npz), r.t0, r.t1) + else: + key = ("restart", str(r.npz), r.t) + if key not in drawn: + stem = Path(key[1]).stem + dst = pictures / f"{rank_no:02d}_{item.finding.kind}_{stem}.png" + try: + if key[0] == "timeline": + match_timeline.build(r.npz, r.t0, r.t1, SIDE, dst) + else: + _draw_restart(r, dst) + drawn[key] = dst + except Exception as exc: # noqa: BLE001 - a picture is optional; the finding is not + item.note = f"picture failed: {type(exc).__name__}: {exc}" + continue + item.image = drawn[key] + return shown + + +# --- report ---------------------------------------------------------------------------------------- + + +def _span_of(window: Optional[tuple[float, float]]) -> str: + if window is None: + return "the match" + a, b = window + return f"{a:.1f}-{b:.1f} s" if b > a else f"{a:.1f} s" + + +def _span(f: Finding) -> str: + return _span_of((f.t0, f.t1)) if f.t0 is not None and f.t1 is not None else "the match" + + +def write_index( + out: Path, + run_dir: Path, + reports: list[MatchReport], + max_pictures: int, + restarts: bool, +) -> Path: + lines = [ + f"# Triage: {run_dir.name}", + "", + f"Friendly side (config_a) of each strategy's worst matches by goal difference. " + f"Pictures: the top {max_pictures} findings with a picture source. Restart findings: " + f"{'yes' if restarts else 'no (--restarts not given)'}.", + "", + ] + by_strategy: dict[str, list[MatchReport]] = defaultdict(list) + for r in reports: + by_strategy[r.match.a].append(r) + if not reports: + lines.append("No matches with this strategy as config_a in the run dir.") + for name in sorted(by_strategy): + group = by_strategy[name] + lines += [f"## {name}", "", "Matches analysed (score friendly-enemy, goal difference):"] + for r in group: + m = r.match + score = "no score" if m.score_a is None else f"{m.score_a}-{m.score_b}" + gd = m.goal_difference(name) + lines.append(f"- {m.npz.stem}: {score} (GD {gd if gd is not None else 'n/a'})") + for note in sorted({n for r in group for n in r.notes}): + lines.append(f"- note: {note}") + counts = Counter(it.finding.kind for r in group for it in r.items) + lines += ["", "| kind | findings |", "|---|---|"] + if not counts: + lines.append("| (none) | 0 |") + for kind, n in sorted(counts.items(), key=lambda kv: _priority(kv[0])): + lines.append(f"| {kind} | {n} |") + lines.append("") + pictured = [it for r in group for it in r.items if it.image is not None or it.note] + if not pictured: + lines += ["No pictured findings.", ""] + for it in pictured: + f = it.finding + lines += [f"### {f.kind} at {_span(f)}", "", f.message] + if it.image is not None: + lines.append(f"Picture of {_span_of(it.window)}: `{it.image.resolve()}`") + if it.note: + lines.append(f"Picture not drawn: {it.note}") + lines.append("") + path = out / "index.md" + path.write_text("\n".join(lines)) + return path + + +def triage( + run_dir: Union[str, Path], + *, + strategies: Optional[set[str]] = None, + matches: int = 3, + out: Optional[Union[str, Path]] = None, + restarts: bool = False, + max_pictures: int = 8, + workers: int = 2, +) -> Path: + """Analyse the selected matches, draw the top pictures and write `out/index.md` and `out/findings.jsonl`. + Returns the index path.""" + run_dir = Path(run_dir) + out = Path(out) if out is not None else run_dir / "triage" + pictures = out / "pictures" + pictures.mkdir(parents=True, exist_ok=True) + selected = select_matches(run_dir, strategies, matches) + jobs = [(m, restarts) for m in selected] + if workers > 1 and len(jobs) > 1: + with multiprocessing.Pool(processes=workers) as pool: + reports = pool.starmap(analyse_match, jobs) + else: + reports = [analyse_match(*j) for j in jobs] + items = [it for r in reports for it in r.items] + draw_pictures(items, pictures, max_pictures) + write_jsonl( + [dataclasses.replace(it.finding, image=str(it.image) if it.image else None) for it in items], + out / "findings.jsonl", + ) + return write_index(out, run_dir, reports, max_pictures, restarts) + + +def main() -> None: + parser = argparse.ArgumentParser( + description=__doc__.split("\n\n")[0], formatter_class=argparse.RawDescriptionHelpFormatter + ) + parser.add_argument( + "run_dir", type=Path, help="a round-robin dir (replays/tournament_) or any dir of match .npz files" + ) + parser.add_argument( + "--strategy", + action="append", + metavar="NAME", + help="only matches where this strategy is config_a (repeatable; short or build_*_kernel_strategy names)", + ) + parser.add_argument( + "--matches", type=int, default=3, help="each strategy's N worst matches by goal difference (default 3)" + ) + parser.add_argument("--out", type=Path, default=None, help="output dir (default RUN_DIR/triage/)") + parser.add_argument("--max-pictures", type=int, default=8, help="pictures to draw (default 8)") + parser.add_argument("--workers", type=int, default=2, help="worker processes, one match each (default 2)") + parser.add_argument( + "--restarts", + action="store_true", + help="also check every restart's referee override (restart_picture findings). SLOW: about 45 s a match", + ) + args = parser.parse_args() + index = triage( + args.run_dir, + strategies=set(args.strategy) if args.strategy else None, + matches=args.matches, + out=args.out, + restarts=args.restarts, + max_pictures=args.max_pictures, + workers=args.workers, + ) + print(f"wrote {index}") + + +if __name__ == "__main__": + main() diff --git a/utama_core/analysis/chances.py b/utama_core/analysis/chances.py index 7c602d35..c6c1fcd0 100644 --- a/utama_core/analysis/chances.py +++ b/utama_core/analysis/chances.py @@ -172,6 +172,7 @@ def _shot(self, side: str, ts: float, ball, frame, goal_x: float) -> None: { "side": side, "t": round(ts, 2), + "xy": [round(xy[0], 2), round(xy[1], 2)], "distance_m": round(math.hypot(along, xy[1]), 2), "angle_deg": round(math.degrees(math.atan2(abs(xy[1]), along)), 1), "open_goal": round(open_goal(xy, goal_x, blockers), 2), diff --git a/utama_core/analysis/findings.py b/utama_core/analysis/findings.py new file mode 100644 index 00000000..547d25a7 --- /dev/null +++ b/utama_core/analysis/findings.py @@ -0,0 +1,75 @@ +"""One shared format for the problems the picture tools flag (`--findings PATH` on each tool). + +A picture is for understanding a flagged problem, never the only evidence for it: each tool also emits +its flags as JSON lines, one `Finding` per line, so an agent (or `tools/triage.py`) can filter on the +JSON and open the picture only to see what it means. + + {"tool": "match_timeline", "kind": "tactic_thrash", "replay": "replays/.../a_vs_b.npz", "side": "friendly", + "t0": 0.0, "t1": 480.0, "robots": [4], "value": 78.98, "threshold": 4.0, "unit": "per live min", + "message": "R4 switched tactic 78.98 times per live minute (threshold 4.00).", "image": null} + +`value` and `threshold` are in `unit`; `threshold` is None for descriptive facts with no limit. +`image` is the PNG drawn alongside, or None when the finding was computed without drawing. +""" + +from __future__ import annotations + +import json +import sys +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Iterable, Optional, Union + + +@dataclass(frozen=True) +class Finding: + tool: str # the tool that computed it, e.g. "match_timeline" + kind: str # e.g. "tactic_thrash", "parked", "pushed_out", "target_in_keep_out" + replay: Optional[str] # the replay path, or None for a run-level fact + side: Optional[str] # "friendly", "enemy" or None + t0: Optional[float] # seconds on the replay's clock + t1: Optional[float] + robots: tuple[int, ...] + value: float + threshold: Optional[float] + unit: str + message: str # one plain sentence with the numbers + image: Optional[str] = None # the PNG drawn for it, when one was drawn + + +def to_dict(finding: Finding) -> dict: + d = asdict(finding) + d["robots"] = list(finding.robots) + return d + + +def from_dict(d: dict) -> Finding: + return Finding( + tool=d["tool"], + kind=d["kind"], + replay=d.get("replay"), + side=d.get("side"), + t0=d.get("t0"), + t1=d.get("t1"), + robots=tuple(int(r) for r in d.get("robots") or ()), + value=float(d["value"]), + threshold=d.get("threshold"), + unit=d["unit"], + message=d["message"], + image=d.get("image"), + ) + + +def write_jsonl(findings: Iterable[Finding], path: Union[str, Path]) -> None: + """One JSON object per line. `path` "-" writes to stdout.""" + lines = "".join(json.dumps(to_dict(f)) + "\n" for f in findings) + if str(path) == "-": + sys.stdout.write(lines) + sys.stdout.flush() + return + Path(path).write_text(lines) + + +def read_jsonl(path: Union[str, Path]) -> list[Finding]: + with open(path) as f: + return [from_dict(json.loads(line)) for line in f if line.strip()] diff --git a/utama_core/analysis/goal_ledger.py b/utama_core/analysis/goal_ledger.py index b9193c90..d2ac23b1 100644 --- a/utama_core/analysis/goal_ledger.py +++ b/utama_core/analysis/goal_ledger.py @@ -36,12 +36,11 @@ touches the ball, for every change of possession, so that its situations partition every goal. Thirds are `chances.py`'s (`THIRD_DEPTH_M` from either goal line). -Strategy names come from `--labels`, else the run's `summary.json`/`ladder.json` (config_a is +Strategy names come from `--labels`, else the run's `summary.json`/`ladder.json` (the retired ladder's runs; config_a is friendly), else the referee's team names (an official log's teams). -`--rates PATH` also writes how often each situation happens in the corpus, owner-neutral and -per live minute (`situation_rates`): the weights `tools/scenario_bench.py` turns its -per-situation results into goals per match with. +`--rates PATH` also writes how often each situation type happens in the corpus, owner-neutral and +per live minute (`situation_rates`). Nothing is written unless a path is given. """ from __future__ import annotations diff --git a/utama_core/analysis/matches.py b/utama_core/analysis/matches.py new file mode 100644 index 00000000..0da6c6b9 --- /dev/null +++ b/utama_core/analysis/matches.py @@ -0,0 +1,74 @@ +"""The matches in a run dir: which two strategies a replay is between, and the score it ended on. + +A match file's stem is `_vs_` (round-robin) or `_vs__s` (an +evaluate.py run). config_a is the friendly side (yellow) in every run. Shared by `tools/evaluation/explain.py` +and `tools/triage.py`. +""" + +from __future__ import annotations + +import json +import re +from dataclasses import dataclass +from pathlib import Path +from typing import Optional + +from utama_core.replay.columnar_reader import load_columnar_replay + +STEM = re.compile(r"^(?P.+?)_vs_(?P.+?)(?:_s(?P\d+))?$") + + +@dataclass +class Match: + """One replay in the run dir. `a` is config_a (the friendly side), `b` config_b (enemy).""" + + npz: Path + a: str # short names + b: str + seed: Optional[int] + score_a: Optional[int] # None when the replay has no referee state to read a score from + score_b: Optional[int] + fouls: list[dict] + + def job(self) -> tuple[str, str, str, list[dict]]: + return (str(self.npz), self.a, self.b, self.fouls) + + def goal_difference(self, name: str) -> Optional[int]: + if self.score_a is None: + return None + return self.score_a - self.score_b if name == self.a else self.score_b - self.score_a + + +def final_score(npz: Path) -> tuple[Optional[int], Optional[int]]: + """The score at the end of a replay: the last referee state the writer stored (scores only + change through stored referee messages). Yellow is config_a (friendly) in every run.""" + replay = load_columnar_replay(npz) + if not replay.sparse_referee: + return None, None + ref = replay.sparse_referee[max(replay.sparse_referee)] + return int(ref.yellow_team.score), int(ref.blue_team.score) + + +def load_match(npz: Path) -> Optional[Match]: + """The `Match` for one replay file, or None when its name is not `_vs_[_s]`.""" + m = STEM.match(npz.name.removesuffix(".npz")) + if m is None: + return None + stats = npz.with_name(npz.name.removesuffix(".npz") + ".stats.json") + fouls = [] + if stats.exists(): + fouls = json.loads(stats.read_text()).get("fouls") or [] + score_a, score_b = final_score(npz) + seed = int(m["seed"]) if m["seed"] is not None else None + return Match(npz, m["a"], m["b"], seed, score_a, score_b, fouls) + + +def find_matches(run_dir: Path, names: set[str]) -> list[Match]: + """Every replay in `run_dir` with `names` (short names) on either side, in file-name order.""" + matches = [] + for npz in sorted(run_dir.glob("*.npz")): + m = STEM.match(npz.name.removesuffix(".npz")) + if m is None or not ({m["a"], m["b"]} & names): + continue + matches.append(load_match(npz)) + return matches diff --git a/utama_core/analysis/motion_stats.py b/utama_core/analysis/motion_stats.py index b904b7b3..8d068d41 100644 --- a/utama_core/analysis/motion_stats.py +++ b/utama_core/analysis/motion_stats.py @@ -8,7 +8,7 @@ import json from pathlib import Path -from typing import Dict, Optional, Union +from typing import Dict, Union import numpy as np @@ -212,7 +212,7 @@ def enrich_tournament_directory(tournament_dir: Union[str, Path]) -> None: st["worst_clearance_m"] = m["worst_clearance_m"] # Rebuild strategy_table if possible - from tools.evaluation.round_robin import strategy_table + from utama_core.analysis.strategy_table import strategy_table summary["strategies"] = strategy_table(summary["results"]) with open(summary_path, "w") as f: diff --git a/utama_core/analysis/physics_ranges.py b/utama_core/analysis/physics_ranges.py new file mode 100644 index 00000000..c821210d --- /dev/null +++ b/utama_core/analysis/physics_ranges.py @@ -0,0 +1,467 @@ +"""Physics parameter ranges from replays: ball deceleration, robot motion limits and dropped +detections, for converted official logs and our own matches alike. + + pixi run python -m utama_core.analysis.physics_ranges --real ~/dev/ssl/official-logs/2025 \ + --sim replays/tournament_ --out ranges.jsonl + +The sim-realism plan's Phase 1 (`docs/sim_realism.md`): the ranges a randomized sim draws from. +Each match gives per-match values (one carpet, one set of robots); the summary reports the spread +between matches, robots or teams ("between") and inside one match ("within"), with 95% bootstrap +intervals over matches for league medians. + +Only live play counts (`chances.LIVE`). A real replay's robots and ball come from an autoref's +tracker, a filter: peaks (speed, acceleration, turn rate) come out smoothed, so they are lower +bounds, and the tracker bridges short occlusions itself, so dropped detections are the ones it +gave up on. Real velocities are the tracker's own; accelerations, ball deceleration and turn +rates are central differences over `DIFF_TICKS` either side, the same on both sides. + +**Ball deceleration.** A tick is *free* when the ball is in play on the ground (`z` below +`GROUND_Z_M`), seen, more than `FREE_ROBOT_M` from every robot and `WALL_MARGIN_M` inside the +field lines (walls stand beyond them), and moving faster than `MIN_SPEED_MPS`. Its deceleration +is the drop in ball speed over the window, from the velocity and, separately, from positions +(independent of the tracker's velocity model). A *kick* is a ball velocity change of at least +`KICK_DV_MPS` within `KICK_WINDOW_S` to a speed of at least `KICK_SPEED_MPS` (as in the shot +detector). Deceleration is reported by ball speed (constant deceleration means rolling friction; +growing with speed means a velocity-proportional damping) and by time since the last kick (a +sliding phase shows as higher deceleration just after it). A *rolling segment* is a run of free +ticks at least `SLIDE_S` after the last kick and at least `MIN_SEGMENT_S` long; a straight line +fitted to its speed gives one deceleration. + +**Robot motion**, per robot with at least `MIN_ROBOT_LIVE_S` of live play: 95th and 99th +percentile speed, acceleration (along the direction of travel, speeding up), braking (along it, +slowing down) and turn rate. Speeds above `GLITCH_SPEED_MPS` (tracker id swaps) are dropped. The +summary keeps robots whose 95th-percentile speed is at least `ACTIVE_SPEED_P95_MPS`: keepers and +the idle robots some real teams leave on the field would otherwise set the range. + +**Dropped detections.** A real replay holds the ball at its last position with zero velocity +while the tracker sees none (`ssl_log`); those live ticks are the ball's dropouts. A robot's +dropout is a gap of at most `MAX_ROBOT_GAP_S` with the robot present on both sides, while other +robots are present (all robots missing at once is a tracker outage, counted apart). The sim has +no vision model in our matches (`rsim_noise`/`rsim_vanishing` off), so its values are zero by +construction; the ball held at rest there is not a dropout, so sim ball dropouts are not counted. +The summary leaves out matches whose ball is unseen more than `BROKEN_BALL_SHARE` of live play. +""" + +from __future__ import annotations + +import argparse +import json +from concurrent.futures import ProcessPoolExecutor +from pathlib import Path +from typing import Optional + +import numpy as np + +from utama_core.analysis.chances import LIVE +from utama_core.replay.columnar_reader import ColumnarReplay, load_columnar_replay + +TICK_S = 1 / 60 +DIFF_TICKS = 3 # central differences over +-3 ticks (0.1 s), as the log converter's +GROUND_Z_M = 0.05 +FREE_ROBOT_M = 0.3 +WALL_MARGIN_M = 0.15 +MIN_SPEED_MPS = 0.3 +KICK_DV_MPS = 1.5 +KICK_WINDOW_S = 0.2 +KICK_SPEED_MPS = 2.0 +SLIDE_S = 0.5 # real balls decelerate several times faster for about 0.5 s after a kick (sliding) +MIN_SEGMENT_S = 0.3 +MIN_SEGMENT_START_MPS = 0.5 +MIN_ROBOT_LIVE_S = 20.0 +MIN_MOVING_MPS = 0.2 # below this a robot's direction of travel is noise +GLITCH_SPEED_MPS = 6.0 +MAX_ROBOT_GAP_S = 3.0 +ACTIVE_SPEED_P95_MPS = 0.5 # summary: robots slower than this are keepers or idle robots +BROKEN_BALL_SHARE = 0.2 # summary: a match whose ball is unseen this often has a broken tracker +DEFAULT_FIELD_M = (9.0, 6.0) # Division B, for replays without a source.json + +SPEED_BINS = (0.3, 0.7, 1.5, 3.0, 5.0, 10.0) +SINCE_KICK_BINS = (0.0, 0.1, 0.2, 0.3, 0.5, 1.0, np.inf) +_LIVE_IDS = [c.value for c in LIVE] + + +def runs(mask: np.ndarray) -> list[tuple[int, int]]: + """The [start, end) index ranges where `mask` is True.""" + padded = np.r_[False, np.asarray(mask, dtype=bool), False] + edges = np.flatnonzero(padded[1:] != padded[:-1]) + return list(zip(edges[::2].tolist(), edges[1::2].tolist())) + + +def window_all(mask: np.ndarray, k: int) -> np.ndarray: + """True at t where `mask` holds on every tick of [t - k, t + k].""" + out = np.zeros(len(mask), dtype=bool) + if len(mask) <= 2 * k: + return out + c = np.r_[0, np.cumsum(~np.asarray(mask, dtype=bool))] + out[k:-k] = (c[2 * k + 1 :] - c[: -2 * k - 1]) == 0 + return out + + +def central_difference(x: np.ndarray, k: int = DIFF_TICKS, dt: float = TICK_S) -> np.ndarray: + """d/dt along axis 0 over +-k ticks; NaN at the ends.""" + out = np.full(x.shape, np.nan) + if len(x) > 2 * k: + out[k:-k] = (x[2 * k :] - x[: -2 * k]) / (2 * k * dt) + return out + + +def kick_ticks(ball_v: np.ndarray, dt: float = TICK_S) -> np.ndarray: + """First tick of each run where the ball's velocity changed by `KICK_DV_MPS` within + `KICK_WINDOW_S` and its speed reached `KICK_SPEED_MPS`.""" + lag = max(1, round(KICK_WINDOW_S / dt)) + v = ball_v[:, :2] + dv = np.zeros(len(v)) + dv[lag:] = np.hypot(*(v[lag:] - v[:-lag]).T) + hit = (dv >= KICK_DV_MPS) & (np.hypot(*v.T) >= KICK_SPEED_MPS) + return np.array([s for s, _ in runs(hit)], dtype=int) + + +def since_last(events: np.ndarray, n: int, dt: float = TICK_S) -> np.ndarray: + """Seconds since the latest event at or before each tick; inf before the first.""" + last = np.full(n, -1) + last[events] = events + last = np.maximum.accumulate(last) + return np.where(last >= 0, (np.arange(n) - last) * dt, np.inf) + + +def fit_segment(ts: np.ndarray, speed: np.ndarray) -> tuple[float, float]: + """Deceleration (minus the slope of a straight line through speed against time) and R².""" + slope, intercept = np.polyfit(ts, speed, 1) + resid = speed - (slope * ts + intercept) + total = np.sum((speed - speed.mean()) ** 2) + return float(-slope), float(1 - np.sum(resid**2) / total) if total > 0 else 0.0 + + +def _field(path: Path) -> tuple[float, float]: + source = path.with_name(path.name[: -len(".npz")] + ".source.json") + if source.exists(): + field = json.loads(source.read_text()).get("field") or {} + if "field_length" in field: + return float(field["field_length"]), float(field["field_width"]) + return DEFAULT_FIELD_M + + +def _teams(path: Path, replay: ColumnarReplay) -> tuple[str, str]: + """Names for the friendly and enemy columns: an official log's teams, else our configs.""" + source = path.with_name(path.name[: -len(".npz")] + ".source.json") + if source.exists(): + s = json.loads(source.read_text()) + yellow, blue = s.get("yellow", "yellow"), s.get("blue", "blue") + return (yellow, blue) if replay.my_team_is_yellow else (blue, yellow) + a, _, b = path.stem.partition("_vs_") + return a, b or "enemy" + + +def ball_seen(ball_p: np.ndarray, ball_v: np.ndarray) -> np.ndarray: + """False where a converted log holds the ball (unseen): zero velocity, position unchanged.""" + held = np.all(ball_v == 0.0, axis=1) & np.r_[False, np.all(ball_p[1:] == ball_p[:-1], axis=1)] + return ~held + + +def free_ball( + ball_p: np.ndarray, robot_p: np.ndarray, field: tuple[float, float], seen: Optional[np.ndarray] = None +) -> np.ndarray: + """Ticks where the ball is on the ground, inside the field lines by `WALL_MARGIN_M` and more + than `FREE_ROBOT_M` from every robot. `robot_p` is (ticks, robots, 2), NaN where absent.""" + gap = robot_p - ball_p[:, None, :2] + dist = np.hypot(gap[..., 0], gap[..., 1]) + nearest = np.min(np.where(np.isnan(dist), np.inf, dist), axis=1) if dist.shape[1] else np.full(len(ball_p), np.inf) + inside = (np.abs(ball_p[:, 0]) < field[0] / 2 - WALL_MARGIN_M) & ( + np.abs(ball_p[:, 1]) < field[1] / 2 - WALL_MARGIN_M + ) + out = (ball_p[:, 2] < GROUND_Z_M) & inside & (nearest > FREE_ROBOT_M) + return out if seen is None else out & seen + + +def ball_deceleration( + ts: np.ndarray, ball_p: np.ndarray, ball_v: np.ndarray, free: np.ndarray, live: np.ndarray +) -> dict: + """Deceleration samples binned by speed and by time since the last kick, and rolling segments.""" + k = DIFF_TICKS + dt = TICK_S + speed = np.hypot(ball_v[:, 0], ball_v[:, 1]) + base = free & live & (speed > MIN_SPEED_MPS) + decel_v = -central_difference(speed, k, dt) + pos_speed = np.hypot(*central_difference(ball_p[:, :2], k, dt).T) + decel_p = -central_difference(pos_speed, k, dt) + ok_v = window_all(base, k) & np.isfinite(decel_v) + ok_p = window_all(base, 2 * k) & np.isfinite(decel_p) + since = since_last(kick_ticks(ball_v, dt), len(ts), dt) + + def binned(values, ok, key, edges): + rows = [] + for lo, hi in zip(edges[:-1], edges[1:]): + m = ok & (key >= lo) & (key < hi) + rows.append( + { + "lo": lo, + "hi": None if np.isinf(hi) else hi, + "n": int(m.sum()), + "median": float(np.median(values[m])) if m.any() else None, + } + ) + return rows + + rolling = base & (since >= SLIDE_S) + segments = [] + for s, e in runs(rolling): + if (e - s) * dt < MIN_SEGMENT_S or speed[s] < MIN_SEGMENT_START_MPS: + continue + decel, r2 = fit_segment(ts[s:e] - ts[s], speed[s:e]) + segments.append( + { + "start_mps": round(float(speed[s]), 3), + "end_mps": round(float(speed[e - 1]), 3), + "duration_s": round((e - s) * dt, 3), + "decel": round(decel, 4), + "r2": round(r2, 3), + } + ) + return { + "by_speed_vel": binned(decel_v, ok_v, speed, SPEED_BINS), + "by_speed_pos": binned(decel_p, ok_p, pos_speed, SPEED_BINS), + "by_since_kick_vel": binned(decel_v, ok_v, since, SINCE_KICK_BINS), + "by_since_kick_pos": binned(decel_p, ok_p, since, SINCE_KICK_BINS), + "segments": segments, + } + + +def robot_motion( + robot_p: np.ndarray, robot_v: np.ndarray, orientation: np.ndarray, live: np.ndarray +) -> list[Optional[dict]]: + """Per robot column: speed, acceleration, braking and turn-rate percentiles in live play + (None for a robot with less than `MIN_ROBOT_LIVE_S`).""" + k, dt = DIFF_TICKS, TICK_S + out = [] + for j in range(robot_p.shape[1]): + present = ~np.isnan(robot_p[:, j, 0]) + v = robot_v[:, j] + speed = np.hypot(v[:, 0], v[:, 1]) + sane = present & (np.nan_to_num(speed, nan=np.inf) <= GLITCH_SPEED_MPS) + ok = window_all(sane & live, k) + if ok.sum() * dt < MIN_ROBOT_LIVE_S: + out.append(None) + continue + a = central_difference(np.where(sane[:, None], v, np.nan), k, dt) + moving = ok & (speed > MIN_MOVING_MPS) & np.isfinite(a[:, 0]) + along = np.einsum("ij,ij->i", a[moving], v[moving]) / speed[moving] + theta = orientation[:, j].copy() + omega = np.full(len(theta), np.nan) + for s, e in runs(present): + if e - s > 2 * k: + omega[s:e] = central_difference(np.unwrap(theta[s:e]), k, dt) + turn = np.abs(omega[ok & np.isfinite(omega)]) + + def pct(x, q): + return round(float(np.percentile(x, q)), 3) if len(x) else None + + out.append( + { + "live_s": round(float(ok.sum() * dt), 1), + "speed_p95": pct(speed[ok], 95), + "speed_p99": pct(speed[ok], 99), + "accel_p95": pct(along[along > 0], 95), + "brake_p95": pct(-along[along < 0], 95), + "turn_p95": pct(turn, 95), + } + ) + return out + + +def dropouts(ball_seen_mask: Optional[np.ndarray], robot_p: np.ndarray, live: np.ndarray) -> dict: + """Ball and robot dropouts in live play. `ball_seen_mask` None: the ball is not counted.""" + dt = TICK_S + live_s = float(live.sum() * dt) + out: dict = {"live_s": round(live_s, 1)} + if ball_seen_mask is not None: + lengths = [(e - s) * dt for s, e in runs(~ball_seen_mask & live)] + out["ball"] = { + "share": round(sum(lengths) / live_s, 5) if live_s else None, + "per_live_min": round(len(lengths) / live_s * 60, 3) if live_s else None, + "run_s": lengths, + } + present = ~np.isnan(robot_p[..., 0]) + outage = live & ~present.any(axis=1) + out["tracker_outage_s"] = round(float(outage.sum() * dt), 2) + gap_s, gaps, present_s = 0.0, [], 0.0 + for j in range(robot_p.shape[1]): + p = present[:, j] + present_s += float((p & live).sum() * dt) + for s, e in runs(~p): + if s == 0 or e == len(p) or (e - s) * dt > MAX_ROBOT_GAP_S or outage[s:e].any() or not live[s:e].all(): + continue + gaps.append((e - s) * dt) + gap_s += (e - s) * dt + out["robot"] = {"share": round(gap_s / present_s, 5) if present_s else None, "run_s": gaps} + return out + + +def analyse(path_str: str, real: bool) -> dict: + """Everything for one replay; JSON-serializable.""" + path = Path(path_str) + try: + r = load_columnar_replay(path) + live = np.isin(r.referee_command, _LIVE_IDS) + field = _field(path) + robot_p = np.concatenate([r.friendly_p, r.enemy_p], axis=1) + seen = ball_seen(r.ball_p, r.ball_v) if real else None + free = free_ball(r.ball_p, robot_p, field, seen) + names = _teams(path, r) + robots = [] + for team, p, v, o, ids in ( + (names[0], r.friendly_p, r.friendly_v, r.friendly_orientation, r.friendly_ids), + (names[1], r.enemy_p, r.enemy_v, r.enemy_orientation, r.enemy_ids), + ): + for rid, m in zip(ids.tolist(), robot_motion(p, v, o, live)): + if m is not None: + robots.append({"team": team, "id": int(rid), **m}) + return { + "match": path.name[: -len(".npz")], + "real": real, + "field": field, + "ball": ball_deceleration(r.ts, r.ball_p, r.ball_v, free, live), + "robots": robots, + "dropouts": dropouts(seen, robot_p, live), + } + except Exception as e: # noqa: BLE001 - one bad replay must not stop a corpus pass + return {"match": path.name, "real": real, "error": repr(e)} + + +# ---------------------------------------------------------------- summary + + +def _boot_median(values: list[float], n: int = 2000, seed: int = 0) -> tuple[float, float]: + rng = np.random.default_rng(seed) + x = np.asarray(values, dtype=float) + meds = np.median(x[rng.integers(0, len(x), (n, len(x)))], axis=1) + return float(np.percentile(meds, 2.5)), float(np.percentile(meds, 97.5)) + + +def _spread(values: list[float]) -> str: + x = np.asarray([v for v in values if v is not None], dtype=float) + if not len(x): + return "n/a" + lo, hi = _boot_median(list(x)) if len(x) > 1 else (x[0], x[0]) + q1, q3 = np.percentile(x, [25, 75]) + return ( + f"{np.median(x):.2f} [{lo:.2f}, {hi:.2f}] IQR {q1:.2f}-{q3:.2f} range {x.min():.2f}-{x.max():.2f} (n={len(x)})" + ) + + +def _pooled_bins(matches: list[dict], key: str) -> list[str]: + """Median of the per-match medians in each bin, weighted by nothing (one match one vote).""" + rows = matches[0]["ball"][key] + out = [] + for i, row in enumerate(rows): + meds = [m["ball"][key][i]["median"] for m in matches if m["ball"][key][i]["n"] >= 10] + n = sum(m["ball"][key][i]["n"] for m in matches) + hi = "inf" if row["hi"] is None else f"{row['hi']:g}" + out.append(f" [{row['lo']:g}, {hi}): {_spread(meds)}, ticks={n}") + return out + + +def summarise(matches: list[dict]) -> str: + lines = [] + ok = [m for m in matches if "error" not in m] + for m in matches: + if "error" in m: + lines.append(f"skipped {m['match']}: {m['error']}") + for label, real in (("REAL", True), ("SIM", False)): + group = [m for m in ok if m["real"] == real] + if not group: + continue + lines.append(f"\n=== {label}: {len(group)} matches ===") + lines.append("ball deceleration, m/s² (median of per-match medians [95% CI], IQR and range across matches)") + for key in ("by_speed_vel", "by_speed_pos", "by_since_kick_vel", "by_since_kick_pos"): + lines.append(f" {key}:") + lines += _pooled_bins(group, key) + per_match, within, n_seg = [], [], 0 + for m in group: + d = [s["decel"] for s in m["ball"]["segments"] if s["r2"] >= 0.5] + n_seg += len(d) + if len(d) >= 5: + per_match.append(float(np.median(d))) + q1, q3 = np.percentile(d, [25, 75]) + within.append(float(q3 - q1)) + lines.append(f" rolling segments (R² >= 0.5, {n_seg} in all), per-match median (between): {_spread(per_match)}") + lines.append(f" rolling segments, within-match IQR: {_spread(within)}") + + every = [r for m in group for r in m["robots"]] + robots = [r for r in every if (r["speed_p95"] or 0) >= ACTIVE_SPEED_P95_MPS] + lines.append( + f"robot motion, per active robot ({len(robots)} of {len(every)} robot-matches): " + "median [95% CI over robots] IQR range" + ) + for key, unit in ( + ("speed_p95", "m/s"), + ("speed_p99", "m/s"), + ("accel_p95", "m/s²"), + ("brake_p95", "m/s²"), + ("turn_p95", "rad/s"), + ): + lines.append(f" {key} ({unit}): {_spread([r[key] for r in robots])}") + teams: dict[str, list[float]] = {} + for r in robots: + if r[key] is not None: + teams.setdefault(r["team"], []).append(r[key]) + team_meds = [float(np.median(v)) for v in teams.values() if len(v) >= 3] + lines.append(f" team medians (between teams): {_spread(team_meds)}") + + drops = [m["dropouts"] for m in group] + if real: + broken = [m["match"] for m in group if m["dropouts"]["ball"]["share"] > BROKEN_BALL_SHARE] + drops = [d for d in drops if d["ball"]["share"] <= BROKEN_BALL_SHARE] + if broken: + lines.append(f"left out of dropouts, ball unseen > {BROKEN_BALL_SHARE:.0%}: {', '.join(broken)}") + lines.append("dropped detections (live play)") + lines.append(f" ball unseen share per match: {_spread([d['ball']['share'] for d in drops])}") + lines.append(f" ball dropouts per live minute: {_spread([d['ball']['per_live_min'] for d in drops])}") + runs_s = [x for d in drops for x in d["ball"]["run_s"]] + if runs_s: + q = np.percentile(runs_s, [50, 90, 99]) + lines.append( + f" ball dropout length s: median {q[0]:.3f} p90 {q[1]:.3f} p99 {q[2]:.3f} max {max(runs_s):.2f} (n={len(runs_s)})" + ) + lines.append(f" robot gap share per match: {_spread([d['robot']['share'] for d in drops])}") + gaps = [x for d in drops for x in d["robot"]["run_s"]] + if gaps: + q = np.percentile(gaps, [50, 90, 99]) + lines.append(f" robot gap length s: median {q[0]:.3f} p90 {q[1]:.3f} p99 {q[2]:.3f} (n={len(gaps)})") + lines.append(f" tracker outage s per match: {_spread([d['tracker_outage_s'] for d in drops])}") + return "\n".join(lines) + + +def _replays(dirs: list[str]) -> list[str]: + out = [] + for d in dirs: + p = Path(d).expanduser() + out += [str(q) for q in sorted(p.rglob("*.npz")) if "match_cache" not in q.parts] if p.is_dir() else [str(p)] + return out + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("--real", nargs="*", default=[], help="converted official logs (dirs or .npz)") + parser.add_argument("--sim", nargs="*", default=[], help="our replays (dirs or .npz)") + parser.add_argument("--out", help="write one JSON line per match here") + parser.add_argument("--from-jsonl", nargs="*", default=[], help="summarise earlier --out files instead") + parser.add_argument("--workers", type=int, default=1) + args = parser.parse_args() + + matches = [json.loads(line) for p in args.from_jsonl for line in open(p)] + jobs = [(p, True) for p in _replays(args.real)] + [(p, False) for p in _replays(args.sim)] + if jobs: + out = open(args.out, "w") if args.out else None + with ProcessPoolExecutor(max(1, args.workers)) as ex: + for i, m in enumerate(ex.map(analyse, *zip(*jobs))): + matches.append(m) + if out: + out.write(json.dumps(m) + "\n") + print(f"[{i + 1}/{len(jobs)}] {m['match']} {m.get('error', '')}", flush=True) + if out: + out.close() + print(summarise(matches)) + + +if __name__ == "__main__": + main() diff --git a/utama_core/analysis/plan_snapshot.py b/utama_core/analysis/plan_snapshot.py new file mode 100644 index 00000000..8726b7bf --- /dev/null +++ b/utama_core/analysis/plan_snapshot.py @@ -0,0 +1,419 @@ +"""A picture of what `FastPathPlanner` decided for one robot on one tick (`tools/plan_snapshot.py`). + +Builds the `Game` the planner saw (from a replay frame via `replay.scenario`, or from a hand-made +state via `build_game`), runs the planner's own `_path_to`, and draws the pitch around the robot: +every robot at its real radius with its id, the ball and the ball keep-out circle when the planner +keeps out of it, each robot's clearance ring as `_get_obstacles` computes it, the target, and the +carrot `_path_to` returned. Read-only: the planner is not changed; obstacles and clearance come from +its own `_get_obstacles`, called after `_path_to`. + +Coordinates are the replay's pitch frame (metres). `side="enemy"` plans the other strategy's robot +by swapping the two robot sets and turning the team flags to match, so the pitch is not mirrored. +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass +from pathlib import Path +from typing import Optional, Sequence, Tuple + +import numpy as np # type: ignore +from matplotlib.figure import Figure +from matplotlib.lines import Line2D +from matplotlib.patches import Circle, Polygon + +from utama_core.config.field_params import STANDARD_FIELD_DIMS, FieldDimensions +from utama_core.config.physical_constants import ROBOT_RADIUS +from utama_core.config.referee_constants import BALL_KEEP_OUT_DISTANCE +from utama_core.entities.data.referee import RefereeData +from utama_core.entities.data.vector import Vector2D, Vector3D +from utama_core.entities.game import Ball, Game, GameFrame, GameHistory, Robot +from utama_core.entities.game.field import Field +from utama_core.entities.game.team_info import TeamInfo +from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.entities.referee.stage import Stage +from utama_core.motion_planning.src.fastpathplanning.planner import ( + FastPathPlanner, + _keeps_off_ball, +) +from utama_core.replay.scenario import Scenario, scenario_from_replay + +# Same ink and palette as tools/signal_report.py (FOR = friendly, AGAINST = enemy). +INK, INK_2, INK_3, SURFACE, GRID = "#0b0b0b", "#52514e", "#8a8984", "#fcfcfb", "#e4e3df" +FOR, AGAINST = "#2a78d6", "#eb6834" +CARROT = "#1baf7a" # categorical slot 3: the planner's output, distinct from the team colours +DPI = 130 + + +@dataclass(frozen=True) +class PlanSnapshot: + carrot: np.ndarray # what `_path_to` returned + inside_keep_out: bool # the robot is strictly inside the ball keep-out circle + keeps_off_ball: bool # the planner keeps out of the ball's circle on this tick + robot_to_carrot: float # metres + robot_to_ball: Optional[float] # metres, None without a ball + path: Path + + +def _referee(command: Optional[RefereeCommand]) -> Optional[RefereeData]: + if command is None: + return None + empty = TeamInfo(name="", score=0, goalkeeper=0) + return RefereeData( + source_identifier=None, + time_sent=0.0, + time_received=0.0, + referee_command=command, + referee_command_timestamp=0.0, + stage=Stage.NORMAL_FIRST_HALF, + stage_time_left=0.0, + blue_team=empty, + yellow_team=empty, + ) + + +def build_game( + friendly: dict, + enemy: dict, + ball: Optional[Ball], + *, + my_team_is_right: bool = True, + my_team_is_yellow: bool = True, + referee_command: Optional[RefereeCommand] = None, + referee: Optional[RefereeData] = None, + field_dims: FieldDimensions = STANDARD_FIELD_DIMS, + ts: float = 0.0, +) -> Game: + """A real `Game` (not a mock) from robot dicts keyed by id, as the planner reads it. + + `referee`, when given, is used as the frame's referee data instead of one built from + `referee_command` (for a replay's own team and designated-position data).""" + frame = GameFrame( + ts=ts, + my_team_is_yellow=my_team_is_yellow, + my_team_is_right=my_team_is_right, + friendly_robots=dict(friendly), + enemy_robots=dict(enemy), + ball=ball, + referee=referee if referee is not None else _referee(referee_command), + ) + field = Field(my_team_is_right=my_team_is_right, field_dims=field_dims, field_bounds=field_dims.full_field_bounds) + return Game(GameHistory(10), frame, field=field) + + +def _robot(state, is_friendly: bool) -> Robot: + return Robot( + id=state.id, + is_friendly=is_friendly, + has_ball=False, + p=Vector2D(state.x, state.y), + v=Vector2D(state.vx, state.vy), + a=Vector2D(0.0, 0.0), + orientation=state.orientation, + ) + + +def game_from_scenario(scenario: Scenario, side: str = "friendly", referee: Optional[RefereeData] = None) -> Game: + """The `Game` one side's planner sees in a replay scenario. + + The replay's friendly side is config_a: yellow, defending the right goal. `side="enemy"` makes + config_b the planned side: blue, defending the left goal, same coordinates. `referee` replaces + the referee data built from the scenario's command (see `build_game`).""" + if side not in ("friendly", "enemy"): + raise ValueError(f"side must be 'friendly' or 'enemy', not {side!r}") + planned_is_replay_friendly = side == "friendly" + mine, theirs = ( + (scenario.friendly_robots, scenario.enemy_robots) + if planned_is_replay_friendly + else (scenario.enemy_robots, scenario.friendly_robots) + ) + ball = Ball( + p=Vector3D(scenario.ball_x, scenario.ball_y, 0.0), + v=Vector3D(scenario.ball_vx, scenario.ball_vy, 0.0), + a=Vector3D(0.0, 0.0, 0.0), + ) + return build_game( + {s.id: _robot(s, True) for s in mine}, + {s.id: _robot(s, False) for s in theirs}, + ball, + my_team_is_right=planned_is_replay_friendly, + my_team_is_yellow=planned_is_replay_friendly, + referee_command=scenario.referee_command, + referee=referee, + ts=scenario.frame_ts, + ) + + +def scenario_robot_position(replay: Path, t_seconds: float, side: str, robot_id: int) -> Tuple[float, float]: + """Where `robot_id` of `side` is at `t_seconds` in the replay (replay pitch frame).""" + scenario = scenario_from_replay(replay, t_seconds) + for state in scenario.friendly_robots if side == "friendly" else scenario.enemy_robots: + if state.id == robot_id: + return state.x, state.y + raise ValueError(f"no {side} robot {robot_id} in {replay} at t={t_seconds}s") + + +def _stadium(a: np.ndarray, b: np.ndarray, r: float, n: int = 24) -> np.ndarray: + """Outline of every point within `r` of the segment `a`-`b` (a rounded rectangle).""" + d = b - a + length = float(np.hypot(d[0], d[1])) + if length < 1e-9: + t = np.linspace(0.0, 2 * math.pi, 2 * n) + return a + r * np.stack([np.cos(t), np.sin(t)], axis=1) + theta = math.atan2(d[1], d[0]) + cap_b = np.linspace(theta + math.pi / 2, theta - math.pi / 2, n) + cap_a = np.linspace(theta - math.pi / 2, theta - 3 * math.pi / 2, n) + pts = np.concatenate( + [ + (b + r * np.array([math.cos(theta + math.pi / 2), math.sin(theta + math.pi / 2)]))[None], + b + r * np.stack([np.cos(cap_b), np.sin(cap_b)], axis=1), + (b + r * np.array([math.cos(theta - math.pi / 2), math.sin(theta - math.pi / 2)]))[None], + (a + r * np.array([math.cos(theta - math.pi / 2), math.sin(theta - math.pi / 2)]))[None], + a + r * np.stack([np.cos(cap_a), np.sin(cap_a)], axis=1), + ] + ) + return pts + + +def _short(config_name: Optional[str]) -> str: + if not config_name: + return "?" + return config_name.removeprefix("build_").removesuffix("_kernel_strategy") + + +def style_axes(ax): + """The look every pitch picture shares (plan_trace, restart_picture): surface, grid, muted spines and ticks.""" + ax.set_facecolor(SURFACE) + ax.set_aspect("equal") + ax.set_axisbelow(True) + ax.grid(True, color=GRID, linewidth=0.5) + for spine in ax.spines.values(): + spine.set_color(INK_3) + spine.set_linewidth(0.6) + ax.tick_params(colors=INK_2, labelsize=7, length=2) + return ax + + +def draw_pitch(ax, field_dims: FieldDimensions = STANDARD_FIELD_DIMS) -> None: + """Outline, halfway line, centre circle, defence areas and goal mouths.""" + half_w = field_dims.full_field_half_width + ax.add_patch(Polygon(field_dims.full_field, closed=True, fill=False, edgecolor=INK_3, linewidth=0.7)) + ax.plot([0, 0], [-half_w, half_w], color=INK_3, linewidth=0.5) + ax.add_patch(Circle((0, 0), field_dims.center_circle_radius, fill=False, edgecolor=INK_3, linewidth=0.5)) + for area in (field_dims.left_defense_area, field_dims.right_defense_area): + ax.add_patch(Polygon(area, closed=True, fill=False, edgecolor=INK_3, linewidth=0.5)) + for goal in (field_dims.left_goal_line, field_dims.right_goal_line): + ax.plot(goal[:, 0], goal[:, 1], color=INK_2, linewidth=1.6, solid_capstyle="butt") + + +def robot_obstacles_for(planner: FastPathPlanner, game: Game, robot_id: int, field_bounds): + """The planner's robot obstacles (predicted runs) and this robot's clearance, as `_get_obstacles` + computes them after `_path_to`. The static field and defence-area edges are left out.""" + robot = game.friendly_robots[robot_id] + our_pos = np.array([robot.p.x, robot.p.y], dtype=float) + obstacles, clearance, _ = planner._get_obstacles(game, robot_id, our_pos, field_bounds) + n_static = len(planner._obstacle_cache_static) + return obstacles[: len(obstacles) - n_static], clearance + + +def draw_obstacles(ax, game: Game, robot_obstacles, clearance: float, *, alpha: float = 1.0) -> None: + """Each robot obstacle's predicted run (thin) and the clearance ring around it.""" + friendly_xy = {(float(r.p.x), float(r.p.y)) for r in game.friendly_robots.values()} + for a, b in robot_obstacles: + colour = FOR if (float(a[0]), float(a[1])) in friendly_xy else AGAINST + ring = _stadium(np.asarray(a, dtype=float), np.asarray(b, dtype=float), clearance) + ax.add_patch( + Polygon(ring, closed=True, fill=False, edgecolor=colour, linewidth=0.6, linestyle=":", alpha=0.8 * alpha) + ) + ax.plot([a[0], b[0]], [a[1], b[1]], color=colour, linewidth=0.8, alpha=0.6 * alpha) + + +def draw_robot( + ax, + x: float, + y: float, + robot_id, + colour: str, + *, + planned: bool = False, + edge: Optional[str] = None, + alpha: float = 0.92, + zorder: int = 5, +) -> None: + """A robot at its real radius with its id; the planned robot, or one given an `edge` colour, gets a + heavier outline.""" + ax.add_patch( + Circle( + (x, y), + ROBOT_RADIUS, + facecolor=colour, + edgecolor=edge or (INK if planned else SURFACE), + linewidth=1.8 if planned or edge else 0.6, + alpha=alpha, + zorder=zorder, + ) + ) + ax.text( + x, + y, + str(robot_id), + ha="center", + va="center", + fontsize=7, + color="white", + fontweight="bold", + alpha=min(1.0, alpha + 0.1), + zorder=zorder + 6, + ) + + +def draw_robots(ax, game: Game, planned_id: Optional[int] = None) -> None: + """Every robot of both teams, the planned one (friendly `planned_id`) outlined.""" + for is_friendly, robots in ((True, game.friendly_robots), (False, game.enemy_robots)): + colour = FOR if is_friendly else AGAINST + for r in robots.values(): + draw_robot(ax, r.p.x, r.p.y, r.id, colour, planned=is_friendly and r.id == planned_id) + + +def draw_ball(ax, ball_pos: np.ndarray, keeps_off: bool) -> None: + """The ball, and its keep-out circle while the planner keeps out of it.""" + if keeps_off: + ax.add_patch( + Circle(ball_pos, BALL_KEEP_OUT_DISTANCE, fill=False, edgecolor=INK_2, linewidth=0.8, linestyle="--") + ) + ax.plot(ball_pos[0], ball_pos[1], "o", color=INK, markersize=3.5, zorder=6) + + +def plan_snapshot( + game: Game, + robot_id: int, + target: Sequence[float], + out_path: Path, + *, + half_width: float = 1.5, + full_pitch: bool = False, + field_dims: FieldDimensions = STANDARD_FIELD_DIMS, + title: Optional[str] = None, + target_note: Optional[str] = None, +) -> PlanSnapshot: + """Run `FastPathPlanner._path_to` for `robot_id` toward `target` on `game` and draw the result. + + The window is `half_width` metres either side of the robot (`full_pitch=True` shows the whole + pitch). `target_note` is printed in the text box, e.g. to say the target is a stand-in.""" + field_bounds = field_dims.full_field_bounds + planner = FastPathPlanner(env=None) + robot = game.friendly_robots[robot_id] + our_pos = np.array([robot.p.x, robot.p.y], dtype=float) + target_xy = np.array(target, dtype=float) + + carrot = np.asarray(planner._path_to(game, robot_id, (target_xy[0], target_xy[1]), field_bounds), dtype=float) + + # Obstacles as the planner sees them: every robot within its look-ahead range (a ghost segment + # from its position to where it will be), then the static field-bound and defence-area edges. + obstacles, clearance, _ = planner._get_obstacles(game, robot_id, our_pos, field_bounds) + n_static = len(planner._obstacle_cache_static) + robot_obstacles = obstacles[: len(obstacles) - n_static] + + ball_pos = np.array([game.ball.p.x, game.ball.p.y], dtype=float) if game.ball is not None else None + keeps_off = _keeps_off_ball(game) + robot_to_ball = float(np.linalg.norm(our_pos - ball_pos)) if ball_pos is not None else None + inside = robot_to_ball is not None and robot_to_ball < BALL_KEEP_OUT_DISTANCE + robot_to_carrot = float(np.linalg.norm(carrot - our_pos)) + + fig = Figure(figsize=(6.6, 7.2), dpi=DPI, facecolor=SURFACE) + fig.subplots_adjust(left=0.13, right=0.97, top=0.92, bottom=0.16) + ax = fig.add_subplot(111) + style_axes(ax) + half_l, half_w = field_dims.full_field_half_length, field_dims.full_field_half_width + draw_pitch(ax, field_dims) + draw_obstacles(ax, game, robot_obstacles, clearance) + if ball_pos is not None: + draw_ball(ax, ball_pos, keeps_off) + draw_robots(ax, game, robot_id) + + # Target (x), the carrot the planner returned (filled dot), and the two runs from the robot. + ax.plot([our_pos[0], carrot[0]], [our_pos[1], carrot[1]], color=INK, linewidth=0.9, zorder=4) + ax.plot( + [our_pos[0], target_xy[0]], [our_pos[1], target_xy[1]], color=INK_3, linewidth=0.7, linestyle="--", zorder=3 + ) + ax.plot(target_xy[0], target_xy[1], "x", color=INK, markersize=7, markeredgewidth=1.4, zorder=8) + ax.plot(carrot[0], carrot[1], "o", color=CARROT, markeredgecolor=INK, markeredgewidth=0.6, markersize=6, zorder=9) + + if full_pitch: + ax.set_xlim(-half_l - 0.2, half_l + 0.2) + ax.set_ylim(-half_w - 0.2, half_w + 0.2) + else: + ax.set_xlim(our_pos[0] - half_width, our_pos[0] + half_width) + ax.set_ylim(our_pos[1] - half_width, our_pos[1] + half_width) + ax.set_xlabel("x (m)", fontsize=8, color=INK_2) + ax.set_ylabel("y (m)", fontsize=8, color=INK_2) + + ref = game.referee.referee_command.name if game.referee is not None else "none" + lines = [ + f"referee: {ref}", + f"keep-out {'active' if keeps_off else 'not active'} ({BALL_KEEP_OUT_DISTANCE:.2f} m)", + f"robot -> ball: {robot_to_ball:.3f} m" if robot_to_ball is not None else "robot -> ball: no ball", + f"robot -> carrot: {robot_to_carrot:.3f} m", + f"inside keep-out circle: {'YES' if inside else 'no'}", + f"clearance: {clearance:.3f} m", + ] + if not full_pitch and not _in_window(target_xy, our_pos, half_width): + lines.append(f"target outside window: ({target_xy[0]:.2f}, {target_xy[1]:.2f})") + if target_note: + lines.append(target_note) + ax.text( + 0.02, + 0.98, + "\n".join(lines), + transform=ax.transAxes, + va="top", + ha="left", + fontsize=7, + color=INK, + bbox=dict(boxstyle="round,pad=0.4", facecolor=SURFACE, edgecolor=GRID, linewidth=0.6), + zorder=10, + ) + + handles = [ + Line2D([], [], marker="o", linestyle="", markerfacecolor=FOR, markeredgecolor="none", label="planned side"), + Line2D([], [], marker="o", linestyle="", markerfacecolor=AGAINST, markeredgecolor="none", label="other side"), + Line2D([], [], marker="o", linestyle="", markerfacecolor=CARROT, markeredgecolor=INK, label="carrot"), + Line2D([], [], marker="x", linestyle="", color=INK, label="target"), + Line2D([], [], color=INK_2, linestyle="--", linewidth=0.8, label="ball keep-out"), + Line2D([], [], color=INK_3, linestyle=":", linewidth=0.8, label="clearance ring"), + ] + fig.legend( + handles=handles, + loc="lower center", + bbox_to_anchor=(0.55, 0.0), + ncol=3, + fontsize=7, + frameon=False, + labelcolor=INK_2, + ) + if title: + ax.set_title(title, fontsize=9, color=INK, loc="left") + + out_path = Path(out_path) + out_path.parent.mkdir(parents=True, exist_ok=True) + # No bbox_inches="tight": it would grow the image to take in the clipped pitch outline. + fig.savefig(out_path, dpi=DPI, facecolor=SURFACE) + + return PlanSnapshot( + carrot=carrot, + inside_keep_out=bool(inside), + keeps_off_ball=bool(keeps_off), + robot_to_carrot=robot_to_carrot, + robot_to_ball=robot_to_ball, + path=out_path, + ) + + +def title_for(scenario: Scenario, side: str, robot_id: int) -> str: + config = scenario.config_a_name if side == "friendly" else scenario.config_b_name + return f"planned {side} {_short(config)}, robot {robot_id}, t = {scenario.sim_time:g} s" + + +def _in_window(point: np.ndarray, centre: np.ndarray, half_width: float) -> bool: + return bool(np.all(np.abs(np.asarray(point) - np.asarray(centre)) <= half_width)) diff --git a/utama_core/analysis/plan_trace.py b/utama_core/analysis/plan_trace.py new file mode 100644 index 00000000..dfb2a669 --- /dev/null +++ b/utama_core/analysis/plan_trace.py @@ -0,0 +1,588 @@ +"""The fast-path planner over a time window: how one robot's plan unfolded (`tools/plan_trace.py`). + +`plan_snapshot` shows one tick. This runs the same `FastPathPlanner._path_to` on every sampled +tick of a window, builds the `Game` each tick saw from a callback (so tests can feed synthetic +games and the replay loader stays separate), and records per tick the robot's position, the +carrot the planner returned, the referee command, the robot's distance to the ball, its +clearance to the nearest obstacle and its speed. Read-only: the planner is not changed. + +One `FastPathPlanner` serves the whole window, as it does in a live run, so anything it keeps +between ticks is carried along. The target is fixed when given; otherwise each tick's target is +the robot's own position `lookahead` seconds later (the stand-in `plan_snapshot` uses). + +The picture: a pitch panel (the robot's path coloured by time, each sample's carrot joined to the +robot, the ball's path, robots at the window's start faded and at its end solid, the keep-out +circle and clearance rings at the end) above time panels that share one x axis: robot-to-ball +distance, robot-to-carrot length, speed, and the referee command as a band. +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass +from pathlib import Path +from typing import Callable, Dict, List, Optional, Sequence, Tuple + +import numpy as np # type: ignore +from matplotlib import cm +from matplotlib import colors as mcolors +from matplotlib.collections import LineCollection +from matplotlib.figure import Figure +from matplotlib.lines import Line2D + +from utama_core.analysis.findings import Finding +from utama_core.analysis.plan_snapshot import ( + AGAINST, + CARROT, + DPI, + FOR, + GRID, + INK, + INK_2, + INK_3, + SURFACE, + draw_ball, + draw_obstacles, + draw_pitch, + draw_robot, + robot_obstacles_for, + style_axes, +) +from utama_core.config.field_params import STANDARD_FIELD_DIMS, FieldDimensions +from utama_core.config.referee_constants import BALL_KEEP_OUT_DISTANCE +from utama_core.entities.game import Game +from utama_core.motion_planning.src.fastpathplanning.planner import ( + FastPathPlanner, + _keeps_off_ball, +) + +GameAt = Callable[[float], Game] + +# Single-hue ramp (light to dark FOR blue) for time; the carrot dots keep the carrot colour. +TIME_CMAP = mcolors.LinearSegmentedColormap.from_list("time", ["#d6e6fa", FOR, "#0f3d7a"]) +# Referee bands: categorical slots from tools/signal_report.py, STOP grey, BALL_PLACEMENT ink. +REFEREE_COLOURS = { + "HALT": "#e4e3df", + "STOP": "#e4e3df", + "NORMAL_START": "#1baf7a", + "FORCE_START": "#008300", + "PREPARE_KICKOFF": "#eda100", + "PREPARE_PENALTY": "#e87ba4", + "DIRECT_FREE": "#6250d6", + "INDIRECT_FREE": "#6250d6", + "TIMEOUT": "#e4e3df", + "BALL_PLACEMENT": "#52514e", +} +DARK_REFEREE = {"FORCE_START", "DIRECT_FREE", "INDIRECT_FREE", "BALL_PLACEMENT"} +# Samples whose carrot is this close to the robot, while the target is farther than +# `PARKED_TARGET_M`, are a parked robot (carrot collapsed); annotated when present. +PARKED_CARROT_M = 0.2 +PARKED_TARGET_M = 0.5 +DETOUR_M = 0.2 # carrot this far off the robot-to-target line is a detour, annotated when present +# A run of short-carrot ticks shorter than this is a blip, not a `parked` or `pushed_out` finding. +PARKED_MIN_S = 1.0 + + +@dataclass(frozen=True) +class TraceRow: + t: float # seconds, on the replay's clock + robot: Tuple[float, float] # planned robot's position + target: Tuple[float, float] # the target `_path_to` was given this tick + carrot: Tuple[float, float] # what `_path_to` returned + robot_to_carrot: float # metres + robot_to_target: float # metres + carrot_offset: float # metres the carrot sits off the robot-to-target line (0 when no distance) + robot_to_ball: Optional[float] # metres, None without a ball + clearance: float # the planner's clearance for this robot this tick, metres + speed: Optional[float] # m/s from the previous sample's position; None on the first sample + ball: Optional[Tuple[float, float]] + referee: Optional[str] # referee command name, None without a referee + + +@dataclass(frozen=True) +class PlanTrace: + rows: Tuple[TraceRow, ...] + path: Path + target_note: str # the target as drawn: the given point, or the stand-in description + + +def sample_times(t0: float, t1: float, step: float) -> List[float]: + """Every `step` seconds from `t0` to `t1` inclusive (`t1` is kept even off the grid).""" + if step <= 0: + raise ValueError(f"step must be positive, not {step}") + if t1 < t0: + raise ValueError(f"t1 ({t1}) is before t0 ({t0})") + n = int(math.floor((t1 - t0) / step + 1e-9)) + times = [round(t0 + i * step, 6) for i in range(n + 1)] + if t1 - times[-1] > 1e-6: + times.append(round(t1, 6)) + return times + + +def _referee_name(game: Game) -> Optional[str]: + if game.referee is None: + return None + return game.referee.referee_command.name + + +def _xy(p) -> Tuple[float, float]: + return float(p[0]), float(p[1]) + + +def is_short_carrot(row: TraceRow) -> bool: + """The carrot has collapsed onto the robot while the target is still far.""" + return row.robot_to_carrot < PARKED_CARROT_M and row.robot_to_target > PARKED_TARGET_M + + +def carrot_kind(row: TraceRow) -> str: + """ "pushed_out" or "parked" for a short-carrot tick. + + Inside the ball's keep-out, a short carrot farther from the ball than the robot is the planner + pushing the robot out, not a stall; any other short carrot is a parked robot.""" + if row.ball is not None and row.robot_to_ball is not None: + if row.robot_to_ball < BALL_KEEP_OUT_DISTANCE < _carrot_to_ball(row): + return "pushed_out" + return "parked" + + +def _carrot_to_ball(row: TraceRow) -> float: + return float(np.hypot(row.carrot[0] - row.ball[0], row.carrot[1] - row.ball[1])) + + +def short_carrot_label(row: TraceRow) -> str: + """Annotation for a tick whose carrot is short while the target is still far.""" + away = "" + if row.ball is not None and row.robot_to_ball is not None: + away = f", {_carrot_to_ball(row):.2f} m from the ball" + what = "pushed out of the keep-out" if carrot_kind(row) == "pushed_out" else "parked" + return f"t={row.t:g} s: {what}\ncarrot {row.robot_to_carrot:.2f} m away{away}; target {row.robot_to_target:.2f} m" + + +def _short_runs(rows: Sequence[TraceRow]) -> List[List[TraceRow]]: + """Contiguous runs of short-carrot ticks of one kind, in time order.""" + runs: List[List[TraceRow]] = [] + prev_kind: Optional[str] = None + for row in rows: + kind = carrot_kind(row) if is_short_carrot(row) else None + if kind is None: + prev_kind = None + elif kind == prev_kind: + runs[-1].append(row) + else: + runs.append([row]) + prev_kind = kind + return runs + + +def plan_findings( + rows: Sequence[TraceRow], + robot_id: int, + *, + replay: Optional[str], + side: str, + image: Optional[str] = None, +) -> List[Finding]: + """One `parked` or `pushed_out` finding per contiguous run of short-carrot ticks that lasts at least + `PARKED_MIN_S`. `rows` are one robot's samples in time order (`plan_trace(...).rows`).""" + out: List[Finding] = [] + for run in _short_runs(rows): + first, last = run[0], run[-1] + duration = last.t - first.t + if duration < PARKED_MIN_S - 1e-9: + continue + kind = carrot_kind(first) + closest = min(run, key=lambda r: r.robot_to_carrot) + out.append( + Finding( + tool="plan_trace", + kind=kind, + replay=replay, + side=side, + t0=first.t, + t1=last.t, + robots=(robot_id,), + value=duration, + threshold=PARKED_MIN_S, + unit="s", + message=( + f"R{robot_id} {'pushed out of the keep-out' if kind == 'pushed_out' else 'parked'} for " + f"{duration:.1f} s (t={first.t:g}-{last.t:g} s): carrot as close as {closest.robot_to_carrot:.2f} m, " + f"target {closest.robot_to_target:.2f} m away." + ), + image=image, + ) + ) + return out + + +def plan_trace( + game_at: GameAt, + robot_id: int, + t0: float, + t1: float, + out_path: Path, + *, + step: float = 0.1, + target: Optional[Sequence[float]] = None, + lookahead: float = 0.5, + field_dims: FieldDimensions = STANDARD_FIELD_DIMS, + title: Optional[str] = None, +) -> PlanTrace: + """Run `_path_to` for `robot_id` on every sampled tick in [t0, t1] and draw the window. + + `game_at(t)` returns the `Game` the planner sees at time `t` (seconds, the replay's clock). + `target` is fixed for the window; without it each tick's target is the robot's position at + `t + lookahead`, which needs `game_at` up to `t1 + lookahead`. The PNG goes to `out_path`.""" + field_bounds = field_dims.full_field_bounds + games: Dict[float, Game] = {} + + def game(t: float) -> Game: + key = round(t, 6) + if key not in games: + games[key] = game_at(t) + return games[key] + + planner = FastPathPlanner(env=None) + rows: List[TraceRow] = [] + times = sample_times(t0, t1, step) + last = None # (game, robot obstacles, clearance, carrot) of the window's final tick + for t in times: + g = game(t) + if robot_id not in g.friendly_robots: + raise ValueError(f"no friendly robot {robot_id} in the game at t={t} s") + robot = g.friendly_robots[robot_id] + pos = np.array([robot.p.x, robot.p.y], dtype=float) + if target is not None: + tgt = np.array(target, dtype=float) + else: + ahead = game(t + lookahead).friendly_robots[robot_id].p + tgt = np.array([ahead.x, ahead.y], dtype=float) + carrot = np.asarray(planner._path_to(g, robot_id, (tgt[0], tgt[1]), field_bounds), dtype=float) + robot_obstacles, clearance = robot_obstacles_for(planner, g, robot_id, field_bounds) + + to_target = tgt - pos + dist_target = float(np.linalg.norm(to_target)) + to_carrot = carrot - pos + if dist_target > 1e-9: + unit = to_target / dist_target + offset = abs(float(unit[0] * to_carrot[1] - unit[1] * to_carrot[0])) + else: + offset = 0.0 + ball = g.ball + ball_xy = _xy((ball.p.x, ball.p.y)) if ball is not None else None + robot_to_ball = float(np.linalg.norm(pos - np.array(ball_xy))) if ball_xy is not None else None + speed = None + if rows: + prev = rows[-1] + dt = t - prev.t + speed = float(np.linalg.norm(pos - np.array(prev.robot))) / dt if dt > 0 else None + rows.append( + TraceRow( + t=float(t), + robot=_xy(pos), + target=_xy(tgt), + carrot=_xy(carrot), + robot_to_carrot=float(np.linalg.norm(to_carrot)), + robot_to_target=dist_target, + carrot_offset=offset, + robot_to_ball=robot_to_ball, + clearance=float(clearance), + speed=speed, + ball=ball_xy, + referee=_referee_name(g), + ) + ) + last = (g, robot_obstacles, float(clearance), carrot) + + note = ( + f"target: ({target[0]:.2f}, {target[1]:.2f}) fixed" + if target is not None + else f"target: stand-in, robot at t+{lookahead:g} s" + ) + first_game = game(times[0]) + end_game, end_obstacles, end_clearance, _ = last + _draw( + rows, + first_game, + end_game, + end_obstacles, + end_clearance, + robot_id, + out_path, + field_dims=field_dims, + title=title, + target_note=note, + fixed_target=target is not None, + ) + return PlanTrace(rows=tuple(rows), path=Path(out_path), target_note=note) + + +def _draw( + rows: List[TraceRow], + first: Game, + end: Game, + end_obstacles, + end_clearance: float, + robot_id: int, + out_path: Path, + *, + field_dims: FieldDimensions, + title: Optional[str], + target_note: str, + fixed_target: bool, +) -> None: + t = np.array([r.t for r in rows]) + robot_xy = np.array([r.robot for r in rows]) + carrot_xy = np.array([r.carrot for r in rows]) + ball_rows = [r.ball for r in rows] + t0, t1 = float(t[0]), float(t[-1]) + norm = mcolors.Normalize(vmin=t0, vmax=max(t1, t0 + 1e-6)) + + fig = Figure(figsize=(7.0, 10.4), dpi=DPI, facecolor=SURFACE) + gs = fig.add_gridspec( + 5, + 1, + height_ratios=[3.3, 1.0, 1.0, 1.0, 0.35], + left=0.12, + right=0.95, + top=0.95, + bottom=0.1, + hspace=0.32, + ) + ax = fig.add_subplot(gs[0]) + style_axes(ax) + draw_pitch(ax, field_dims) + + # Window the robot moved through, plus its carrots and target, with a margin. + pts = np.vstack([robot_xy, carrot_xy] + ([np.array([rows[0].target])] if fixed_target else [])) + lo, hi = pts.min(axis=0) - 0.4, pts.max(axis=0) + 0.4 + centre = (lo + hi) / 2 + half = max(float(np.max(hi - lo)) / 2, 0.6) + ax.set_xlim(centre[0] - half, centre[0] + half) + ax.set_ylim(centre[1] - half, centre[1] + half) + + # Other robots: at t0 faded, at t1 solid. The planned robot is drawn at t1 only, as the path's end. + for is_friendly, robots in ((True, first.friendly_robots), (False, first.enemy_robots)): + colour = FOR if is_friendly else AGAINST + for r in robots.values(): + if is_friendly and r.id == robot_id: + continue + draw_robot(ax, r.p.x, r.p.y, r.id, colour, alpha=0.2, zorder=3) + for is_friendly, robots in ((True, end.friendly_robots), (False, end.enemy_robots)): + colour = FOR if is_friendly else AGAINST + for r in robots.values(): + if is_friendly and r.id == robot_id: + continue + draw_robot(ax, r.p.x, r.p.y, r.id, colour) + draw_obstacles(ax, end, end_obstacles, end_clearance) + + # Ball path (t0 to t1), and the keep-out circle at t1 while the planner keeps out of it. + ball_pts = np.array([b for b in ball_rows if b is not None]) + if len(ball_pts) > 0: + ax.plot(ball_pts[:, 0], ball_pts[:, 1], color=INK_2, linewidth=0.8, linestyle="-.", zorder=4) + ax.plot( + ball_pts[0, 0], ball_pts[0, 1], "o", markerfacecolor=SURFACE, markeredgecolor=INK, markersize=3, zorder=6 + ) + if end.ball is not None: + end_ball = np.array([end.ball.p.x, end.ball.p.y], dtype=float) + draw_ball(ax, end_ball, _keeps_off_ball(end)) + + # Carrot per sample: a thin line from the robot to the carrot it returned, and a small dot. + segs = np.stack([robot_xy, carrot_xy], axis=1) + ax.add_collection(LineCollection(segs, colors=CARROT, linewidths=0.5, alpha=0.55, zorder=5)) + ax.plot(carrot_xy[:, 0], carrot_xy[:, 1], "o", color=CARROT, markersize=2.2, markeredgewidth=0, zorder=6) + + # The robot's path, each segment coloured by its start time. + if len(rows) > 1: + path_segs = np.stack([robot_xy[:-1], robot_xy[1:]], axis=1) + path_colours = TIME_CMAP(norm(t[:-1])) + ax.add_collection(LineCollection(path_segs, colors=path_colours, linewidths=2.4, zorder=7, capstyle="round")) + ax.plot(robot_xy[-1, 0], robot_xy[-1, 1], "s", color=INK, markersize=3, zorder=8) + + if fixed_target: + ax.plot(rows[0].target[0], rows[0].target[1], "x", color=INK, markersize=7, markeredgewidth=1.4, zorder=9) + + # Annotations: at most two. A parked robot (carrot collapsed, target still far) and a detour. + annotations = [] + parked = [r for r in rows if r.robot_to_target > PARKED_TARGET_M] + if parked: + worst = min(parked, key=lambda r: r.robot_to_carrot) + if worst.robot_to_carrot < PARKED_CARROT_M: + annotations.append( + ( + worst.t, + worst.robot_to_carrot, + short_carrot_label(worst), + ) + ) + detour = max(rows, key=lambda r: r.carrot_offset) + if detour.carrot_offset > DETOUR_M: + ax.annotate( + f"t={detour.t:g} s: carrot {detour.carrot_offset:.2f} m off the\nrobot-to-target line", + xy=detour.carrot, + xytext=(0.98, 0.97), + textcoords="axes fraction", + ha="right", + va="top", + fontsize=6.5, + color=INK, + bbox=dict(boxstyle="square,pad=0.2", facecolor=SURFACE, edgecolor="none"), + arrowprops=dict(arrowstyle="-", linewidth=0.6, color=INK_2), + zorder=12, + ) + + ref_names = [r.referee for r in rows] + lines = [ + f"t = {t0:g} to {t1:g} s, {len(rows)} samples", + target_note, + f"min robot-to-ball: {min((r.robot_to_ball for r in rows if r.robot_to_ball is not None), default=float('nan')):.2f} m", + f"max carrot offset: {max(r.carrot_offset for r in rows):.2f} m", + ] + if ref_names and ref_names[-1]: + lines.append(f"referee at t1: {ref_names[-1]}") + ax.text( + 0.02, + 0.98, + "\n".join(lines), + transform=ax.transAxes, + va="top", + ha="left", + fontsize=6.5, + color=INK, + bbox=dict(boxstyle="round,pad=0.35", facecolor=SURFACE, edgecolor=GRID, linewidth=0.6), + zorder=13, + ) + sm = cm.ScalarMappable(norm=norm, cmap=TIME_CMAP) + sm.set_array([]) + cbar = fig.colorbar(sm, ax=ax, shrink=0.55, aspect=18, pad=0.02) + cbar.set_label("time (s)", fontsize=7, color=INK_2) + cbar.ax.tick_params(labelsize=6.5, colors=INK_2, length=2) + cbar.outline.set_edgecolor(INK_3) + ax.set_xlabel("x (m)", fontsize=8, color=INK_2) + ax.set_ylabel("y (m)", fontsize=8, color=INK_2) + if title: + ax.set_title(title, fontsize=9, color=INK, loc="left") + + # Time panels: one quantity each, sharing the x axis. + ax_ball = fig.add_subplot(gs[1]) + ax_carrot = fig.add_subplot(gs[2], sharex=ax_ball) + ax_speed = fig.add_subplot(gs[3], sharex=ax_ball) + ax_ref = fig.add_subplot(gs[4], sharex=ax_ball) + for a in (ax_ball, ax_carrot, ax_speed): + style_axes(a) + a.set_aspect("auto") + a.grid(True, axis="y", color=GRID, linewidth=0.5) + a.tick_params(labelbottom=False) + + ball_d = np.array([np.nan if r.robot_to_ball is None else r.robot_to_ball for r in rows]) + ax_ball.plot(t, ball_d, color=FOR, linewidth=1.1) + ax_ball.axhline(BALL_KEEP_OUT_DISTANCE, color=INK_2, linewidth=0.8, linestyle="--") + ax_ball.text( + t1, + BALL_KEEP_OUT_DISTANCE, + f" keep-out {BALL_KEEP_OUT_DISTANCE:.1f} m", + va="bottom", + ha="right", + fontsize=6.5, + color=INK_2, + ) + ax_ball.set_ylabel("robot-ball\n(m)", fontsize=7, color=INK_2) + + carrot_len = np.array([r.robot_to_carrot for r in rows]) + ax_carrot.plot(t, carrot_len, color=CARROT, linewidth=1.1) + ax_carrot.set_ylabel("robot-carrot\n(m)", fontsize=7, color=INK_2) + ax_carrot.set_ylim(bottom=0) + if annotations: + at, val, text = annotations[0] + ax_carrot.annotate( + text, + xy=(at, val), + # Text on whichever side of the point has more room, so it stays inside the panel. + xytext=(0.97 if at < (t0 + t1) / 2 else 0.03, 0.6), + textcoords="axes fraction", + ha="right" if at < (t0 + t1) / 2 else "left", + fontsize=6.5, + color=INK, + arrowprops=dict(arrowstyle="-", linewidth=0.6, color=INK_2), + zorder=12, + ) + + speed = np.array([np.nan if r.speed is None else r.speed for r in rows]) + ax_speed.plot(t, speed, color=INK_2, linewidth=1.0) + ax_speed.set_ylabel("speed\n(m/s)", fontsize=7, color=INK_2) + ax_speed.set_ylim(bottom=0) + + # Referee band: a span per run of one command, labelled when wide enough. + style_axes(ax_ref) + ax_ref.set_aspect("auto") + ax_ref.set_yticks([]) + ax_ref.set_ylim(0, 1) + ax_ref.set_ylabel("referee", fontsize=7, color=INK_2, rotation=0, ha="right", va="center") + spans = _referee_spans(rows, t1) + for name, start, end_t in spans: + colour = REFEREE_COLOURS.get(_family(name), "#e4e3df") + ax_ref.axvspan(start, end_t, color=colour, linewidth=0) + if end_t - start > 0.12 * max(t1 - t0, 1e-6): + ax_ref.text( + (start + end_t) / 2, + 0.5, + _band_label(name), + ha="center", + va="center", + fontsize=6, + color="white" if _family(name) in DARK_REFEREE else INK, + clip_on=True, + ) + ax_ref.set_xlim(t0, t1 if t1 > t0 else t0 + 1e-6) + ax_ref.set_xlabel("time (s)", fontsize=8, color=INK_2) + ax_ref.tick_params(labelbottom=True, labelsize=7) + + handles = [ + Line2D([], [], color=TIME_CMAP(0.6), linewidth=2.4, label="robot path (time)"), + Line2D([], [], marker="o", linestyle="", color=CARROT, markersize=4, label="carrot"), + Line2D([], [], color=INK_2, linestyle="-.", linewidth=0.8, label="ball path"), + Line2D([], [], color=INK_2, linestyle="--", linewidth=0.8, label="ball keep-out (t1)"), + Line2D([], [], color=INK_3, linestyle=":", linewidth=0.8, label="clearance (t1)"), + ] + fig.legend( + handles=handles, + loc="lower center", + bbox_to_anchor=(0.53, 0.0), + ncol=3, + fontsize=6.5, + frameon=False, + labelcolor=INK_2, + ) + + out_path = Path(out_path) + out_path.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(out_path, dpi=DPI, facecolor=SURFACE) + + +def _family(name: str) -> str: + """The referee command's band colour key: PREPARE_KICKOFF_YELLOW -> PREPARE_KICKOFF.""" + for prefix in sorted(REFEREE_COLOURS, key=len, reverse=True): + if name.startswith(prefix): + return prefix + return "" + + +def _band_label(name: str) -> str: + """The band's label: the command without its side suffix (PREPARE_KICKOFF_YELLOW -> PREPARE_KICKOFF).""" + for suffix in ("_YELLOW", "_BLUE"): + if name.endswith(suffix): + return name[: -len(suffix)] + return name + + +def _referee_spans(rows: List[TraceRow], t1: float) -> List[Tuple[str, float, float]]: + """(command, start, end) for each run of one referee command over the samples.""" + spans: List[Tuple[str, float, float]] = [] + for i, r in enumerate(rows): + name = r.referee or "none" + end_t = rows[i + 1].t if i + 1 < len(rows) else t1 + if spans and spans[-1][0] == name: + spans[-1] = (name, spans[-1][1], end_t) + else: + spans.append((name, r.t, end_t)) + return spans diff --git a/utama_core/analysis/ratings.py b/utama_core/analysis/ratings.py new file mode 100644 index 00000000..c854751d --- /dev/null +++ b/utama_core/analysis/ratings.py @@ -0,0 +1,118 @@ +"""Strategy ratings with uncertainty, from match margins. + +Model: a match's margin (goal difference, config_a minus config_b) is strength(a) - strength(b) +plus noise, normal with SD `noise_sd`; every strength has a normal prior, mean 0 and SD +`PRIOR_SD`. The posterior is normal and closed-form (Bayesian ridge regression), so a strength +comes with its SD, and two strengths with the probability one is the larger. + +A strength is in goals per match against an opponent of strength 0. The prior centres the +ratings of whoever has played on 0, so compare strengths within one fit (`p_better`), not +across fits. + +`noise_sd` is fitted from the residuals, shrunk toward `NOISE_SD` (the per-match spread of +goal difference left after strengths in `tournament_20261016_201213`, 66 matches) with the +weight of `NOISE_PRIOR_N` matches, so a few matches cannot make it tiny. +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass + +import numpy as np + +PRIOR_SD = 1.0 # goals per match; strengths in the 2026-10-16 baseline spread with SD ~0.7 +NOISE_SD = 1.6 +NOISE_PRIOR_N = 10 + + +@dataclass +class Ratings: + names: list[str] + mean: np.ndarray + cov: np.ndarray + noise_sd: float + n_matches: int + + def _i(self, name: str) -> int: + return self.names.index(name) + + def strength(self, name: str) -> tuple[float, float]: + """(mean, SD) of `name`'s strength.""" + i = self._i(name) + return float(self.mean[i]), math.sqrt(self.cov[i, i]) + + def diff(self, x: str, y: str) -> tuple[float, float]: + """(mean, SD) of strength(x) - strength(y): x's expected margin over y.""" + i, j = self._i(x), self._i(y) + var = self.cov[i, i] + self.cov[j, j] - 2 * self.cov[i, j] + return float(self.mean[i] - self.mean[j]), math.sqrt(max(var, 0.0)) + + def p_better(self, x: str, y: str) -> float: + """Posterior probability that x is stronger than y.""" + m, s = self.diff(x, y) + return 0.5 * (1 + math.erf(m / (s * math.sqrt(2)))) if s > 0 else float(m > 0) + + +def fit( + matches: list[tuple[str, str, float]], + names: list[str] | None = None, + prior_sd: float = PRIOR_SD, + noise_sd: float | None = None, +) -> Ratings: + """Ratings from `(config_a, config_b, margin)` triples. `names` adds strategies with no + matches yet (they keep the prior). `noise_sd`, if given, is used as is instead of fitted.""" + names = sorted(set(names or []) | {a for a, _, _ in matches} | {b for _, b, _ in matches}) + ix = {n: i for i, n in enumerate(names)} + A = np.zeros((len(matches), len(names))) + y = np.array([m for _, _, m in matches], float) + for row, (a, b, _) in enumerate(matches): + A[row, ix[a]] += 1.0 + A[row, ix[b]] -= 1.0 + prior_precision = np.eye(len(names)) / prior_sd**2 + + def posterior(sd: float) -> tuple[np.ndarray, np.ndarray]: + cov = np.linalg.inv(A.T @ A / sd**2 + prior_precision) + return cov @ A.T @ y / sd**2, cov + + sd = noise_sd + if sd is None: + sd = NOISE_SD + for _ in range(5): # a few fixed-point steps: the residuals depend on the fit + mean, _ = posterior(sd) + rss = float(np.sum((y - A @ mean) ** 2)) + dof = max(len(matches) - len(names) + 1, 0) + sd = math.sqrt((NOISE_PRIOR_N * NOISE_SD**2 + rss) / (NOISE_PRIOR_N + dof)) + mean, cov = posterior(sd) + return Ratings(names, mean, cov, sd, len(matches)) + + +PAIRED_PRIOR_N = 6 # weight, in paired differences, of the prior that pairing removes no noise + + +def paired_difference(diffs: list[float], head_to_head: list[float], noise_sd: float) -> tuple[float, float]: + """(mean, SD) of strength(candidate) - strength(baseline) from paired matches. + + `diffs`: for each opponent, seed and colour both played, the candidate's goal difference minus + the baseline's. Same opponent and same noise, so much of the noise cancels: all of it for + identical code, little for unrelated strategies. Their variance is estimated from the diffs + themselves, shrunk toward 2 * noise_sd^2 (no cancelling) with the weight of + `PAIRED_PRIOR_N` diffs, so a few equal results cannot make it tiny. + `head_to_head`: the candidate's margins against the baseline, each with variance noise_sd^2. + The prior on the difference is normal, mean 0, SD sqrt(2) * PRIOR_SD (two strengths).""" + precision = 1 / (2 * PRIOR_SD**2) + weighted = 0.0 + if diffs: + d = np.asarray(diffs, float) + ss = float(np.sum((d - d.mean()) ** 2)) + var_d = (PAIRED_PRIOR_N * 2 * noise_sd**2 + ss) / (PAIRED_PRIOR_N + len(d) - 1) + precision += len(d) / var_d + weighted += d.sum() / var_d + if head_to_head: + precision += len(head_to_head) / noise_sd**2 + weighted += float(np.sum(head_to_head)) / noise_sd**2 + return weighted / precision, math.sqrt(1 / precision) + + +def p_positive(mean: float, sd: float) -> float: + return 0.5 * (1 + math.erf(mean / (sd * math.sqrt(2)))) diff --git a/utama_core/analysis/realism/__init__.py b/utama_core/analysis/realism/__init__.py new file mode 100644 index 00000000..30312b00 --- /dev/null +++ b/utama_core/analysis/realism/__init__.py @@ -0,0 +1,2 @@ +"""Sim against real matches: shot detection, expected goals, shot outcomes and the realism +scorecard. See `docs/sim_realism.md`.""" diff --git a/utama_core/analysis/realism/passes.py b/utama_core/analysis/realism/passes.py new file mode 100644 index 00000000..4282f5f6 --- /dev/null +++ b/utama_core/analysis/realism/passes.py @@ -0,0 +1,136 @@ +"""Pass accuracy, by proxy: the logs record no intended receiver, so the receiver is inferred. + +Passes are softer than shots, so they get their own kick detector: in live play, the ball's +speed rises by `KICK_DV_MPS` within `shots.KICK_WINDOW_S` to at least `KICK_MPS`, with a robot +(the kicker) within `KICKER_M` of where the ball was. Kicks that are shots (`shots.scan`) are +left out. The kick's direction is the ball's velocity `shots.SETTLE_S` after it. Its **target** +is the kicker's teammate nearest the kick's line among those ahead of the ball by `AHEAD_M`, +and at most `LINE_M` off the line; a kick with no such teammate (a clearance, a dribble push) +is not a pass. Per pass: + +- `offset_m`: that teammate's distance from the line at the kick: how far off the kick was, + plus however far the receiver still had to move; +- `completed`: the next robot other than the kicker within `shot_outcomes.TOUCH_M` of the + ball, at most `FOLLOW_S` later and before the next kick, is a teammate. + +Both sources go through the same inference, so read real against sim, not as an aim error. +""" + +from __future__ import annotations + +import bisect +import math + +from utama_core.analysis.chances import LIVE +from utama_core.analysis.realism.shot_outcomes import SKIP_S, TOUCH_M +from utama_core.analysis.realism.shots import ( + KICK_WINDOW_S, + REFRACTORY_S, + SETTLE_S, + on_field, + team, +) + +KICK_MPS = 1.0 +KICK_DV_MPS = 0.8 +KICKER_M = 0.3 +AHEAD_M = (0.5, 6.0) +LINE_M = 1.0 +FOLLOW_S = 3.0 + + +def _seen(ball) -> bool: + return ball is not None and not math.isnan(ball.p.x) + + +def _kicker(frame, x, y): + best = None + for side in ("friendly", "enemy"): + for rid, r in team(frame, side).items(): + if on_field(r): + d = math.hypot(r.p.x - x, r.p.y - y) + if d <= KICKER_M and (best is None or d < best[0]): + best = (d, side, rid) + return None if best is None else best[1:] + + +def kicks(frames) -> list[tuple[int, float, float]]: + """(frame index, x, y where the ball was) per live kick, `REFRACTORY_S` apart.""" + out, history, last = [], [], -math.inf + for i, f in enumerate(frames): + b = f.ball + if not _seen(b): + continue + history.append(i) + while frames[i].ts - frames[history[0]].ts > KICK_WINDOW_S: + history.pop(0) + live = f.referee is not None and f.referee.referee_command in LIVE + before = frames[history[0]].ball + speed = math.hypot(b.v.x, b.v.y) + rise = speed - math.hypot(before.v.x, before.v.y) + if live and speed >= KICK_MPS and rise >= KICK_DV_MPS and f.ts - last >= REFRACTORY_S: + last = f.ts + out.append((i, before.p.x, before.p.y)) + return out + + +def follow(frames, shot_times) -> list[dict]: + """One record per pass: kicker side, `offset_m`, `completed`, ball speed.""" + ts = [f.ts for f in frames] + found = kicks(frames) + starts = [frames[i].ts for i, _, _ in found] + shot_times = sorted(shot_times) + out = [] + for n, (i, x, y) in enumerate(found): + # a shot detected within a kick window of this kick is the same kick + k = bisect.bisect_left(shot_times, ts[i] - KICK_WINDOW_S) + if k < len(shot_times) and shot_times[k] <= ts[i] + KICK_WINDOW_S: + continue + j = bisect.bisect_left(ts, ts[i] + SETTLE_S) + if j >= len(frames) or not _seen(frames[j].ball): + continue + b = frames[j].ball + speed = math.hypot(b.v.x, b.v.y) + if speed < KICK_MPS: + continue + ux, uy = b.v.x / speed, b.v.y / speed + kicker = _kicker(frames[i], x, y) + if kicker is None: + continue + side, kid = kicker + offsets = [] + for rid, r in team(frames[i], side).items(): + if rid == kid or not on_field(r): + continue + rx, ry = r.p.x - x, r.p.y - y + along = rx * ux + ry * uy + off = abs(rx * uy - ry * ux) + if AHEAD_M[0] <= along <= AHEAD_M[1] and off <= LINE_M: + offsets.append(off) + if not offsets: + continue + nxt = starts[n + 1] if n + 1 < len(starts) else math.inf + completed = False + for f in frames[i:]: + dt = f.ts - ts[i] + if dt > FOLLOW_S or f.ts >= nxt: + break + fb = f.ball + if dt <= SKIP_S or not _seen(fb): + continue + hit = next( + ( + (s, rid) + for s in ("friendly", "enemy") + for rid, r in team(f, s).items() + if not (s == side and rid == kid) + and on_field(r) + and math.hypot(r.p.x - fb.p.x, r.p.y - fb.p.y) <= TOUCH_M + ), + None, + ) + if hit: + completed = hit[0] == side + break + out.append({"side": side, "offset_m": round(min(offsets), 3), "completed": completed, "speed": round(speed, 2)}) + return out diff --git a/utama_core/analysis/realism/reaction.py b/utama_core/analysis/realism/reaction.py new file mode 100644 index 00000000..3014019a --- /dev/null +++ b/utama_core/analysis/realism/reaction.py @@ -0,0 +1,143 @@ +"""How fast the keeper and the nearest defender react to a shot: time from the kick until their +velocity changes, and until they move toward the shot's line. + +Velocity comes from positions by central difference over `VEL_SPAN_FRAMES` frames either side, +for real logs and our sim alike (the real tracker's own velocity lags differently). The +baseline is the robot's mean velocity over `BASELINE_S` before the kick, so a robot already +moving is not counted as reacting. A reaction is the first frame whose velocity differs from +the baseline by `REACT_DV_MPS` (`*_react_s`), or whose change points toward the shot line by +that much (`*_toward_s`, only for a robot at least `ON_LINE_M` off the line). + +The search runs until the ball passes the robot along the shot, `MAX_S` after the kick, or the +next shot, whichever is first; that length is `*_window_s`. A shot that gives no time to react +says nothing about reaction time, so the scorecard only uses shots with a long enough window. + +Caveats: the real tracker filters positions, which delays an onset by a few tens of +milliseconds; the central difference looks `VEL_SPAN_FRAMES` frames ahead, which advances it by +about as much on both sources. The kick time is the shot detector's, a frame or two after +contact on real logs. Read real and sim against each other, not as absolute latencies. +""" + +from __future__ import annotations + +import bisect +import math +from typing import Optional + +from utama_core.analysis.realism.shots import HALF_LEN, keeper, on_field, other, team + +VEL_SPAN_FRAMES = 2 +BASELINE_S = 0.05 +REACT_DV_MPS = 0.25 +ON_LINE_M = 0.05 +MAX_S = 1.0 +DEFENDER_LINE_M = 1.0 # the nearest field defender this close to the shot segment + + +def _goal_sign(frame, side): + friendly_goal = -1.0 if frame.my_team_is_right else 1.0 + return friendly_goal if side == "friendly" else -friendly_goal + + +def _project(px, py, ax, ay, bx, by): + """(t along a->b in [0, 1] unclamped, distance from the line, unit normal from p to it).""" + dx, dy = bx - ax, by - ay + length_sq = dx * dx + dy * dy + t = ((px - ax) * dx + (py - ay) * dy) / length_sq + cx, cy = ax + t * dx, ay + t * dy + d = math.hypot(cx - px, cy - py) + n = ((cx - px) / d, (cy - py) / d) if d > 1e-9 else (0.0, 0.0) + return t, d, n + + +def _positions(frames, lo, hi, side, rid): + out = [] + for f in frames[lo:hi]: + r = team(f, side).get(rid) + if r is not None and on_field(r): + out.append((f.ts, r.p.x, r.p.y)) + return out + + +def _velocities(track): + k = VEL_SPAN_FRAMES + out = [] + for i in range(k, len(track) - k): + t0, x0, y0 = track[i - k] + t1, x1, y1 = track[i + k] + if t1 > t0: + out.append((track[i][0], (x1 - x0) / (t1 - t0), (y1 - y0) / (t1 - t0))) + return out + + +def onsets(track, t_kick, t_end, normal) -> tuple[Optional[float], Optional[float]]: + """(seconds to any velocity change, seconds to a change toward the line) in [t_kick, t_end].""" + vel = _velocities(track) + base = [(vx, vy) for t, vx, vy in vel if t_kick - BASELINE_S <= t <= t_kick] + if not base: + return None, None + bx = sum(v[0] for v in base) / len(base) + by = sum(v[1] for v in base) / len(base) + react = toward = None + for t, vx, vy in vel: + if t <= t_kick or t > t_end: + continue + dvx, dvy = vx - bx, vy - by + if react is None and math.hypot(dvx, dvy) >= REACT_DV_MPS: + react = round(t - t_kick, 3) + if toward is None and normal is not None and dvx * normal[0] + dvy * normal[1] >= REACT_DV_MPS: + toward = round(t - t_kick, 3) + if react is not None and (toward is not None or normal is None): + break + return react, toward + + +def measure(frames, shots: list[dict]) -> None: + """Add `keeper_*` and `defender_*` reaction fields to each shot, in place.""" + ts = [f.ts for f in frames] + times = sorted(s["t"] for s in shots) + for s in shots: + i = bisect.bisect_left(ts, s["t"]) + if i >= len(frames): + continue + f0 = frames[i] + defending = other(s["side"]) + g = _goal_sign(f0, s["side"]) + ox, oy = s["raw_x"], s["raw_y"] + gx, gy = g * HALF_LEN, s["y_at_goal"] * g + if math.hypot(gx - ox, gy - oy) < 1e-6: + continue + nxt = next((t for t in times if t > s["t"] + 1e-6), math.inf) + kid, kp = keeper(f0, defending) + robots = [] + if kp is not None and on_field(kp) and abs(kp.p.x - gx) < 2.0: + robots.append(("keeper", kid, kp)) + field = [] + for rid, r in team(f0, defending).items(): + if rid == kid or not on_field(r): + continue + t_along, d, _ = _project(r.p.x, r.p.y, ox, oy, gx, gy) + if 0.0 < t_along < 1.0 and d <= DEFENDER_LINE_M: + field.append((d, rid, r)) + if field: + _, rid, r = min(field, key=lambda x: x[0]) + robots.append(("defender", rid, r)) + for role, rid, r in robots: + t_along, d, n = _project(r.p.x, r.p.y, ox, oy, gx, gy) + t_end = min(s["t"] + MAX_S, nxt) + for f in frames[i:]: # until the ball passes the robot along the shot + if f.ts > t_end: + break + b = f.ball + if b is not None and not math.isnan(b.p.x): + if _project(b.p.x, b.p.y, ox, oy, gx, gy)[0] >= min(t_along, 1.0): + t_end = f.ts + break + lo = bisect.bisect_left(ts, s["t"] - BASELINE_S - 0.1) + hi = bisect.bisect_right(ts, t_end + 0.1) + track = _positions(frames, lo, hi, defending, rid) + react, toward = onsets(track, s["t"], t_end, n if d >= ON_LINE_M else None) + s[f"{role}_react_s"] = react + s[f"{role}_toward_s"] = toward + s[f"{role}_window_s"] = round(t_end - s["t"], 3) + s[f"{role}_line_m"] = round(d, 3) diff --git a/utama_core/analysis/realism/real_2025_2026.json b/utama_core/analysis/realism/real_2025_2026.json new file mode 100644 index 00000000..c34da33e --- /dev/null +++ b/utama_core/analysis/realism/real_2025_2026.json @@ -0,0 +1,309 @@ +{ + "sources": [ + "official-logs/2025", + "official-logs/2026" + ], + "matches": 53, + "attempts": 959, + "goals_from_attempts": 216, + "xg_model": { + "features": [ + "log_dist", + "angle", + "open_goal", + "og_keeper", + "no_keeper", + "peak", + "free_kick", + "kickoff", + "penalty" + ], + "mu": [ + 1.1669862148045886, + 0.5284343749961606, + 0.4188029197080292, + 0.6807090719499473, + 0.056308654848800835, + 4.810636079249216, + 0.37122002085505734, + 0.06569343065693431, + 0.0031282586027111575 + ], + "sd": [ + 0.4306277958271294, + 0.4399006727963889, + 0.3379020879833778, + 0.22253176422137147, + 0.23051679049247956, + 1.0960297647166253, + 0.4831311601808429, + 0.2477454425836078, + 0.0558432870854878 + ], + "w": [ + -1.8661095725864691, + -0.2955012335021089, + -0.21409972719244905, + 1.3208414439801872, + -0.09851365097398686, + -0.13742826237275188, + 0.39821056046981823, + -0.501789377254026, + -0.076640576435085, + -0.2925168906405054 + ] + }, + "xg_check": { + "shots": 959, + "scored": 216, + "log_loss": 0.40421868583376075, + "log_loss_constant": 0.5461479904764098, + "brier": 0.1329963194665174, + "brier_constant": 0.17892988439404492, + "auc": 0.8330155525646777, + "calibration": [ + [ + 0.012544628140241784, + 0.0, + 96 + ], + [ + 0.022391891972937273, + 0.010416666666666666, + 96 + ], + [ + 0.03651274310039746, + 0.020833333333333332, + 96 + ], + [ + 0.060986840724056655, + 0.09375, + 96 + ], + [ + 0.10426069761842487, + 0.125, + 96 + ], + [ + 0.17745951036422655, + 0.19791666666666666, + 96 + ], + [ + 0.2754857063230783, + 0.2916666666666667, + 96 + ], + [ + 0.3928159309083097, + 0.3229166666666667, + 96 + ], + [ + 0.5045936788542781, + 0.6041666666666666, + 96 + ], + [ + 0.6634168757529989, + 0.5833333333333334, + 96 + ] + ] + }, + "rows": { + "attempts per team per live minute": { + "value": 1.2745202275264471, + "lo": 1.0079004463861407, + "hi": 1.5486496056834096 + }, + "open-play attempts per team per live minute": { + "value": 0.7136781670299293, + "lo": 0.5852663815341502, + "hi": 0.8526985716496699 + }, + "set-piece attempts per team per live minute": { + "value": 0.560842060496518, + "lo": 0.39148618615855224, + "hi": 0.7315851590479346 + }, + "attempt distance, median (m)": { + "value": 3.534, + "lo": 3.258, + "hi": 3.8166374999999997 + }, + "attempts beyond 4 m, share": { + "value": 0.4129301355578728, + "lo": 0.35437823161010157, + "hi": 0.4709173694371062 + }, + "real xG per attempt": { + "value": 0.22523461939520337, + "lo": 0.18777054285126077, + "hi": 0.27442292433703064 + }, + "finishing (goals / real xG)": { + "value": 0.9999999999999999, + "lo": 0.8692411270504213, + "hi": 1.1429872532517718 + }, + "goal rate, xG 0-0.05": { + "value": 0.006535947712418301, + "lo": 0.0, + "hi": 0.016877637130801686 + }, + "goal rate, xG 0.05-0.15": { + "value": 0.12698412698412698, + "lo": 0.08716346153846154, + "hi": 0.17284284284284282 + }, + "goal rate, xG 0.15-0.35": { + "value": 0.2459016393442623, + "lo": 0.17672899048625793, + "hi": 0.33507853403141363 + }, + "goal rate, xG 0.35-1": { + "value": 0.5160142348754448, + "lo": 0.44202477249747224, + "hi": 0.5909136915715864 + }, + "outcome: goal": { + "value": 0.22523461939520334, + "lo": 0.17460385861051866, + "hi": 0.29616294921324804 + }, + "outcome: keeper": { + "value": 0.18143899895724713, + "lo": 0.1457806477615062, + "hi": 0.22071343532121512 + }, + "outcome: keeper, xG 0-0.05": { + "value": 0.058823529411764705, + "lo": 0.03182312629174372, + "hi": 0.09865747660964401 + }, + "outcome: keeper, xG 0.05-0.15": { + "value": 0.17989417989417988, + "lo": 0.127151089908768, + "hi": 0.23940707133917394 + }, + "outcome: keeper, xG 0.15-0.35": { + "value": 0.366120218579235, + "lo": 0.2799805194805195, + "hi": 0.44304310639753675 + }, + "outcome: keeper, xG 0.35-1": { + "value": 0.19572953736654805, + "lo": 0.1266923244895731, + "hi": 0.26720191952750094 + }, + "outcome: defender": { + "value": 0.35140771637122004, + "lo": 0.2700769652899646, + "hi": 0.4214712025528306 + }, + "outcome: wide, post or crossed": { + "value": 0.1470281543274244, + "lo": 0.12219765590437688, + "hi": 0.17667040017621105 + }, + "outcome: wide": { + "value": 0.09697601668404589, + "lo": 0.07801948051948052, + "hi": 0.11868183672241206 + }, + "outcome: post": { + "value": 0.021897810218978103, + "lo": 0.012512429781617226, + "hi": 0.03410477090111838 + }, + "outcome: crossed, no goal": { + "value": 0.028154327424400417, + "lo": 0.016706654449293885, + "hi": 0.04302450794596205 + }, + "aim outside the posts, share": { + "value": 0.19603753910323254, + "lo": 0.16554682159945316, + "hi": 0.22775431892280984 + }, + "wide miss beyond the post, median (m)": { + "value": 0.152, + "lo": 0.12, + "hi": 0.2152999999999997 + }, + "ball speed kept 1 m from goal (3-5 m shots)": { + "value": 0.6685931289511853, + "lo": 0.6614483111599266, + "hi": 0.6834061135371179 + }, + "kick speed, median (m/s)": { + "value": 4.91, + "lo": 4.664875, + "hi": 5.18 + }, + "kick speed, interquartile range (m/s)": { + "value": 1.4399999999999995, + "lo": 1.2800000000000002, + "hi": 1.5999999999999996 + }, + "keeper depth off the goal line, median (m)": { + "value": 0.211, + "lo": 0.1419875, + "hi": 0.273 + }, + "keeper velocity change within 0.4 s, share": { + "value": 0.6169088507265522, + "lo": 0.5152519586894586, + "hi": 0.7066734685378753 + }, + "keeper velocity change, median (s)": { + "value": 0.184, + "lo": 0.15, + "hi": 0.217 + }, + "keeper moves toward the shot line within 0.4 s, share": { + "value": 0.3965785381026439, + "lo": 0.2988581726593712, + "hi": 0.48670561903110404 + }, + "keeper moves toward the shot line, median (s)": { + "value": 0.217, + "lo": 0.2, + "hi": 0.233 + }, + "nearest defender moves toward the line within 0.4 s, share": { + "value": 0.2112676056338028, + "lo": 0.15345935803257746, + "hi": 0.267391615737204 + }, + "nearest defender moves toward the line, median (s)": { + "value": 0.234, + "lo": 0.15050000000000002, + "hi": 0.2835 + }, + "pass line offset to the nearest teammate ahead, median (m)": { + "value": 0.259, + "lo": 0.2289875, + "hi": 0.2875 + }, + "pass completion (next touch a teammate)": { + "value": 0.49732620320855614, + "lo": 0.45071686854080645, + "hi": 0.5383438703420039 + }, + "live minutes per match": { + "value": 7.098490566037737, + "lo": 6.582202044025158, + "hi": 7.651442610062893 + }, + "restarts per match": { + "value": 42.132075471698116, + "lo": 34.98018867924528, + "hi": 49.17075471698113 + } + } +} diff --git a/utama_core/analysis/realism/scorecard.py b/utama_core/analysis/realism/scorecard.py new file mode 100644 index 00000000..63c9a6d8 --- /dev/null +++ b/utama_core/analysis/realism/scorecard.py @@ -0,0 +1,282 @@ +"""The realism scorecard: our sim against real Division B matches on the parts that decide goals. + +Goal totals can agree for the wrong reasons (2026-10-14: sim keepers save twice as many good +chances while sim shooters almost never miss, and the two cancel), so each row is one +component. A row holds the real value with a 95% bootstrap interval over matches, the sim value +with its own interval, and a pass mark: the sim value inside the real interval. Rows driven by +the referee (live minutes, restarts) are shown but not marked; they differ for referee reasons, +not physics. + +Every row is computed over **attempts** (`shot_outcomes.is_attempt`: no pass to a teammate, +at most 6 m out), scored with one xG model fitted on the real attempts, except the pass rows +(`passes.py`). The real side, with the +fitted model, is stored in a reference file (`REFERENCE_PATH`), so the sim side needs no real +logs. See `docs/sim_realism.md`. +""" + +from __future__ import annotations + +import math +import warnings +from pathlib import Path +from typing import Callable, Optional + +import numpy as np + +from utama_core.analysis.realism import passes, reaction, shot_outcomes, shots, xg + +REFERENCE_PATH = Path(__file__).with_name("real_2025_2026.json") +XG_BANDS = ((0.0, 0.05), (0.05, 0.15), (0.15, 0.35), (0.35, 1.01)) +TOP_BAND = XG_BANDS[-1] +BOOTSTRAP_N = 1000 +KEPT_SPEED_RANGE_M = (3.0, 5.0) # shots this far out for the ball speed kept 1 m from goal +REFEREE_ROWS = ("live minutes per match", "restarts per match") +LONG_ATTEMPT_M = 4.0 +REACT_WINDOW_S = 0.4 # reaction rows use only shots that leave at least this long to react +# Matches whose tracker failed: 2026-07-02 TurtleRabbit vs NAMeC has the ball unseen 57% of the +# time, with a 782 s outage. +EXCLUDED_MATCHES = frozenset({"2026-07-02_18-16_GROUP_PHASE_TurtleRabbit-vs-NAMeC"}) + + +def analyse_replay(path) -> dict: + """One match's record: shots with features, goal links and outcomes, live time, restarts.""" + from utama_core.replay.replay_player import load_frames_in_range + + path = Path(path) + stem = path.name[: -len(".npz")] if path.name.endswith(".npz") else path.stem + try: + frames = load_frames_in_range(path, 0.0, math.inf) + scanned = shots.scan(frames) + links = shots.link_goals(scanned) + shot_outcomes.follow(frames, scanned["shots"]) + reaction.measure(frames, scanned["shots"]) + pass_list = passes.follow(frames, [s["t"] for s in scanned["shots"]]) + live_s = scanned["live_s"] + return { + "match": stem, + "live_s": round(live_s, 1), + "goals": scanned["goals"], + "restarts": scanned["restarts"], + "robots_live": {s: round(v / live_s, 2) if live_s else None for s, v in scanned["robots_live"].items()}, + **links, + "shots": scanned["shots"], + "passes": pass_list, + } + except Exception as e: # noqa: BLE001 - one bad log must not stop a corpus pass + return {"match": stem, "error": repr(e)} + + +def usable(records: list[dict]) -> list[dict]: + """Records without an error, not excluded, with at least `xg.MIN_LIVE_S` of live play, shots + cut to attempts.""" + out = [] + for r in records: + if "error" in r or r["live_s"] < xg.MIN_LIVE_S or r.get("match") in EXCLUDED_MATCHES: + continue + out.append({**r, "shots": [s for s in r["shots"] if shot_outcomes.is_attempt(s)]}) + return out + + +def _react(a, role, key): + """Onsets (inf where none came in time) of `role` on shots leaving at least `REACT_WINDOW_S` + to react; toward-line onsets only for a robot off the line.""" + out = [] + for s in a: + window = s.get(f"{role}_window_s") + if window is None or window < REACT_WINDOW_S: + continue + if key == "toward" and s[f"{role}_line_m"] < reaction.ON_LINE_M: + continue + v = s.get(f"{role}_{key}_s") + out.append(math.inf if v is None or v > REACT_WINDOW_S else v) + return out + + +def _arr(xs, dtype=float): + return np.array(list(xs), dtype=dtype) + + +class _Match: + """One match's attempts and passes as arrays, so a bootstrap sample is a concatenation.""" + + def __init__(self, record: dict, model: xg.Logit): + a = record["shots"] + p = record.get("passes", []) + self.live_s = record["live_s"] + self.restarts = record.get("restarts", 0) + lo, hi = KEPT_SPEED_RANGE_M + self.arrays = { + "xg": model.predict(np.array([xg.row(s) for s in a])) if a else np.empty(0), + "scored": _arr(s["scored"] for s in a), + "outcome": _arr((s["outcome"] for s in a), object), + "set_piece": _arr((s["context"] != "open_play" for s in a), bool), + "distance": _arr(s["distance_m"] for s in a), + "kick": _arr(s["peak"] for s in a), + "aim_out": _arr(abs(s.get("y_at_goal", 0.0)) > shots.HALF_GOAL for s in a), + "kept": _arr( + s["speed_1m"] / s["peak"] + for s in a + if s.get("speed_1m") and s["peak"] > shots.KICK_MPS and lo <= s["distance_m"] <= hi + ), + "depth": _arr(s["keeper_depth_m"] for s in a if s.get("keeper_depth_m") is not None), + "miss": _arr(s["miss_m"] for s in a if s.get("miss_m") is not None), + "keeper_react": _arr(_react(a, "keeper", "react")), + "keeper_toward": _arr(_react(a, "keeper", "toward")), + "defender_toward": _arr(_react(a, "defender", "toward")), + "pass_offset": _arr(x["offset_m"] for x in p), + "pass_completed": _arr(x["completed"] for x in p), + } + + +def _share(outcomes: tuple[str, ...], band: Optional[tuple[float, float]] = None) -> Callable: + def f(c): + sel = np.ones(len(c["xg"]), bool) if band is None else (c["xg"] >= band[0]) & (c["xg"] < band[1]) + return float(np.isin(c["outcome"][sel], outcomes).mean()) if sel.any() else math.nan + + return f + + +def _band_goal_rate(band): + def f(c): + sel = (c["xg"] >= band[0]) & (c["xg"] < band[1]) + return float(c["scored"][sel].mean()) if sel.any() else math.nan + + return f + + +def _median(key): + return lambda c: float(np.median(c[key])) if len(c[key]) else math.nan + + +def _mean(key): + return lambda c: float(np.mean(c[key])) if len(c[key]) else math.nan + + +def _reacted_share(key): + return lambda c: float(np.isfinite(c[key]).mean()) if len(c[key]) else math.nan + + +def _reacted_median(key): + def f(c): + v = c[key][np.isfinite(c[key])] + return float(np.median(v)) if len(v) else math.nan + + return f + + +def _attempts_per_minute(set_piece): + return lambda c: float((c["set_piece"] == set_piece).sum()) / (c["live_s"] / 60 * 2) + + +ROWS: dict[str, Callable] = { + "attempts per team per live minute": lambda c: len(c["xg"]) / (c["live_s"] / 60 * 2), + "open-play attempts per team per live minute": _attempts_per_minute(False), + "set-piece attempts per team per live minute": _attempts_per_minute(True), + "attempt distance, median (m)": _median("distance"), + f"attempts beyond {LONG_ATTEMPT_M:g} m, share": lambda c: ( + float((c["distance"] > LONG_ATTEMPT_M).mean()) if len(c["distance"]) else math.nan + ), + "real xG per attempt": lambda c: float(c["xg"].mean()), + "finishing (goals / real xG)": lambda c: float(c["scored"].sum() / c["xg"].sum()), + **{f"goal rate, xG {lo:g}-{min(hi, 1):g}": _band_goal_rate((lo, hi)) for lo, hi in XG_BANDS}, + "outcome: goal": _share(("goal",)), + "outcome: keeper": _share(("keeper",)), + **{f"outcome: keeper, xG {lo:g}-{min(hi, 1):g}": _share(("keeper",), (lo, hi)) for lo, hi in XG_BANDS}, + "outcome: defender": _share(("defender",)), + "outcome: wide, post or crossed": _share(("wide", "post", "crossed_no_goal")), + "outcome: wide": _share(("wide",)), + "outcome: post": _share(("post",)), + "outcome: crossed, no goal": _share(("crossed_no_goal",)), + "aim outside the posts, share": _mean("aim_out"), + "wide miss beyond the post, median (m)": _median("miss"), + "ball speed kept 1 m from goal (3-5 m shots)": _median("kept"), + "kick speed, median (m/s)": _median("kick"), + "kick speed, interquartile range (m/s)": lambda c: ( + float(np.subtract(*np.percentile(c["kick"], [75, 25]))) if len(c["kick"]) else math.nan + ), + "keeper depth off the goal line, median (m)": _median("depth"), + f"keeper velocity change within {REACT_WINDOW_S:g} s, share": _reacted_share("keeper_react"), + "keeper velocity change, median (s)": _reacted_median("keeper_react"), + f"keeper moves toward the shot line within {REACT_WINDOW_S:g} s, share": _reacted_share("keeper_toward"), + "keeper moves toward the shot line, median (s)": _reacted_median("keeper_toward"), + f"nearest defender moves toward the line within {REACT_WINDOW_S:g} s, share": _reacted_share("defender_toward"), + "nearest defender moves toward the line, median (s)": _reacted_median("defender_toward"), + "pass line offset to the nearest teammate ahead, median (m)": _median("pass_offset"), + "pass completion (next touch a teammate)": _mean("pass_completed"), + "live minutes per match": lambda c: c["live_s"] / 60 / c["n"], + "restarts per match": lambda c: c["restarts"] / c["n"], +} + + +def _pool(ms: list[_Match]) -> dict: + pooled = {k: np.concatenate([m.arrays[k] for m in ms]) for k in ms[0].arrays} if ms else {} + pooled.update( + live_s=sum(m.live_s for m in ms), + restarts=sum(m.restarts for m in ms), + n=len(ms), + ) + return pooled + + +def rows(records: list[dict], model: xg.Logit, n_boot: int = BOOTSTRAP_N, seed: int = 0) -> dict: + """{row: {value, lo, hi}} over `records` (already `usable`), 95% intervals by resampling matches.""" + ms = [_Match(r, model) for r in records] + point = {k: f(_pool(ms)) for k, f in ROWS.items()} + rng = np.random.default_rng(seed) + samples = {k: [] for k in ROWS} + for _ in range(n_boot): + pooled = _pool([ms[i] for i in rng.integers(0, len(ms), len(ms))]) + for k, f in ROWS.items(): + samples[k].append(f(pooled)) + out = {} + for k, v in point.items(): + with warnings.catch_warnings(): # a row with no data in a resample (an empty xG band) + warnings.simplefilter("ignore", RuntimeWarning) + lo, hi = np.nanpercentile(samples[k], [2.5, 97.5]) + out[k] = {"value": v, "lo": float(lo), "hi": float(hi)} + return out + + +def reference(real_records: list[dict], sources: list[str]) -> dict: + """The real side of the scorecard, stored so the sim side needs no real logs.""" + real = usable(real_records) + X, y, g = xg.design(real) + model = xg.Logit().fit(X, y) + return { + "sources": sources, + "matches": len(real), + "attempts": len(y), + "goals_from_attempts": int(y.sum()), + "xg_model": model.to_dict(), + "xg_check": xg.cross_validate(X, y, g), + "rows": rows(real, model), + } + + +def compare(ref: dict, sim_records: list[dict]) -> list[dict]: + """One line per row: real value and interval, sim value and interval, and whether it passes.""" + sim = usable(sim_records) + model = xg.Logit.from_dict(ref["xg_model"]) + sim_rows = rows(sim, model) + out = [] + for k, r in ref["rows"].items(): + s = sim_rows[k] + passes = None if k in REFEREE_ROWS else bool(r["lo"] <= s["value"] <= r["hi"]) + out.append( + {"row": k, "real": r, "sim": s, "pass": passes, "ratio": s["value"] / r["value"] if r["value"] else None} + ) + return out + + +def format_table(lines: list[dict]) -> str: + def fmt(x): + return f"{x['value']:.3g} [{x['lo']:.3g}, {x['hi']:.3g}]" + + width = max(len(line["row"]) for line in lines) + head = f"{'row':{width}} {'real [95%]':>24} {'sim [95%]':>24} {'sim/real':>8} pass" + out = [head, "-" * len(head)] + for line in lines: + mark = {True: "yes", False: "NO", None: "-"}[line["pass"]] + ratio = "" if line["ratio"] is None else f"x{line['ratio']:.2f}" + out.append(f"{line['row']:{width}} {fmt(line['real']):>24} {fmt(line['sim']):>24} {ratio:>8} {mark}") + return "\n".join(out) diff --git a/utama_core/analysis/realism/shot_outcomes.py b/utama_core/analysis/realism/shot_outcomes.py new file mode 100644 index 00000000..751b66c7 --- /dev/null +++ b/utama_core/analysis/realism/shot_outcomes.py @@ -0,0 +1,162 @@ +"""What happened to each shot `shots.scan` found, and which shots count as attempts. + +`follow` tracks the ball for up to `FOLLOW_S` after each shot (or until the next shot) and +records the first of: + +- `goal`: the shot is marked scored; +- `keeper` / `defender` / `teammate`: a robot comes within `TOUCH_M` of the ball; +- `post`: the ball deflects within `POST_NEAR_M` of a post (its direction turns by + `POST_TURN_DEG` or its speed drops to `POST_SLOW` of what it was `POST_LOOKBACK_S` earlier, + with no robot touching), or crosses the goal line within `POST_BAND_M` of a post (ball + radius plus post thickness: the frame it hit the post); +- `wide`: the ball crosses the goal line outside the mouth; +- `crossed_no_goal`: it crosses inside the mouth but no goal was given; +- `stopped`: the ball slows below `STOP_MPS` short of the goal; +- `open`: none of these within `FOLLOW_S`. + +Also measured at the kick: the keeper's distance from the shot line (origin to where the line +meets the goal line), its depth off the goal line and its offset across from the aim point, and +the ball speed when it reaches 1 m from the goal line, for the ball's slow-down. Where the ball +crossed the goal line (`cross_y`, shooter's view) and, for a wide shot, how far outside the post +(`miss_m`). + +**Attempts** (`is_attempt`), the default shot definition for the scorecard and the xG fit: a +shot whose next touch is not a teammate (that is a pass) and that starts at most +`MAX_ATTEMPT_M` from the goal (beyond that the sim's on-target kicks are mostly clearances from +its own box down the middle; real teams score only 6% of their goals from there). +""" + +from __future__ import annotations + +import bisect +import math + +from utama_core.analysis.realism.shots import ( + HALF_GOAL, + HALF_LEN, + keeper, + on_field, + other, + team, +) + +FOLLOW_S = 2.5 +TOUCH_M = 0.14 # robot radius 0.09 + ball radius 0.0215 + tracking slack +STOP_MPS = 0.3 +SKIP_S = 0.1 # ignore the kicker's own contact just after the kick +MAX_ATTEMPT_M = 6.0 +BALL_RADIUS_M = 0.0215 +POST_THICKNESS_M = 0.02 +POST_BAND_M = BALL_RADIUS_M + POST_THICKNESS_M +POST_NEAR_M = 0.1 +POST_TURN_DEG = 30.0 +POST_SLOW = 0.6 +POST_LOOKBACK_S = 0.05 # the real tracker smooths a bounce over a few frames +OUTCOMES = ("goal", "keeper", "defender", "teammate", "post", "wide", "crossed_no_goal", "stopped", "open") + + +def _seen(ball) -> bool: + return ball is not None and not math.isnan(ball.p.x) + + +def _deflected(v_then, v_now) -> bool: + s0, s1 = math.hypot(*v_then), math.hypot(*v_now) + if s0 < STOP_MPS: + return False + if s1 < POST_SLOW * s0: + return True + cos = (v_then[0] * v_now[0] + v_then[1] * v_now[1]) / (s0 * s1) + return math.degrees(math.acos(max(-1.0, min(1.0, cos)))) > POST_TURN_DEG + + +def is_attempt(shot: dict) -> bool: + return shot.get("outcome") != "teammate" and shot["distance_m"] <= MAX_ATTEMPT_M + + +def _goal_sign(frame, side): + friendly_goal = -1.0 if frame.my_team_is_right else 1.0 + return friendly_goal if side == "friendly" else -friendly_goal + + +def _seg_dist(px, py, ax, ay, bx, by): + dx, dy = bx - ax, by - ay + length_sq = dx * dx + dy * dy + t = 0.0 if length_sq == 0 else max(0.0, min(1.0, ((px - ax) * dx + (py - ay) * dy) / length_sq)) + return math.hypot(px - ax - t * dx, py - ay - t * dy) + + +def follow(frames, shots: list[dict]) -> None: + """Add `outcome`, `speed_1m` and the keeper measures to each shot, in place.""" + ts = [f.ts for f in frames] + times = sorted(s["t"] for s in shots) + for s in shots: + i = bisect.bisect_left(ts, s["t"]) + if i >= len(frames): + s["outcome"], s["speed_1m"] = "open", None + continue + f0 = frames[i] + side = s["side"] + g = _goal_sign(f0, side) + goal_x = g * HALF_LEN + ox, oy = s["raw_x"], s["raw_y"] + aim_y = s["y_at_goal"] * g # back to field coordinates + kid, kp = keeper(f0, other(side)) + if kp is not None and on_field(kp) and abs(kp.p.x - goal_x) < 2.0: + s["keeper_line_m"] = round(_seg_dist(kp.p.x, kp.p.y, ox, oy, goal_x, aim_y), 3) + s["keeper_depth_m"] = round(abs(goal_x - kp.p.x), 3) + s["keeper_y_off"] = round(abs(kp.p.y - aim_y), 3) + nxt = next((t for t in times if t > s["t"] + 1e-6), math.inf) + outcome, speed_1m, cross_y, prev = "open", None, None, None + recent = [] # (ts, vx, vy) over the last POST_LOOKBACK_S, for a bounce off a post + for f in frames[i:]: + dt = f.ts - s["t"] + if dt > FOLLOW_S or f.ts > nxt + 1e-3: # the next kick's own frame still counts (a bounce off a post) + break + b = f.ball + if not _seen(b): + continue + if speed_1m is None and abs(goal_x - b.p.x) <= 1.0: + speed_1m = math.hypot(b.v.x, b.v.y) + crossed = (b.p.x - goal_x) * g >= 0 + if crossed and cross_y is None: + cross_y = b.p.y + if prev is not None and b.p.x != prev[0]: # interpolate to the goal line + cross_y = prev[1] + (b.p.y - prev[1]) * (goal_x - prev[0]) / (b.p.x - prev[0]) + cross_y *= g + prev = (b.p.x, b.p.y) + if s["scored"]: + outcome = "goal" + if speed_1m is not None and cross_y is not None: + break + continue + if crossed: + off = abs(cross_y) - HALF_GOAL + outcome = "post" if abs(off) <= POST_BAND_M else ("wide" if off > 0 else "crossed_no_goal") + if outcome == "wide": + s["miss_m"] = round(off, 3) + break + if dt > SKIP_S: + hit = None + for side_ in (other(side), side): + for rid, r in team(f, side_).items(): + if on_field(r) and math.hypot(r.p.x - b.p.x, r.p.y - b.p.y) <= TOUCH_M: + hit = ("keeper" if rid == kid else "defender") if side_ != side else "teammate" + break + if hit: + break + if hit: + outcome = hit + break + near_post = min(math.hypot(b.p.x - goal_x, b.p.y - y) for y in (HALF_GOAL, -HALF_GOAL)) + if near_post <= POST_NEAR_M and recent and _deflected(recent[0][1:], (b.v.x, b.v.y)): + outcome = "post" + break + if math.hypot(b.v.x, b.v.y) < STOP_MPS: + outcome = "stopped" + break + recent.append((f.ts, b.v.x, b.v.y)) + while recent and f.ts - recent[0][0] > POST_LOOKBACK_S: + recent.pop(0) + s["outcome"] = outcome + s["speed_1m"] = None if speed_1m is None else round(speed_1m, 2) + s["cross_y"] = None if cross_y is None else round(cross_y, 3) diff --git a/utama_core/analysis/realism/shots.py b/utama_core/analysis/realism/shots.py new file mode 100644 index 00000000..aab6b9bc --- /dev/null +++ b/utama_core/analysis/realism/shots.py @@ -0,0 +1,370 @@ +"""Kicks, shots and which shot scored, from a replay: our sim's or a converted real log. + +One shot definition for both sources, made for comparing them (not `MatchStats`' shot counter, +which only fires while a >= 3.5 m/s ball is already in the attacking third and so misses real +teams' long-range goals): + +- A **kick** is the ball's velocity changing by `KICK_DV_MPS` within `KICK_WINDOW_S` to a speed + of at least `KICK_MPS`, so a kick from a dribble or a one-touch redirect counts. +- A kick is a **shot** if, within `SETTLE_S` of it, in live play, its straight line reaches the + goal mouth (plus `ON_TARGET_MARGIN_M`) and its speed could carry it there at `REACH_DECEL`. + Kicks from anywhere count (real teams score from their own half); the side is the one + attacking that goal. + +This is the raw detector. The scorecard and the xG fit drop passes and clearances on top of it +(`shot_outcomes.is_attempt`). + +Which shot scored (`link_goals`): + +- **Real logs:** the game controller halts on the autoref's POSSIBLE_GOAL, and a human accepts + it ("Accepted by GC operator") seconds to minutes later, so the score rises late. Each score + rise is matched to its GOAL game event. The event's `kick_location`, the autoref's own kick + detection, picks the detected shot whose origin is within `KICK_MATCH_M`, kicked at most + `GOAL_AFTER_SHOT_S` before the ball last entered that goal. POSSIBLE_GOAL can be entered by + hand long after the ball was reset, so that entry is searched up to `MANUAL_GOAL_S` back. If + no shot is there (the autoref's kick is a later tap-in or deflection), the side's latest shot + in that window scores, flagged `kick_mismatch`. Goals with no shot at all are `missed`, with + the autoref's kick location, so the detector itself can be checked. +- **Our sim** (no game events): the latest shot of that side at most `GOAL_AFTER_SHOT_S` before + the ball last entered that goal; our referee awards at once. + +Shot features, from the shooter's view (attacking +x) where the kick started: distance, angle, +open goal share against all opponents / the keeper alone / field players alone, keeper +position, peak speed in the first `PEAK_WINDOW_S`, robots on the field, the restart it came +from. +""" + +from __future__ import annotations + +import math +from typing import Optional + +from utama_core.analysis.chances import LIVE, open_goal +from utama_core.config.field_params import STANDARD_FIELD_DIMS +from utama_core.entities.referee.referee_command import RefereeCommand as RC +from utama_core.entities.referee.stage import Stage + +HALF_LEN = STANDARD_FIELD_DIMS.full_field_half_length +HALF_GOAL = STANDARD_FIELD_DIMS.half_goal_width +GOAL_DEPTH = STANDARD_FIELD_DIMS.goal_depth +KICK_MPS = 2.0 # ball speed after a kick +REACH_DECEL = 0.4 # m/s^2: a shot must be able to roll to the goal line at this deceleration +KICK_DV_MPS = 1.5 # velocity change within KICK_WINDOW_S that makes a kick (or redirect) +KICK_WINDOW_S = 0.2 # the real tracker smooths velocity over several frames +REFRACTORY_S = 0.25 +SETTLE_S = 0.1 # the ball's direction settles over a kick's first frames +ON_TARGET_MARGIN_M = 0.15 +PEAK_WINDOW_S = 0.3 +GOAL_AFTER_SHOT_S = 5.0 # a goal belongs to a shot this recent (rebounds and deflections included) +KICK_MATCH_M = 0.5 # detected kick origin vs the autoref's kick_location +MANUAL_GOAL_S = 180.0 # how far back a hand-entered POSSIBLE_GOAL can be from the ball crossing +SIM_AWARD_LAG_S = 1.0 +SET_PIECE_WINDOW_S = 10.0 +POSSESSION_M = 0.15 # nearest robot this close to the ball holds it (ends a restart's spell) +RESTARTS = { + RC.DIRECT_FREE_YELLOW: ("free_kick", True), + RC.DIRECT_FREE_BLUE: ("free_kick", False), + RC.PREPARE_PENALTY_YELLOW: ("penalty", True), + RC.PREPARE_PENALTY_BLUE: ("penalty", False), + RC.PREPARE_KICKOFF_YELLOW: ("kickoff", True), + RC.PREPARE_KICKOFF_BLUE: ("kickoff", False), +} +SHOOTOUT = {Stage.PENALTY_SHOOTOUT, Stage.PENALTY_SHOOTOUT_BREAK} + + +def team(frame, side): + return frame.friendly_robots if side == "friendly" else frame.enemy_robots + + +def other(side): + return "enemy" if side == "friendly" else "friendly" + + +def on_field(r): + return not (math.isnan(r.p.x) or math.isnan(r.p.y)) + + +def attacker_of(frame, goal_sign): + """The side attacking the goal at x = goal_sign * HALF_LEN.""" + friendly_goal_sign = -1.0 if frame.my_team_is_right else 1.0 # the goal friendly attacks + return "friendly" if goal_sign == friendly_goal_sign else "enemy" + + +def keeper(frame, side): + """(robot id, robot) of `side`'s keeper as the referee names it; (None, None) without a referee.""" + if frame.referee is None: + return None, None + yellow = (side == "friendly") == frame.my_team_is_yellow + kid = (frame.referee.yellow_team if yellow else frame.referee.blue_team).goalkeeper + return kid, team(frame, side).get(kid) + + +def _holder(frame): + b = frame.ball + best = None + for side in ("friendly", "enemy"): + for r in team(frame, side).values(): + if on_field(r): + d = math.hypot(r.p.x - b.p.x, r.p.y - b.p.y) + if d <= POSSESSION_M and (best is None or d < best[0]): + best = (d, side) + return None if best is None else best[1] + + +def _shot(frame, start, side, gsign, ts, restart, y_at): + defending = other(side) + goal_x = gsign * HALF_LEN + kid, kp = keeper(start, defending) + keeper_ok = kp is not None and on_field(kp) and abs(kp.p.x - goal_x) < 2.0 + field = [(r.p.x, r.p.y) for rid, r in team(start, defending).items() if rid != kid and on_field(r)] + kxy = [(kp.p.x, kp.p.y)] if keeper_ok else [] + ox, oy = start.ball.p.x, start.ball.p.y + along = abs(goal_x - ox) + set_piece = ( + restart is not None + and restart["side"] == side + and not restart["lost"] + and ts - restart["t"] <= SET_PIECE_WINDOW_S + ) + return { + "side": side, + "t": round(ts, 3), + "raw_x": round(ox, 3), + "raw_y": round(oy, 3), + "distance_m": round(math.hypot(along, oy), 3), + "angle_deg": round(math.degrees(math.atan2(abs(oy), along)), 1), + "origin_x": round(ox * gsign, 3), + "origin_y": round(oy * gsign, 3), + "open_goal": round(open_goal((ox, oy), goal_x, field + kxy), 3), + "og_keeper": round(open_goal((ox, oy), goal_x, kxy), 3), + "og_field": round(open_goal((ox, oy), goal_x, field), 3), + "keeper_in_place": keeper_ok, + "keeper_x": round(kp.p.x * gsign, 3) if keeper_ok else None, + "keeper_y": round(kp.p.y * gsign, 3) if keeper_ok else None, + "peak": math.hypot(frame.ball.v.x, frame.ball.v.y), + "n_att": sum(on_field(r) for r in team(start, side).values()), + "n_def": sum(on_field(r) for r in team(start, defending).values()), + "context": restart["kind"] if set_piece else "open_play", + "y_at_goal": round(y_at * gsign, 3), + } + + +def _goal_events(ref, seen, ts, out): + """Record each new POSSIBLE_GOAL / GOAL game event once (they are resent every message).""" + for e in ref.game_events or []: + kind = e.get("type") + if kind not in ("POSSIBLE_GOAL", "GOAL"): + continue + body = e.get("possible_goal") or e.get("goal") or {} + key = e.get("id") or (kind, e.get("created_timestamp")) + if key in seen: + continue + seen.add(key) + loc, kick = body.get("location") or {}, body.get("kick_location") + out.append( + { + "type": kind, + "t": ts, + "goal_sign": 1.0 if loc.get("x", 0.0) > 0 else -1.0, + "kick": None if kick is None else (kick.get("x", 0.0), kick.get("y", 0.0)), + "message": body.get("message"), + "origin": e.get("origin"), + } + ) + + +def scan(frames) -> dict: + """One pass over a match's frames: shots, score rises, ball entries into a goal, goal game + events, live seconds, mean robots on the field per side, and restarts that went live.""" + shots, score_events, in_goal_events, game_events, seen_events = [], [], [], [], set() + goals = {"friendly": 0, "enemy": 0} + live_s, robots_live, restarts = 0.0, {"friendly": 0.0, "enemy": 0.0}, 0 + prev_ts, prev_score = None, None + history, last_kick_t, open_kick, pending = [], -math.inf, None, None + pending_restart, restart = None, None + was_in_goal = False + for frame in frames: + ref = frame.referee + cmd = ref.referee_command if ref else None + stage = ref.stage if ref else None + ts = frame.ts + live = cmd in LIVE and stage not in SHOOTOUT + if prev_ts is not None and 0 < ts - prev_ts < 1.0 and live: + dt = ts - prev_ts + live_s += dt + for s in robots_live: + robots_live[s] += dt * sum(on_field(r) for r in team(frame, s).values()) + prev_ts = ts + if ref is not None: + if stage not in SHOOTOUT: + _goal_events(ref, seen_events, ts, game_events) + y, b = ref.yellow_team.score, ref.blue_team.score + score = {"friendly": y, "enemy": b} if frame.my_team_is_yellow else {"friendly": b, "enemy": y} + if prev_score is not None and stage not in SHOOTOUT: + for s in goals: + for _ in range(max(0, score[s] - prev_score[s])): + goals[s] += 1 + score_events.append((ts, s, frame.my_team_is_right)) + prev_score = score + if cmd in RESTARTS: + kind, yellow = RESTARTS[cmd] + pending_restart = (kind, "friendly" if yellow == frame.my_team_is_yellow else "enemy") + elif cmd == RC.NORMAL_START and pending_restart is not None: + restart = {"kind": pending_restart[0], "side": pending_restart[1], "t": ts, "lost": False} + pending_restart = None + restarts += 1 + ball = frame.ball + if ball is None: + continue + if restart is not None and not restart["lost"]: + h = _holder(frame) + if h is not None and h != restart["side"]: + restart["lost"] = True + in_goal = HALF_LEN < abs(ball.p.x) < HALF_LEN + GOAL_DEPTH + 0.1 and abs(ball.p.y) < HALF_GOAL + 0.05 + if in_goal and not was_in_goal and live: + in_goal_events.append((ts, attacker_of(frame, 1.0 if ball.p.x > 0 else -1.0))) + was_in_goal = in_goal + speed = math.hypot(ball.v.x, ball.v.y) + if open_kick is not None and ts - open_kick["t"] <= PEAK_WINDOW_S: + open_kick["peak"] = max(open_kick["peak"], speed) + history.append(frame) + while history and ts - history[0].ts > KICK_WINDOW_S: + history.pop(0) + before = history[0] + dv = math.hypot(ball.v.x - before.ball.v.x, ball.v.y - before.ball.v.y) + if speed >= KICK_MPS and dv >= KICK_DV_MPS and ts - last_kick_t >= REFRACTORY_S: + last_kick_t = ts + pending = {"t": ts, "before": before} + if pending is not None and ts - pending["t"] <= SETTLE_S and speed >= KICK_MPS and ball.v.x != 0: + gsign = 1.0 if ball.v.x > 0 else -1.0 + t_line = (gsign * HALF_LEN - ball.p.x) / ball.v.x + y_at = ball.p.y + ball.v.y * t_line + reaches = speed * speed / (2 * REACH_DECEL) >= speed * t_line + # t_line <= 0: the ball is already past that goal line, bouncing inside the goal after + # a goal (real logs: play runs on until a human confirms it), not a shot + if live and t_line > 0 and reaches and abs(y_at) <= HALF_GOAL + ON_TARGET_MARGIN_M: + start = pending["before"] + shots.append(_shot(frame, start, attacker_of(frame, gsign), gsign, pending["t"], restart, y_at)) + open_kick, pending = shots[-1], None + for s in shots: + s["scored"] = False + s["peak"] = round(s["peak"], 2) + return { + "shots": shots, + "score_events": score_events, + "in_goal_events": in_goal_events, + "game_events": game_events, + "goals": goals, + "live_s": live_s, + "robots_live": robots_live, + "restarts": restarts, + } + + +def _link_real(t_score, side, my_right, accepted, used, game_events, in_goal_events, shots): + """(shot or None, missed record or None, whether a ball entry was found); None for the + first two and None for the third when no GOAL event matches the score rise.""" + goal_sign = 1.0 if (side == "friendly") != my_right else -1.0 # the goal `side` attacks + ev = next( + ( + i + for i, e in enumerate(accepted) + if i not in used and e["goal_sign"] == goal_sign and abs(e["t"] - t_score) <= 1.0 + ), + None, + ) + if ev is None: + return None, None, None + used.add(ev) + e = accepted[ev] + possible = [p for p in game_events if p["type"] == "POSSIBLE_GOAL" and p["kick"] == e["kick"]] + t_event = possible[0]["t"] if possible else e["t"] + # The ball's last entry into that goal before the event: a goal the referee entered by + # hand (origin "UI") comes after the ball was already reset. + entries = [g[0] for g in in_goal_events if g[1] == side and t_event - MANUAL_GOAL_S <= g[0] <= t_event + 0.5] + t_cross = entries[-1] if entries else t_event + cands = [s for s in shots if s["side"] == side and not s["scored"] and 0 <= t_cross - s["t"] <= GOAL_AFTER_SHOT_S] + near = cands + if e["kick"] is not None: + kx, ky = e["kick"] + near = [s for s in cands if math.hypot(s["raw_x"] - kx, s["raw_y"] - ky) <= KICK_MATCH_M] + shot = None + if near: + shot = near[-1] + elif cands: # the autoref's kick is a later touch (tap-in, deflection) we did not count as a shot + shot = cands[-1] + shot["kick_mismatch"] = True + missed = None + if shot is None: + missed = { + "side": side, + "t_cross": round(t_cross, 2), + "kick": e["kick"], + "entry_found": bool(entries), + "manual": "UI" in (possible[0]["origin"] or []) if possible else None, + } + return shot, missed, bool(entries) + + +def _link_sim(t_score, side, used, in_goal_events, shots): + entry = next( + ( + g + for g in reversed(in_goal_events) + if g[1] == side and t_score - SIM_AWARD_LAG_S <= g[0] <= t_score + 0.5 and g not in used + ), + None, + ) + if entry is None: + return None + used.add(entry) + return next( + ( + s + for s in reversed(shots) + if s["side"] == side and not s["scored"] and 0 <= entry[0] - s["t"] <= GOAL_AFTER_SHOT_S + ), + None, + ) + + +def link_goals(scanned: dict) -> dict: + """Mark each scored shot in `scanned["shots"]` (in place) and return how the goals linked: + `link_source`, `unshot_goals` per side, `missed` (real goals with no detected shot), + `link_stats` and the GOAL events' confirmation lags.""" + shots, game_events, in_goal_events = scanned["shots"], scanned["game_events"], scanned["in_goal_events"] + unlinked = {"friendly": 0, "enemy": 0} + missed: list[dict] = [] + accepted = [e for e in game_events if e["type"] == "GOAL"] + used: set = set() + link_stats = {"entry_found": 0, "no_entry": 0} + for t_score, side, my_right in scanned["score_events"]: + shot: Optional[dict] + if accepted: + shot, miss, entry_found = _link_real( + t_score, side, my_right, accepted, used, game_events, in_goal_events, shots + ) + if entry_found is not None: + link_stats["entry_found" if entry_found else "no_entry"] += 1 + if miss is not None: + missed.append(miss) + else: + shot = _link_sim(t_score, side, used, in_goal_events, shots) + if shot is not None: + shot["scored"] = True + else: + unlinked[side] += 1 + lags = [ + round(g["t"] - p["t"], 1) + for g in accepted + for p in game_events + if p["type"] == "POSSIBLE_GOAL" and p["kick"] == g["kick"] and p["kick"] is not None + ] + return { + "link_source": "game_events" if accepted else "ball_in_goal", + "unshot_goals": unlinked, + "missed": missed, + "link_stats": link_stats, + "confirm_lags": lags, + "goal_messages": [g["message"] for g in accepted], + } diff --git a/utama_core/analysis/realism/xg.py b/utama_core/analysis/realism/xg.py new file mode 100644 index 00000000..638627ba --- /dev/null +++ b/utama_core/analysis/realism/xg.py @@ -0,0 +1,143 @@ +"""Expected goals (xG) fitted on real shots, and the split of goals into shots × shot quality × +finishing. + +- **Model:** logistic regression (numpy IRLS, small L2 on standardised features) on the + `FEATURES` of each attempt, from the shooter's view. +- **Check** (`cross_validate`): k-fold cross-validation grouped by match, so no match is in both + train and test; log loss and Brier against a constant rate, AUC, calibration by decile. +- **Decomposition** (`per_match`, `factors`), per team per 10 live minutes: goals from shots = + shots × mean real xG per shot × finishing (goals from shots ÷ summed xG). A factor's sim ÷ + real ratio says where a goal gap comes from. Goals with no linked shot are counted apart. +""" + +from __future__ import annotations + +import math + +import numpy as np + +SET_PIECES = ("free_kick", "kickoff", "penalty") +FEATURES = ("log_dist", "angle", "open_goal", "og_keeper", "no_keeper", "peak", "free_kick", "kickoff", "penalty") +L2 = 1.0 +MIN_LIVE_S = 60.0 # matches with less live play are dropped; the 10-goal-rule blowouts are short, keep them + + +def row(s: dict) -> list[float]: + return [ + math.log(max(s["distance_m"], 0.2)), + math.radians(s["angle_deg"]), + s["open_goal"], + s["og_keeper"], + 0.0 if s["keeper_in_place"] else 1.0, + min(s["peak"], 8.0), + float(s["context"] == "free_kick"), + float(s["context"] == "kickoff"), + float(s["context"] == "penalty"), + ] + + +def design(matches: list[dict]): + """(X, y, match index) over every shot of `matches`.""" + X, y, g = [], [], [] + for i, m in enumerate(matches): + for s in m["shots"]: + X.append(row(s)) + y.append(float(s["scored"])) + g.append(i) + return np.array(X).reshape(-1, len(FEATURES)), np.array(y), np.array(g) + + +class Logit: + def fit(self, X, y): + self.mu, self.sd = X.mean(0), X.std(0) + 1e-9 + Z = np.c_[np.ones(len(X)), (X - self.mu) / self.sd] + w = np.zeros(Z.shape[1]) + reg = np.full(Z.shape[1], L2) + reg[0] = 0.0 + for _ in range(100): + p = 1 / (1 + np.exp(-Z @ w)) + grad = Z.T @ (p - y) + reg * w + H = (Z * (p * (1 - p))[:, None]).T @ Z + np.diag(reg) + step = np.linalg.solve(H, grad) + w -= step + if np.abs(step).max() < 1e-8: + break + self.w = w + return self + + def predict(self, X): + Z = np.c_[np.ones(len(X)), (np.asarray(X) - self.mu) / self.sd] + return 1 / (1 + np.exp(-Z @ self.w)) + + def to_dict(self) -> dict: + return {"features": list(FEATURES), "mu": self.mu.tolist(), "sd": self.sd.tolist(), "w": self.w.tolist()} + + @classmethod + def from_dict(cls, d: dict) -> "Logit": + if tuple(d["features"]) != FEATURES: + raise ValueError(f"xG model features {d['features']} differ from {FEATURES}") + m = cls() + m.mu, m.sd, m.w = np.array(d["mu"]), np.array(d["sd"]), np.array(d["w"]) + return m + + +def auc(p, y) -> float: + order = np.argsort(p) + ranks = np.empty(len(p)) + ranks[order] = np.arange(1, len(p) + 1) + pos = y == 1 + n1, n0 = pos.sum(), (~pos).sum() + return float((ranks[pos].sum() - n1 * (n1 + 1) / 2) / (n1 * n0)) + + +def logloss(p, y) -> float: + p = np.clip(p, 1e-6, 1 - 1e-6) + return float(-np.mean(y * np.log(p) + (1 - y) * np.log(1 - p))) + + +def cross_validate(X, y, g, k: int = 5, seed: int = 0) -> dict: + """Held-out predictions by match fold: log loss, Brier and AUC, each against a constant rate, + and calibration by decile as (mean xG, observed rate, n).""" + groups = np.unique(g) + np.random.default_rng(seed).shuffle(groups) + p, base = np.empty(len(y)), np.empty(len(y)) + for f in np.array_split(groups, k): + test = np.isin(g, f) + p[test] = Logit().fit(X[~test], y[~test]).predict(X[test]) + base[test] = y[~test].mean() + edges = np.quantile(p, np.linspace(0, 1, 11)) + calibration = [] + for lo, hi in zip(edges[:-1], edges[1:]): + m = (p >= lo) & (p <= hi) + calibration.append((float(p[m].mean()), float(y[m].mean()), int(m.sum()))) + return { + "shots": len(y), + "scored": int(y.sum()), + "log_loss": logloss(p, y), + "log_loss_constant": logloss(base, y), + "brier": float(np.mean((p - y) ** 2)), + "brier_constant": float(np.mean((base - y) ** 2)), + "auc": auc(p, y), + "calibration": calibration, + } + + +def per_match(m: dict, model: Logit, kind: str = "all") -> np.ndarray: + """Per team per 10 live minutes for one match: shots, summed xG, goals from shots, goals with + no linked shot (counted only for `kind="all"`). `kind`: all, open_play or set_piece.""" + shots = [s for s in m["shots"] if kind == "all" or (s["context"] in SET_PIECES) == (kind == "set_piece")] + xg = model.predict(np.array([row(s) for s in shots])).sum() if shots else 0.0 + scale = 600.0 / m["live_s"] / 2 + unlinked = sum(m["unshot_goals"].values()) if kind == "all" else 0 + return np.array([len(shots), xg, sum(s["scored"] for s in shots), unlinked]) * scale + + +def factors(rows: np.ndarray) -> dict: + shots, xg, goals, unlinked = rows.mean(0) + return { + "shots": shots, + "xg_per_shot": xg / shots if shots else float("nan"), + "finishing": goals / xg if xg else float("nan"), + "goals_from_shots": goals, + "unlinked_goals": unlinked, + } diff --git a/utama_core/analysis/restart_picture.py b/utama_core/analysis/restart_picture.py new file mode 100644 index 00000000..178230c4 --- /dev/null +++ b/utama_core/analysis/restart_picture.py @@ -0,0 +1,581 @@ +"""Picture of where the referee override sends every friendly robot for one restart (`tools/restart_picture.py`). + +For a replay state whose referee command is a restart (STOP, timeouts, kick-offs, penalties, free kicks, +ball placement), this runs `RefereeOverride.tick` exactly as the strategy does and records the target +each robot is commanded to. The `*Step` classes only produce velocity commands through `move` and +`turn_on_spot`, so `_recording_steps` swaps those two names in `custom_referee.actions` for recorders for +the duration of the call (and restores them): the commanded target is what is captured, and no engine +or planner code changes. A detour waypoint is captured as the target, as commanded. + +The picture: the full pitch, the ball with its keep-out circle, the placement or free-kick spot, each of +our robots at its real radius with its id, an arrow to its commanded target and the role the step gives +it (kicker, placer, keeper), and the other team faded. A robot whose target is inside the keep-out circle, +inside the opponent defence area (with its keep distance), or inside our own defence area beyond the one +allowed defender is flagged in red. Read-only: nothing is sent to a robot or a referee. + +Coordinates are the replay's pitch frame (metres). `side="enemy"` gives the other team's override: its +robots are "ours", and the team flags are swapped, so the pitch is not mirrored. +""" + +from __future__ import annotations + +import dataclasses +import json +import math +from contextlib import contextmanager +from dataclasses import dataclass +from functools import lru_cache +from pathlib import Path +from typing import Iterator, Optional + +import numpy as np # type: ignore +from matplotlib.figure import Figure +from matplotlib.lines import Line2D +from matplotlib.patches import Circle + +from utama_core.analysis.findings import Finding +from utama_core.analysis.plan_snapshot import ( + AGAINST, + DPI, + FOR, + GRID, + INK, + INK_2, + INK_3, + SURFACE, + draw_pitch, + draw_robot, + game_from_scenario, + style_axes, +) +from utama_core.config.field_params import STANDARD_FIELD_DIMS +from utama_core.config.physical_constants import ROBOT_RADIUS +from utama_core.config.referee_constants import ( + BALL_KEEP_OUT_DISTANCE, + OPPONENT_DEFENSE_AREA_KEEP_DISTANCE, +) +from utama_core.custom_referee import actions as _actions +from utama_core.engine.referee_override import RefereeOverride, is_override_command +from utama_core.entities.data.referee import RefereeData +from utama_core.entities.data.vector import Vector2D +from utama_core.entities.game import Game +from utama_core.entities.game.team_info import TeamInfo +from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.entities.referee.stage import Stage +from utama_core.replay.columnar_reader import load_columnar_replay +from utama_core.replay.scenario import Scenario, scenario_from_replay + +# A red that is not in the categorical palette, reserved for the defects this picture exists to show. +ALERT = "#b3261e" +# The clearing step holds a robot whose commanded move is under 1 cm (dx^2 + dy^2 < 1e-4, actions.py), so a +# target within 1 cm inside the keep-out edge is that deadband, not a robot sent into the circle. +_DEADBAND_M = 0.01 +_KICKOFF = ("PREPARE_KICKOFF_YELLOW", "PREPARE_KICKOFF_BLUE") +_CLEARS_OWN_AREA = ("STOP", "TIMEOUT_YELLOW", "TIMEOUT_BLUE") + _KICKOFF +_FREE_KICKS = ("DIRECT_FREE_YELLOW", "DIRECT_FREE_BLUE", "INDIRECT_FREE_YELLOW", "INDIRECT_FREE_BLUE") +_PENALTIES = ("PREPARE_PENALTY_YELLOW", "PREPARE_PENALTY_BLUE") + + +@dataclass(frozen=True) +class CommandedTarget: + kind: str # "move" (a `move` call) or "turn" (a `turn_on_spot` call; the target is the robot's own spot) + x: float + y: float + + +@dataclass(frozen=True) +class RobotPlan: + robot_id: int + pos: tuple[float, float] + kind: str # "move" | "turn" | "hold" (an empty command: target = own spot) | "none" (no override command) + target: Optional[tuple[float, float]] + role: Optional[str] # "kicker", "placer", "keeper" or None + flags: tuple[str, ...] = () + measures: tuple[tuple[float, Optional[float], str], ...] = () # per flag: (value, threshold, unit) + + +@dataclass(frozen=True) +class RestartPlan: + command: RefereeCommand + side: str # "ours", "theirs" or "both" (STOP) + sim_time: Optional[float] + ball: Optional[tuple[float, float]] + designated: Optional[tuple[float, float]] + robots: tuple[RobotPlan, ...] # the planned side, by id + enemy: tuple[tuple[int, float, float], ...] + notes: tuple[str, ...] = () + + @property + def flagged(self) -> tuple[RobotPlan, ...]: + return tuple(r for r in self.robots if r.flags) + + +@contextmanager +def _recording_steps() -> Iterator[dict[int, CommandedTarget]]: + """Swap `move` and `turn_on_spot` in `custom_referee.actions` for recorders, then restore them.""" + recorded: dict[int, CommandedTarget] = {} + real_move, real_turn = _actions.move, _actions.turn_on_spot + + def move(game, motion_controller, robot_id, target_coords, target_oren, dribbling=False): + recorded[robot_id] = CommandedTarget("move", float(target_coords.x), float(target_coords.y)) + return _actions.empty_command(dribbling) + + def turn_on_spot(game, motion_controller, robot_id, target_oren, dribbling=False): + p = game.friendly_robots[robot_id].p + recorded[robot_id] = CommandedTarget("turn", float(p.x), float(p.y)) + return _actions.empty_command(dribbling) + + _actions.move, _actions.turn_on_spot = move, turn_on_spot + try: + yield recorded + finally: + _actions.move, _actions.turn_on_spot = real_move, real_turn + + +def _team_is_ours(command: RefereeCommand, my_team_is_yellow: bool) -> Optional[bool]: + """True when the command is aimed at our team, False when at theirs, None when at both (STOP).""" + name = command.name + if name.endswith("_YELLOW"): + return my_team_is_yellow + if name.endswith("_BLUE"): + return not my_team_is_yellow + return None + + +def _keeper_id(game: Game) -> Optional[int]: + ref = game.referee + if ref is None: + return None + team = ref.yellow_team if game.my_team_is_yellow else ref.blue_team + return None if team is None else team.goalkeeper + + +def _roles(game: Game, command: RefereeCommand, ours: Optional[bool]) -> dict[int, str]: + """The role each step gives a robot: the lowest-id non-keeper, or the robot nearest the ball.""" + roles: dict[int, str] = {} + keeper = _keeper_id(game) + if keeper in game.friendly_robots: + roles[keeper] = "keeper" + if not ours or not game.friendly_robots: + return roles + name = command.name + others = sorted(rid for rid in game.friendly_robots if rid != keeper) + if name in _KICKOFF or name in _PENALTIES: + if others: + roles[others[0]] = "kicker" + elif name in _FREE_KICKS or name.startswith("BALL_PLACEMENT"): + if game.ball is not None: + nearest = min(game.friendly_robots, key=lambda rid: game.friendly_robots[rid].p.distance_to(game.ball.p)) + roles[nearest] = "placer" if name.startswith("BALL_PLACEMENT") else "kicker" + return roles + + +def _flags( + game: Game, rp: RobotPlan, ball: Optional[Vector2D], own_area_rank: Optional[int] +) -> tuple[tuple[str, float, Optional[float], str], ...]: + """Each flag as (text, value, threshold, unit): the text is drawn; the numbers go to the findings.""" + if rp.target is None or rp.kind == "none": + return () + flags: list[tuple[str, float, Optional[float], str]] = [] + target = Vector2D(rp.target[0], rp.target[1]) + if rp.role in ("kicker", "placer"): + return () # the robot that must reach the ball is allowed inside the keep-out + if ball is not None: + d = (target - ball).mag() + if d < BALL_KEEP_OUT_DISTANCE - 1e-9: + # The clearing step holds a robot already within 1 cm of its edge, so a target 1 cm inside + # is that deadband, not a robot sent into the circle. + deadband = " (1 cm stop deadband)" if d > BALL_KEEP_OUT_DISTANCE - _DEADBAND_M else "" + text = f"target inside keep-out ({d:.2f} m < {BALL_KEEP_OUT_DISTANCE:.2f} m){deadband}" + flags.append((text, d, BALL_KEEP_OUT_DISTANCE, "m")) + projected = _actions._project_outside_opp_defense_area(game, target, OPPONENT_DEFENSE_AREA_KEEP_DISTANCE) + if projected != target: + push = (projected - target).mag() + text = f"target inside opponent defence area (+{OPPONENT_DEFENSE_AREA_KEEP_DISTANCE:.2f} m)" + flags.append((text, push, None, "m")) # no limit: the value is how far the target must move out + if own_area_rank is not None and own_area_rank > 0: + text = "target inside own defence area beyond the one allowed defender" + flags.append((text, float(own_area_rank + 1), 1.0, "robots in own box")) + return tuple(flags) + + +def flag_kind(flag: str) -> str: + """The findings kind of a flag text from `_flags`.""" + if flag.startswith("target inside keep-out"): + return "keep_out_short" if "deadband" in flag else "target_in_keep_out" + return "target_in_defense_area" + + +def plan_restart(game: Game, command: RefereeCommand, *, sim_time: Optional[float] = None) -> RestartPlan: + """What the referee override commands for every friendly robot, for this restart, on this `game`.""" + if not is_override_command(command): + raise ValueError(f"{command.name} is not a restart the referee override handles") + with _recording_steps() as recorded: + cmd_map = RefereeOverride().tick(game, None, command) + + ours = _team_is_ours(command, game.my_team_is_yellow) + roles = _roles(game, command, ours) + ball = None if game.ball is None else Vector2D(game.ball.p.x, game.ball.p.y) + notes: list[str] = [] + team = None + if game.referee is not None: + team = game.referee.yellow_team if game.my_team_is_yellow else game.referee.blue_team + if team is None: + notes.append("no team data in the replay at this tick: goalkeeper taken as 0") + # The referee keeps its last designated position after a placement or free kick ends; it is only + # the spot the restart means when the command is one of those. + spot_command = command.name.startswith("BALL_PLACEMENT") or command.name in _FREE_KICKS + designated = game.referee.designated_position if game.referee is not None and spot_command else None + + robots: list[RobotPlan] = [] + for rid, robot in sorted(game.friendly_robots.items()): + pos = (robot.p.x, robot.p.y) + if rid in recorded: + c = recorded[rid] + kind, target = c.kind, (c.x, c.y) + elif rid in cmd_map: + kind, target = "hold", pos + else: + kind, target = "none", None + robots.append(RobotPlan(rid, pos, kind, target, roles.get(rid))) + + # Our robots whose target is inside our own defence area, beyond the one allowed defender (ranked by + # id; STOP and kick-off clear the area, a kick-off leaves its kicker and keeper out of the count). + own_rank: dict[int, int] = {} + if command.name in _CLEARS_OWN_AREA: + keeper = _keeper_id(game) + in_own = sorted( + rp.robot_id + for rp in robots + if rp.target is not None + and rp.role not in ("kicker", "placer") + and not (command.name in _KICKOFF and rp.robot_id == keeper) + and _actions._is_in_own_defense_area(game, rp.target[0], rp.target[1]) + ) + own_rank = {rid: rank for rank, rid in enumerate(in_own)} + + def with_flags(rp: RobotPlan) -> RobotPlan: + measured = _flags(game, rp, ball, own_rank.get(rp.robot_id)) + return dataclasses.replace(rp, flags=tuple(m[0] for m in measured), measures=tuple(m[1:] for m in measured)) + + flagged = tuple(with_flags(rp) for rp in robots) + enemy = tuple((r.id, float(r.p.x), float(r.p.y)) for r in sorted(game.enemy_robots.values(), key=lambda r: r.id)) + return RestartPlan( + command=command, + side="both" if ours is None else ("ours" if ours else "theirs"), + sim_time=sim_time, + ball=None if ball is None else (ball.x, ball.y), + designated=None if designated is None else (float(designated[0]), float(designated[1])), + robots=flagged, + enemy=enemy, + notes=tuple(notes), + ) + + +# --------------------------------------------------------------------------- +# Replay state for a restart +# --------------------------------------------------------------------------- + + +@lru_cache(maxsize=1) +def _columnar(replay: str): + return load_columnar_replay(Path(replay)) + + +def restart_referee(replay: Path, scenario: Scenario) -> RefereeData: + """The replay's own team and designated-position data at the scenario's frame, as the override reads it. + + `Scenario` carries only the command, so this reads the columnar replay at the same frame. The scenario + turns the replay's coordinates half a turn when its team defends the left goal; the designated position + is turned the same way here.""" + rep = _columnar(str(Path(replay))) + tick = int(np.argmin(np.abs(rep.ts - scenario.frame_ts))) + ref = rep.frame_at(tick).referee + designated = None if ref is None else ref.designated_position + if designated is not None and not rep.is_right_at(tick): + designated = (-float(designated[0]), -float(designated[1])) + empty = TeamInfo(name="", score=0, goalkeeper=0) + blue = ref.blue_team if ref is not None and ref.blue_team is not None else empty + yellow = ref.yellow_team if ref is not None and ref.yellow_team is not None else empty + return RefereeData( + source_identifier=None, + time_sent=0.0, + time_received=0.0, + referee_command=scenario.referee_command, + referee_command_timestamp=0.0, + stage=ref.stage if ref is not None else Stage.NORMAL_FIRST_HALF, + stage_time_left=0.0, + blue_team=blue, + yellow_team=yellow, + designated_position=designated, + ) + + +def restart_game(replay: Path, scenario: Scenario, side: str = "friendly") -> Game: + """The `Game` one side's override sees for the scenario's restart.""" + return game_from_scenario(scenario, side, referee=restart_referee(replay, scenario)) + + +def restart_occurrences(replay: Path) -> list[tuple[float, RefereeCommand]]: + """Every restart the override handles, in the replay's referee sidecar (`.intentions.jsonl`), + in time order.""" + sidecar = Path(replay).with_name(f"{Path(replay).stem}.intentions.jsonl") + rows: list[tuple[float, RefereeCommand]] = [] + if sidecar.exists(): + for line in sidecar.read_text().splitlines(): + if not line.strip(): + continue + row = json.loads(line) + if row.get("event") != "referee" or row.get("sim_time") is None: + continue + try: + command = RefereeCommand[row["command"]] + except KeyError: + continue + rows.append((float(row["sim_time"]), command)) + return [(t, c) for t, c in sorted(rows, key=lambda r: r[0]) if is_override_command(c)] + + +def restart_commands(replay: Path) -> list[tuple[float, RefereeCommand]]: + """The first occurrence of each distinct restart command, in time order.""" + seen: set[RefereeCommand] = set() + first: list[tuple[float, RefereeCommand]] = [] + for t, command in restart_occurrences(replay): + if command not in seen: + seen.add(command) + first.append((t, command)) + return first + + +def plan_findings(plan: RestartPlan, replay: Path, side: str, t: float) -> list[Finding]: + """One finding per flag on each flagged robot's target: a robot can carry two (keep-out and box).""" + out: list[Finding] = [] + for rp in plan.flagged: + role = f" ({rp.role})" if rp.role else "" + for flag, (value, threshold, unit) in zip(rp.flags, rp.measures): + target = rp.target if rp.target is not None else (float("nan"), float("nan")) + out.append( + Finding( + tool="restart_picture", + kind=flag_kind(flag), + replay=str(replay), + side=side, + t0=t, + t1=t, + robots=(rp.robot_id,), + value=value, + threshold=threshold, + unit=unit, + message=( + f"R{rp.robot_id}{role} is commanded to ({target[0]:+.2f}, {target[1]:+.2f}) for " + f"{plan.command.name} at t={t:g} s: {flag}." + ), + ) + ) + return out + + +def restart_findings(replay: Path, side: str = "friendly") -> list[Finding]: + """Findings for every restart in the replay, each occurrence (not only the first of each command), without + drawing. A restart whose state cannot be rebuilt from the replay is skipped.""" + replay = Path(replay) + out: list[Finding] = [] + for t, command in restart_occurrences(replay): + try: + scenario = scenario_from_replay(replay, t, showing=command) + except ValueError: + continue + game = restart_game(replay, scenario, side) + out.extend(plan_findings(plan_restart(game, command, sim_time=scenario.sim_time), replay, side, t)) + return out + + +# --------------------------------------------------------------------------- +# Drawing +# --------------------------------------------------------------------------- + + +def _dist(a, b) -> float: + return math.hypot(a[0] - b[0], a[1] - b[1]) + + +def _draw_plan(ax, plan: RestartPlan, *, compact: bool) -> None: + field_dims = STANDARD_FIELD_DIMS + draw_pitch(ax, field_dims) + half_l, half_w = field_dims.full_field_half_length, field_dims.full_field_half_width + ax.set_xlim(-half_l - 0.2, half_l + 0.2) + ax.set_ylim(-half_w - 0.2, half_w + 0.2) + + if plan.ball is not None: + ax.add_patch( + Circle( + plan.ball, + BALL_KEEP_OUT_DISTANCE, + fill=False, + edgecolor=INK_2, + linewidth=0.8, + linestyle="--", + zorder=2, + ) + ) + if plan.designated is not None: + ax.plot( + [plan.ball[0], plan.designated[0]], + [plan.ball[1], plan.designated[1]], + color=INK_3, + linewidth=0.7, + linestyle=":", + zorder=3, + ) + ax.plot(plan.designated[0], plan.designated[1], "x", color=INK, markersize=6, markeredgewidth=1.3, zorder=8) + ax.plot(plan.ball[0], plan.ball[1], "o", color=INK, markersize=3.5 if compact else 4.5, zorder=6) + + for rid, x, y in plan.enemy: + draw_robot(ax, x, y, rid, AGAINST, alpha=0.3, zorder=4) + + for rp in plan.robots: + colour = ALERT if rp.flags else INK_2 + if rp.target is not None and rp.kind in ("move", "turn") and _dist(rp.pos, rp.target) > 0.02: + _arrow(ax, rp.pos, rp.target, colour) + ax.plot(rp.target[0], rp.target[1], "x", color=colour, markersize=5, markeredgewidth=1.2, zorder=7) + draw_robot( + ax, + rp.pos[0], + rp.pos[1], + rp.robot_id, + FOR, + edge=ALERT if rp.flags else None, + ) + if rp.role: + ax.text( + rp.pos[0], + rp.pos[1] - ROBOT_RADIUS - 0.05, + rp.role, + ha="center", + va="top", + fontsize=5.5 if compact else 6.5, + color=INK_2, + fontweight="bold", + zorder=12, + ) + + +def _arrow(ax, start, end, colour) -> None: + """An arrow from the edge of the robot at `start` to `end` (a target).""" + d = np.array(end, dtype=float) - np.array(start, dtype=float) + u = d / float(np.hypot(*d)) + begin = np.array(start, dtype=float) + u * ROBOT_RADIUS + ax.annotate( + "", + xy=(end[0], end[1]), + xytext=(begin[0], begin[1]), + arrowprops=dict(arrowstyle="-|>", color=colour, lw=0.9, shrinkA=0, shrinkB=0, mutation_scale=8), + zorder=4, + ) + + +def _text_lines(plan: RestartPlan) -> list[str]: + side = { + "ours": "ours (a restart for the planned side's team)", + "theirs": "theirs (a restart for the other team, against the planned side)", + }.get(plan.side, "both teams") + when = f", t = {plan.sim_time:g} s" if plan.sim_time is not None else "" + lines = [f"command: {plan.command.name}{when}", f"for: {side}"] + if plan.designated is not None: + lines.append(f"designated spot: ({plan.designated[0]:.2f}, {plan.designated[1]:.2f})") + for rp in plan.robots: + role = rp.role or "" + if rp.target is None: + lines.append(f" {rp.robot_id:>2} {role:<8} no override command") + continue + d = "" if plan.ball is None else f" {_dist(rp.target, plan.ball):.2f} m from ball" + lines.append(f" {rp.robot_id:>2} {role:<8} {rp.kind:<4} -> ({rp.target[0]:+.2f}, {rp.target[1]:+.2f}){d}") + for flag in rp.flags: + lines.append(f" FLAG: {flag}") + if not plan.flagged: + lines.append("no robot target flagged") + lines.extend(plan.notes) + return lines + + +def _legend_handles() -> list[Line2D]: + return [ + Line2D([], [], marker="o", linestyle="", markerfacecolor=FOR, markeredgecolor="none", label="our robot"), + Line2D( + [], + [], + marker="o", + linestyle="", + markerfacecolor=AGAINST, + alpha=0.4, + markeredgecolor="none", + label="other team", + ), + Line2D([], [], marker="x", linestyle="", color=INK_2, label="commanded target"), + Line2D([], [], marker="x", linestyle="", color=ALERT, label="flagged target"), + Line2D([], [], color=INK_2, linestyle="--", linewidth=0.8, label="ball keep-out 0.8 m"), + Line2D([], [], marker="x", linestyle="", color=INK, label="designated spot"), + ] + + +def render_restart(plan: RestartPlan, out_path: Path, *, title: Optional[str] = None) -> Path: + """One restart as a single picture: the full pitch, the text box beneath it and a legend.""" + fig = Figure(figsize=(6.6, 7.4), dpi=DPI, facecolor=SURFACE) + ax = style_axes(fig.add_axes([0.1, 0.42, 0.86, 0.52])) + _draw_plan(ax, plan, compact=False) + ax.set_xlabel("x (m)", fontsize=8, color=INK_2) + ax.set_ylabel("y (m)", fontsize=8, color=INK_2) + if title: + ax.set_title(title, fontsize=9, color=INK, loc="left") + fig.text( + 0.1, + 0.37, + "\n".join(_text_lines(plan)), + ha="left", + va="top", + fontsize=6.8, + color=INK, + family="monospace", + bbox=dict(boxstyle="round,pad=0.4", facecolor=SURFACE, edgecolor=GRID, linewidth=0.6), + ) + fig.legend( + handles=_legend_handles(), + loc="lower center", + bbox_to_anchor=(0.5, 0.0), + ncol=3, + fontsize=6.5, + frameon=False, + labelcolor=INK_2, + ) + out_path = Path(out_path) + out_path.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(out_path, dpi=DPI, facecolor=SURFACE) + return out_path + + +def render_restart_grid( + plans: list[RestartPlan], out_path: Path, *, ncols: int = 4, title: Optional[str] = None +) -> Path: + """Small multiples: one compact pitch per restart, titled with its command, side, time and flag count.""" + ncols = max(1, min(ncols, len(plans))) + nrows = math.ceil(len(plans) / ncols) + fig = Figure(figsize=(4.3 * ncols, 3.4 * nrows + 0.5), dpi=DPI, facecolor=SURFACE) + for i, plan in enumerate(plans): + ax = style_axes(fig.add_subplot(nrows, ncols, i + 1)) + _draw_plan(ax, plan, compact=True) + ax.tick_params(labelsize=5) + when = f" t={plan.sim_time:.1f}s" if plan.sim_time is not None else "" + n = len(plan.flagged) + ax.set_title( + f"{plan.command.name}\n{plan.side}{when} flags: {n}", + fontsize=6.5, + color=ALERT if n else INK, + loc="left", + ) + if title: + fig.suptitle(title, fontsize=9, color=INK, x=0.02, ha="left") + fig.subplots_adjust(left=0.03, right=0.98, top=0.86 if title else 0.9, bottom=0.03, wspace=0.12, hspace=0.45) + out_path = Path(out_path) + out_path.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(out_path, dpi=DPI, facecolor=SURFACE) + return out_path diff --git a/utama_core/analysis/strategy_table.py b/utama_core/analysis/strategy_table.py new file mode 100644 index 00000000..9ee2ded9 --- /dev/null +++ b/utama_core/analysis/strategy_table.py @@ -0,0 +1,102 @@ +"""Per-strategy totals over a round-robin's `summary.json`-shaped results. + +Lives in `utama_core` so the analysis code can rebuild a `summary.json`'s `strategies` +table without importing the `tools/` scripts (which are not part of the installed package). +`tools/evaluation/round_robin.py` re-exports it. +""" + +from __future__ import annotations + +from typing import Optional + +from utama_core.analysis import chances + + +def _short_name(config_name: str) -> str: + """A config's name in replay file names (mirrors `tools/evaluation/match._short_name`).""" + return config_name.removeprefix("build_").removesuffix("_kernel_strategy") + + +def strategy_table( + results: list[dict], + real_losses_by_match: Optional[dict[str, dict[str, int]]] = None, + chances_by_match: Optional[dict[str, dict]] = None, +) -> dict[str, dict]: + """Per-strategy totals over `summary.json`-shaped `results`. Match stats are from + config_a's side, so config_b reads the `enemy_*` counterparts. Real ball losses are only + measured for config_a (the side with an intentions log), so they are totalled over the + matches a strategy played as config_a (`matches_as_a`), in total and by kind + (`turnover_breakdown.TURNOVER_KINDS` and `RESTART_KINDS`). `stalled` counts matches that + recorded a stall event or tripped the possession backstop: their result is still in + W-D-L, flagged rather than dropped, since a stall can be the strategy's own fault. + `chances` (`analysis.chances.rates`) is measured for both sides, over the matches in + `chances_by_match`; `pass_progress_m` is the mean metres gained toward goal per + completed pass, `forward_pass_share` the share gaining at least 1 m.""" + table: dict[str, dict] = {} + chance_totals: dict[str, dict[str, float]] = {} + progress: dict[str, list[float]] = {} + for r in results: + stats = r.get("stats") or {} + fouls = stats.get("fouls_by_side") or {} + tag = f"{_short_name(r['config_a'])}_vs_{_short_name(r['config_b'])}" + for name, own, is_a in ((r["config_a"], "friendly", True), (r["config_b"], "enemy", False)): + row = table.setdefault( + name, + { + "matches": 0, + "wins": 0, + "draws": 0, + "losses": 0, + "goals_for": 0, + "goals_against": 0, + "shots": 0, + "completed_passes": 0, + "attacking_third_entries": 0, + "fouls": 0, + "stalled": 0, + "matches_as_a": 0, + "real_losses_as_a": 0, + "real_loss_kinds_as_a": {}, + "robot_overlap_ticks": 0, + "robot_overlap_incidents": 0, + "teammate_overlap_ticks": 0, + "teammate_overlap_incidents": 0, + "near_miss_ticks": 0, + "worst_clearance_m": None, + }, + ) + prefix = "" if is_a else "enemy_" + gf, ga = (r["score_a"], r["score_b"]) if is_a else (r["score_b"], r["score_a"]) + row["matches"] += 1 + row["wins" if gf > ga else "draws" if gf == ga else "losses"] += 1 + row["goals_for"] += gf + row["goals_against"] += ga + row["shots"] += (stats.get("shots") or {}).get(own, 0) + row["completed_passes"] += stats.get(f"{prefix}completed_passes", 0) + row["attacking_third_entries"] += stats.get(f"{prefix}attacking_third_entries", 0) + row["fouls"] += sum((fouls.get(own) or {}).values()) + row["stalled"] += bool(stats.get("stall_events") or r.get("possession_backstop")) + row["robot_overlap_ticks"] += stats.get("robot_overlap_ticks", 0) + row["robot_overlap_incidents"] += stats.get("robot_overlap_incidents", 0) + row["teammate_overlap_ticks"] += (stats.get("teammate_overlap_ticks") or {}).get(own, 0) + row["teammate_overlap_incidents"] += (stats.get("teammate_overlap_incidents") or {}).get(own, 0) + row["near_miss_ticks"] += stats.get("near_miss_ticks", 0) + wc = stats.get("worst_clearance_m") + if wc is not None: + if row["worst_clearance_m"] is None or wc < row["worst_clearance_m"]: + row["worst_clearance_m"] = round(wc, 3) + progress.setdefault(name, []).extend(stats.get(f"{prefix}pass_progress_m") or []) + if chances_by_match and tag in chances_by_match: + chances.add(chance_totals.setdefault(name, {}), chances.side_totals(chances_by_match[tag], own)) + if is_a and real_losses_by_match is not None and tag in real_losses_by_match: + row["matches_as_a"] += 1 + kinds = real_losses_by_match[tag] + row["real_losses_as_a"] += sum(kinds.values()) + for kind, n in kinds.items(): + row["real_loss_kinds_as_a"][kind] = row["real_loss_kinds_as_a"].get(kind, 0) + n + for name, row in table.items(): + gains = progress.get(name, []) + row["pass_progress_m"] = round(sum(gains) / len(gains), 2) if gains else None + row["forward_pass_share"] = round(sum(g >= 1.0 for g in gains) / len(gains), 2) if gains else None + row["chances"] = chances.rates(chance_totals.get(name, {})) + return table diff --git a/utama_core/custom_referee/actions.py b/utama_core/custom_referee/actions.py index dd31f28a..67a996e4 100644 --- a/utama_core/custom_referee/actions.py +++ b/utama_core/custom_referee/actions.py @@ -24,7 +24,6 @@ ) from utama_core.custom_referee.geometry import RefereeGeometry from utama_core.entities.data.vector import Vector2D -from utama_core.entities.referee.referee_command import RefereeCommand from utama_core.global_utils.math_utils import ( detour_around_circle, distance_point_to_segment, @@ -237,12 +236,14 @@ def _in_planner_opp_defense_margin(game, point: Vector2D) -> bool: def _reachable_on_circle(game, point: Vector2D, center: Vector2D, radius: float) -> Vector2D: """`point` (on the circle round `center`), or the nearest point of that circle - the planner will drive to. + on the pitch that the planner will drive to. A free kick 1 m in front of the opponent box pushed a robot between the ball and the box straight toward the box; the planner stopped it 0.48 m from the ball, a - Defender Too Close foul every free kick.""" - if not _in_planner_opp_defense_margin(game, point): + Defender Too Close foul every free kick. A placement near a corner pushed a robot + off the pitch; clamped back on, its target was still inside the circle and it never + left (moment placement_in_corner_traps_defender).""" + if not _in_planner_opp_defense_margin(game, point) and _clamp_to_field(point, game) == point: return point base = math.atan2(point.y - center.y, point.x - center.x) for step in range(1, 37): @@ -567,6 +568,15 @@ def update(self) -> None: ball_pos_for_clamp.y - approach_dir.y * self._APPROACH_OFFSET, ) target_for_move = _clamp_to_field_or_ball(approach, game, ball_pos_for_clamp) + # Still on the designated side of the ball: go round it, not through it, to + # the approach point (a hand-made moment: the straight run passed 0.03 m + # from the ball, and the placer pushed it off the spot). Once behind the ball + # the run to the approach point is the final approach and stays straight. + robot_rel = robot.p - ball_pos_for_clamp + if robot_rel.x * approach_dir.x + robot_rel.y * approach_dir.y > 0.0: + target_for_move = detour_around_circle( + robot.p, target_for_move, ball_pos_for_clamp, ROBOT_RADIUS + BALL_RADIUS + ) self.blackboard.cmd_map[robot_id] = move( game, motion_controller, robot_id, target_for_move, oren, dribbling=True ) @@ -961,13 +971,3 @@ def update(self) -> None: self.blackboard, ball_keep_dist=BALL_KEEP_OUT_DISTANCE, ) - - -# --------------------------------------------------------------------------- -# Helper: resolve bilateral commands -# --------------------------------------------------------------------------- - - -def is_our_command(command: RefereeCommand, our_command: RefereeCommand, their_command: RefereeCommand) -> bool: - """Not used directly — bilateral resolution is done in tree.py via command sets.""" - return command == our_command diff --git a/utama_core/custom_referee/state_machine.py b/utama_core/custom_referee/state_machine.py index c54ff358..7c8bfb1d 100644 --- a/utama_core/custom_referee/state_machine.py +++ b/utama_core/custom_referee/state_machine.py @@ -57,6 +57,10 @@ # SSL rulebook §5.4: the ball is in play 10 s (Division B) after a free kick # even if nobody kicked it. A Defender Too Close foul (§8.4.3) restarts the clock. _FREE_KICK_TIMEOUT_SECONDS = 10.0 +# SSL rulebook §5.3.3 / §5.4: a free kick is taken, and the ball in play, once the ball has moved this +# far from where it was when the free kick's command began (or, for a kick-off, when it moved from +# NORMAL_START's spot). +_BALL_MOVED_M = 0.05 _PLAYING_STAGES = frozenset({Stage.NORMAL_FIRST_HALF, Stage.NORMAL_SECOND_HALF}) @@ -188,6 +192,10 @@ def _reset_state(self) -> None: self._advance4_ready_since: float = math.inf # BALL_PLACEMENT_* → next_command # First Defender Too Close foul of the current free kick; restarts its clock. self._defender_too_close_at: float = -math.inf + # Where the ball was when the current DIRECT_FREE_* command began, and the command_counter + # it began at: a kick that moves the ball from here ends the free kick (§5.4). + self._free_kick_ball_origin: Optional[tuple[float, float]] = None + self._free_kick_origin_counter: int = -1 # Cooldown: don't process a new violation within this window. self._last_transition_time: float = -math.inf @@ -253,6 +261,10 @@ def step( if self.stage == Stage.POST_GAME: return self._generate_referee_data(current_time) + if self.command in self._DIRECT_FREE_COMMANDS and self._free_kick_origin_counter != self.command_counter: + self._free_kick_origin_counter = self.command_counter + self._free_kick_ball_origin = self._ball_xy(game_frame) + if violation is not None and self._can_transition(current_time): self._apply_violation(violation, current_time) @@ -385,7 +397,12 @@ def step( elif self._auto_advance.direct_free_to_normal and self.command in self._DIRECT_FREE_COMMANDS: ready = game_frame is not None and self._free_kick_ready(self.command, game_frame) kick_clock_start = max(self.command_timestamp, self._defender_too_close_at) - if current_time - kick_clock_start >= _FREE_KICK_TIMEOUT_SECONDS: + if self._free_kick_taken(game_frame): + # The kicker has kicked: the ball is in play, as after the kick of a ready free kick + # (so KeepOutRule stops applying, and DoubleTouchRule sees DIRECT_FREE_* → NORMAL_START). + logger.info("Free kick taken (ball moved) — %s → NORMAL_START", self.command.name) + self._start_normal_play(current_time, game_frame) + elif current_time - kick_clock_start >= _FREE_KICK_TIMEOUT_SECONDS: logger.info("Free kick not taken in time — auto-advancing %s → FORCE_START", self.command.name) self.command = RefereeCommand.FORCE_START self.command_counter += 1 @@ -400,17 +417,7 @@ def step( logger.debug("Advance 3 countdown started (%s)", self.command.name) elif (current_time - self._advance3_ready_since) >= _AUTO_ADVANCE_DELAY: logger.info("Free kick ready — auto-advancing %s → NORMAL_START", self.command.name) - self.command = RefereeCommand.NORMAL_START - self.command_counter += 1 - self.command_timestamp = current_time - self.next_command = None - self.status_message = None - self._normal_start_time = current_time - self._ball_pos_at_normal_start = ( - (game_frame.ball.p.x, game_frame.ball.p.y) if game_frame.ball is not None else None - ) - self._advance3_ready_since = math.inf - self._last_transition_time = current_time + self._start_normal_play(current_time, game_frame) else: self._advance3_ready_since = math.inf @@ -643,7 +650,33 @@ def _ball_has_moved(self, game_frame: "GameFrame") -> bool: if self._ball_pos_at_normal_start is None or game_frame.ball is None: return False ox, oy = self._ball_pos_at_normal_start - return math.hypot(game_frame.ball.p.x - ox, game_frame.ball.p.y - oy) >= 0.05 + return math.hypot(game_frame.ball.p.x - ox, game_frame.ball.p.y - oy) >= _BALL_MOVED_M + + def _free_kick_taken(self, game_frame: Optional["GameFrame"]) -> bool: + """Return True if the ball has moved ≥ 0.05 m from where it was when this DIRECT_FREE_* began.""" + if self._free_kick_ball_origin is None or game_frame is None or game_frame.ball is None: + return False + ox, oy = self._free_kick_ball_origin + return math.hypot(game_frame.ball.p.x - ox, game_frame.ball.p.y - oy) >= _BALL_MOVED_M + + @staticmethod + def _ball_xy(game_frame: Optional["GameFrame"]) -> Optional[tuple[float, float]]: + if game_frame is None or game_frame.ball is None: + return None + return (game_frame.ball.p.x, game_frame.ball.p.y) + + def _start_normal_play(self, current_time: float, game_frame: "GameFrame") -> None: + """DIRECT_FREE_* → NORMAL_START (the kicker was ready, or the ball has been kicked): the ball + is in play, and NORMAL_START's own timeout measures from here.""" + self.command = RefereeCommand.NORMAL_START + self.command_counter += 1 + self.command_timestamp = current_time + self.next_command = None + self.status_message = None + self._normal_start_time = current_time + self._ball_pos_at_normal_start = self._ball_xy(game_frame) + self._advance3_ready_since = math.inf + self._last_transition_time = current_time def _ball_placement_done(self, game_frame: "GameFrame") -> bool: """Return True when the ball is within _PLACEMENT_DONE_DIST of the placement target.""" diff --git a/utama_core/dashboard/static/tournament.js b/utama_core/dashboard/static/tournament.js index 754985b8..05d22a6a 100644 --- a/utama_core/dashboard/static/tournament.js +++ b/utama_core/dashboard/static/tournament.js @@ -1,11 +1,11 @@ // Tournament view: standings + per-match results, sortable/expandable. -// Successor concern to hand-reading full_match_tournament.py log output — +// Successor concern to hand-reading round-robin log output — // reads replays//summary.json via the tournament view's Python side. // A round_robin.py summary also carries a per-strategy table, stall // incidents, restart outcomes and fouls (docs/STRATEGY_DEVELOPMENT.md // "Reading a tournament run"); those are shown when present. Each result's // `replay` is the path of its replay file, or null (added server-side). -// `full_match_tournament.py` writes `winner` as the literal string "draw" on +// `round_robin.py` writes `winner` as the literal string "draw" on // a tie (never null), and `standings` only carries wins/draws per config — // losses/GF/GA/win% are derived here from `results`, not shipped precomputed. diff --git a/utama_core/dashboard/views/replay.py b/utama_core/dashboard/views/replay.py index 349ef492..99d68fae 100644 --- a/utama_core/dashboard/views/replay.py +++ b/utama_core/dashboard/views/replay.py @@ -11,7 +11,7 @@ `FieldCanvas.draw()` on the frontend needs no replay-specific branch. If a sibling `.intentions.jsonl` sits next to the `.pkl` (written -by `full_match_tournament.py` via `StrategyRunner(match_log_path=...)`), its +by a round-robin run via `StrategyRunner(match_log_path=...)`), its sparse `IntentionEvent`/`RefereeEvent` rows are shipped to the browser as-is — `tactic_events`/`referee_events` — rather than forward-filled onto every frame here. Frame counts run into the tens of thousands while event diff --git a/utama_core/dashboard/views/tournament.py b/utama_core/dashboard/views/tournament.py index 16990b0f..70882e2b 100644 --- a/utama_core/dashboard/views/tournament.py +++ b/utama_core/dashboard/views/tournament.py @@ -52,8 +52,8 @@ def _short(config: str) -> str: def _replay_path(run_dir: Path, result: dict) -> Optional[str]: """The result's replay, relative to `REPLAY_BASE_PATH`: `round_robin.py` writes - `_vs_.npz`, `ladder.py` `_vs__.npz` (the retired - `full_match_tournament.py` wrote the same tags as `.pkl`).""" + `_vs_.npz`. Older runs (the retired `ladder.py`, `full_match_tournament.py`) wrote + side and kickoff tags, `_vs__` (as `.pkl` for the latter).""" stem = f"{_short(result['config_a'])}_vs_{_short(result['config_b'])}" names = [f"{stem}.npz", f"{stem}.pkl"] if "a_is_right" in result: diff --git a/utama_core/data_processing/refiners/filters/kalman.py b/utama_core/data_processing/refiners/filters/kalman.py index 1c91aae6..d48221cd 100644 --- a/utama_core/data_processing/refiners/filters/kalman.py +++ b/utama_core/data_processing/refiners/filters/kalman.py @@ -136,9 +136,6 @@ def _step_xy( """ - # class Robot: id: int; is_friendly: bool; has_ball: bool - # p: Vector2D; v: Vector2D; a: Vector2D; orientation: float - # x and y are filtered as two independent scalar Kalman filters (see __init__ for why # that's mathematically equivalent to the 2D matrix form here), using the same # closed-form update as _step_th below. @@ -258,7 +255,6 @@ def filter_data( and orientation, packaged as a VisionRobotData object. """ - # class VisionRobotData: id: int; x: float; y: float; orientation: float xy_tuple = (data.x, data.y) if data is not None else None x_f, y_f = self._step_xy(xy_tuple, last_frame, time_elapsed) th_f = self._step_th( @@ -341,8 +337,6 @@ def _step( returned to the filter_data function for packaging. """ - # class Ball: p: Vector3D; v: Vector3D; a: Vector3D - # Phase 0: Initialised with the 1st valid GameFrame (only on initialisation) if self.state is None: self.state = np.array((last_ball.p.x, last_ball.p.y, last_ball.p.z)) @@ -406,7 +400,6 @@ def filter_data(self, data: Optional[Ball], last_frame: Ball, time_elapsed: floa acceleration. """ - # class Ball: p: Vector3D, v: Vector3D, a: Vector3D if data is not None: new_data = (data.p.x, data.p.y, data.p.z) velocity, acceleration = data.v, data.a diff --git a/utama_core/entities/data/vector.py b/utama_core/entities/data/vector.py index 20b0de08..5fac85ab 100644 --- a/utama_core/entities/data/vector.py +++ b/utama_core/entities/data/vector.py @@ -226,19 +226,3 @@ def to_2d(self) -> Vector2D: def __repr__(self): return f"Vector3D(x={self.x}, y={self.y}, z={self.z})" - - -if __name__ == "__main__": - import numpy as np - - v2d = Vector2D(3, 4) - v2d2 = Vector2D(1, 2) - print(np.dot(v2d, v2d2)) # Should print 11.0 - print(v2d.dot(v2d2)) # Should print 11.0 - - print(v2d.angle_to(v2d2)) # Should print -2.356194490192345 - print(np.arctan2(v2d2.y - v2d.y, v2d2.x - v2d.x)) # Should print -2.356194490192345 - - d = v2d2 - v2d - print(np.linalg.norm(d)) # Should print 2.8284271247461903 - print(v2d.distance_to(v2d2)) # Should print 2.8284271247461903 diff --git a/utama_core/entities/game/game.py b/utama_core/entities/game/game.py index 44d7054d..2d359b83 100644 --- a/utama_core/entities/game/game.py +++ b/utama_core/entities/game/game.py @@ -16,8 +16,6 @@ def add_game_frame(self, game_frame: GameFrame): self._current_game_standard_frame = game_frame self.current = CurrentGameFrame(game_frame) - # def predict_/ - @property def ts(self) -> float: return self.current.ts diff --git a/utama_core/global_utils/git_utils.py b/utama_core/global_utils/git_utils.py new file mode 100644 index 00000000..9e78573e --- /dev/null +++ b/utama_core/global_utils/git_utils.py @@ -0,0 +1,10 @@ +import subprocess + + +def git_revision() -> str | None: + """Short git commit of the checkout the process runs from, or None outside a git checkout.""" + try: + result = subprocess.run(["git", "rev-parse", "--short", "HEAD"], check=True, capture_output=True, text=True) + except (OSError, subprocess.CalledProcessError): + return None + return result.stdout.strip() or None diff --git a/utama_core/motion_planning/src/fastpathplanning/planner.py b/utama_core/motion_planning/src/fastpathplanning/planner.py index 0f8e8ece..6b84e9fc 100644 --- a/utama_core/motion_planning/src/fastpathplanning/planner.py +++ b/utama_core/motion_planning/src/fastpathplanning/planner.py @@ -16,6 +16,7 @@ closest_point_on_segment, detour_around_circle, distance, + distance_between_line_segments, distance_point_to_segment, find_intersection, ) @@ -104,6 +105,10 @@ def _perp_rotation(subgoal_direction: int) -> Tuple[float, float]: } +# How far past the ball's keep-out edge a robot inside it is pushed out. +_PUSH_OUT_MARGIN = 0.05 + + def _keeps_off_ball(game: Game) -> bool: """True during STOP and the other team's restarts, when every robot of ours must stay out of the ball's keep-out circle.""" @@ -115,6 +120,20 @@ def _keeps_off_ball(game: Game) -> bool: return command.name.endswith("_YELLOW") != game.my_team_is_yellow +def _leaving(our_pos: np.ndarray, target: np.ndarray, obstacle, clearance: float) -> bool: + """True if the robot at `our_pos` is inside `obstacle`'s clearance and the straight run + to `target` never takes it closer to the obstacle than it is now.""" + here = distance_point_to_segment(our_pos, obstacle[0], obstacle[1]) + if here >= clearance: + return False + return distance_between_line_segments(our_pos, target, obstacle[0], obstacle[1]) >= here - 1e-9 + + +def _approaches(our_pos: np.ndarray, target: np.ndarray, point: np.ndarray) -> bool: + """True if the straight run from `our_pos` to `target` gets closer to `point` than the robot is now.""" + return distance_point_to_segment(point, our_pos, target) < np.linalg.norm(point - our_pos) - 1e-9 + + class FastPathPlanner: def __init__(self, env: SSLStandardEnv | None): self._env = env @@ -1567,6 +1586,21 @@ def _path_to( obstacles if not routing_exempt else [o for o in obstacles if _segment_key(o) not in routing_exempt] ) + # 3b. A robot already inside another robot's clearance (pressed against it) can plan + # no segment out: each starts in collision, the straight one away too, and the detour + # round it can run back through it. Leave out of routing each robot obstacle the robot + # is inside whose distance only grows along the straight run to the target; it counts + # again once the robot is clear. Not static obstacles (box edges, field lines): there + # the clearance itself is the margin a robot must keep, and hugging it fails the + # defense-area and corner-route tests. Moments placement_in_corner_traps_defender and + # free_kick_defender_dodges_into_ball: defenders pressed against another robot were + # routed back through it and toward the ball. + routing_obstacles = [ + o + for o in routing_obstacles + if _segment_key(o) in self._obstacle_cache_static_keys or not _leaving(our_pos, safe_target, o, clearance) + ] + # 4. Plan geometric path. Recompute-skip: if this robot was asked for # essentially the same target last call and every segment of the # detour trajectory that call computed is still clear of every @@ -1685,11 +1719,23 @@ def _path_to( # defender sidestepping teammates queued at them reached 0.38 m from # the ball and KeepOutRule voided the penalty. A robot already inside is # left to the referee step's own straight push-out: a detour waypoint - # swings it round the ball and can clip it. + # swings it round the ball and can clip it. It is not left free to + # close in, though: a carrot whose first run gets closer to the ball + # (round a robot it is pressed against) is replaced by the ray out from + # the ball through the robot, which only gets farther (moment + # placement_corner_robot_drives_onto_ball; test_fpp_inside_ball_keep_out). + # The ray runs past the edge: a point on the edge itself parked a robot + # 5 mm inside, never encroaching-free, the kick-off step's chord round the + # circle replaced by it every tick (a 15 s kick-off stall, tournament_20261017_151052). ball = Vector2D(game.ball.p.x, game.ball.p.y) if game.ball is not None else None - if ball is not None and _keeps_off_ball(game) and (Vector2D(*our_pos) - ball).mag() >= BALL_KEEP_OUT_DISTANCE: - waypoint = detour_around_circle(Vector2D(*our_pos), Vector2D(*new_target), ball, BALL_KEEP_OUT_DISTANCE) - new_target = np.array([waypoint.x, waypoint.y]) + if ball is not None and _keeps_off_ball(game): + ball_np = np.array([ball.x, ball.y]) + if (Vector2D(*our_pos) - ball).mag() >= BALL_KEEP_OUT_DISTANCE: + waypoint = detour_around_circle(Vector2D(*our_pos), Vector2D(*new_target), ball, BALL_KEEP_OUT_DISTANCE) + new_target = np.array([waypoint.x, waypoint.y]) + elif _approaches(our_pos, new_target, ball_np) and np.linalg.norm(our_pos - ball_np) > 1e-9: + away = (our_pos - ball_np) / np.linalg.norm(our_pos - ball_np) + new_target = ball_np + away * (BALL_KEEP_OUT_DISTANCE + _PUSH_OUT_MARGIN) if self._should_draw: self._env.draw_line((our_pos, new_target), color="Blue") diff --git a/utama_core/motion_planning/src/planning/obstacles.py b/utama_core/motion_planning/src/planning/obstacles.py index 2988ebf5..00106f48 100644 --- a/utama_core/motion_planning/src/planning/obstacles.py +++ b/utama_core/motion_planning/src/planning/obstacles.py @@ -34,11 +34,6 @@ def from_polygon(cls, polygon: np.ndarray) -> "ObstacleRegion": return cls(polygon=polygon, rect=AxisAlignedRectangle(min_x, max_x, min_y, max_y)) -def to_polygons(obstacles: Iterable[ObstacleRegion]) -> List[np.ndarray]: - """Return the list of polygon arrays for the given obstacles.""" - return [obs.polygon for obs in obstacles] - - def to_rectangles(obstacles: Iterable[ObstacleRegion]) -> List[AxisAlignedRectangle]: """Return the list of bounding rectangles for the given obstacles.""" return [obs.rect for obs in obstacles] diff --git a/utama_core/replay/columnar_reader.py b/utama_core/replay/columnar_reader.py index 0ad4d03f..a4321e07 100644 --- a/utama_core/replay/columnar_reader.py +++ b/utama_core/replay/columnar_reader.py @@ -14,6 +14,7 @@ import bisect import dataclasses +import json import pickle from dataclasses import dataclass from functools import cached_property @@ -180,6 +181,12 @@ def _robots_at(ids, is_friendly, p_row, v_row, a_row, orientation_row, has_ball_ return robots +def load_sim_noise(path: Union[str, Path]) -> Optional[dict]: + """The `SimNoise` settings and per-robot draw a replay was played with, or None if it had none.""" + with np.load(path, allow_pickle=False) as data: + return json.loads(str(data["sim_noise"])) if "sim_noise" in data.files else None + + def load_columnar_replay(path: Union[str, Path]) -> ColumnarReplay: path = Path(path) with np.load(path, allow_pickle=False) as data: diff --git a/utama_core/replay/columnar_writer.py b/utama_core/replay/columnar_writer.py index 75b1f627..c4dfe2cc 100644 --- a/utama_core/replay/columnar_writer.py +++ b/utama_core/replay/columnar_writer.py @@ -134,6 +134,8 @@ def __init__( self.path = path if path is not None else self._resolve_path(replay_configs) self._my_team_is_right: Optional[bool] = None + # The match's `SimNoise` and its draw, as JSON (`StrategyRunner` sets it); None writes nothing. + self.sim_noise_json: Optional[str] = None self._ticks: list[_TickRecord] = [] self._sparse_referee: list[tuple[int, RefereeData]] = [] self._last_stored_ts = 0.0 @@ -272,7 +274,9 @@ def _build_arrays(self) -> dict: designated_position[tick_i] = t.designated_position f32 = np.float32 # see _FLOAT32_KEYS + extra = {"sim_noise": np.array(self.sim_noise_json)} if self.sim_noise_json is not None else {} return { + **extra, "my_team_is_yellow": np.array(self.metadata.my_team_is_yellow), # the side at the first tick; `side_is_right` has every tick's (teams change ends at half-time) "my_team_is_right": np.array(bool(self._my_team_is_right)), diff --git a/utama_core/replay/eval_ledger.py b/utama_core/replay/eval_ledger.py new file mode 100644 index 00000000..96cc5627 --- /dev/null +++ b/utama_core/replay/eval_ledger.py @@ -0,0 +1,100 @@ +"""Every evaluation match's result, in one append-only file, for ratings across runs. + +`tools/evaluation/evaluate.py` and `round_robin.py` append one JSON line per match they play: +the environment it ran in, both sides with the fingerprint of their code, the noise seed, the +score and the shots. A rating fit reads back the matches of one environment, so each result +counts in every later evaluation until the code that played it changes. + +- **Environment** (`environment`): the shared code and machine (`fingerprint.base_fingerprint` + of the match runner), the match length, the planner and the sim noise without its seed. + Matches from different environments are never mixed: a planner or sim change replays all. +- **Player**: a strategy name and its `fingerprint.strategy_fingerprint`. Editing a strategy + makes it a new player; its old matches stay in the file but no longer count. +- **Fixture** (`fixture_id`): environment, both players, sides and seed. An rsim match is a + pure function of it, so a fixture already in the ledger is never played again. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Iterable, Optional + +from utama_core.config.settings import REPLAY_BASE_PATH +from utama_core.replay.fingerprint import ROUND_ROBIN_ENTRY, CodeGraph, _digest + +LEDGER_PATH = Path(REPLAY_BASE_PATH) / "eval" / "ledger.jsonl" + + +def environment( + graph: CodeGraph, + *, + duration_seconds: float, + sim_noise: Optional[dict], + control_scheme: str = "fpp", +) -> str: + """Id of what every match of an evaluation shares; `sim_noise` is `SimNoise.to_dict()`, + its seed left out.""" + noise = {k: v for k, v in (sim_noise or {}).items() if k != "seed"} + return _digest( + [ + "evaluation environment", + graph.base_fingerprint([ROUND_ROBIN_ENTRY]), + json.dumps( + {"duration_seconds": duration_seconds, "control_scheme": control_scheme, "sim_noise": noise}, + sort_keys=True, + ), + ] + )[:16] + + +def fixture_id(env: str, a: str, a_fp: str, b: str, b_fp: str, seed: Optional[int]) -> str: + """config_a is always yellow and starts on the right, kicking off (`match.run_match`'s defaults).""" + return _digest([env, a, a_fp, b, b_fp, json.dumps(seed)])[:16] + + +def entry(env: str, graph: CodeGraph, result: dict, seed: Optional[int], source: str) -> dict: + """A ledger line for `result` (a `match.MatchResult` as a dict, with its stats).""" + a, b = result["config_a"], result["config_b"] + a_fp, b_fp = graph.strategy_fingerprint(a)[:16], graph.strategy_fingerprint(b)[:16] + shots = (result.get("stats") or {}).get("shots") or {} + return { + "fixture": fixture_id(env, a, a_fp, b, b_fp, seed), + "env": env, + "a": a, + "a_fp": a_fp, + "b": b, + "b_fp": b_fp, + "seed": seed, + "score_a": result["score_a"], + "score_b": result["score_b"], + "shots_a": shots.get("friendly"), + "shots_b": shots.get("enemy"), + "source": source, + } + + +def append(entries: Iterable[dict], path: Path = LEDGER_PATH) -> None: + """One line per entry; each write is a single append, so concurrent writers interleave + whole lines.""" + path.parent.mkdir(parents=True, exist_ok=True) + with open(path, "a") as f: + for e in entries: + f.write(json.dumps(e, sort_keys=True) + "\n") + f.flush() + + +def load(env: str, path: Path = LEDGER_PATH) -> dict[str, dict]: + """The ledger's entries for `env`, by fixture id (the last one wins if a fixture was played + twice).""" + out: dict[str, dict] = {} + if not path.exists(): + return out + for line in path.read_text().splitlines(): + try: + e = json.loads(line) + except json.JSONDecodeError: + continue # a torn last line from a crash + if e.get("env") == env: + out[e["fixture"]] = e + return out diff --git a/utama_core/replay/fingerprint.py b/utama_core/replay/fingerprint.py index 50bf572d..51b19336 100644 --- a/utama_core/replay/fingerprint.py +++ b/utama_core/replay/fingerprint.py @@ -10,12 +10,11 @@ the other configs' fingerprints alone; editing `pickers.py` moves every config that imports it. - `base_fingerprint(entry)`: everything both sides share, from the module that plays the - match (`tools.evaluation.match` for round-robins, `utama_core.scenario_bench.scenario_scorer` for the - bench): runner, planner, referee, sim wrapper, the rsim subprocess script, the files + match (`tools.evaluation.match`): runner, planner, referee, sim wrapper, the rsim subprocess script, the files next to that code (referee profiles), both pixi environments (every installed package's name, version and build), the installed robosim binary, the CPU model and the environment variables that change numerics. -- `match_key(...)` / `bench_key(...)`: those fingerprints plus every run setting. +- `match_key(...)`: those fingerprints plus every run setting. `kernel_strategy.py` imports every strategy module, and they run in every match whichever config is playing, so a module only one config uses still runs its import-time code in every @@ -26,6 +25,9 @@ `tests/replay/test_fingerprint.py` pins that list, so a new one fails a test until someone decides how it enters the fingerprint. +A repo module counts by its syntax tree without docstrings (`_code_sha`): editing a comment, a +docstring or the formatting changes no fingerprint. + Standard library only. Nothing here imports the code it fingerprints: the repo's `.py` files are parsed, not run. """ @@ -46,9 +48,6 @@ REPO_ROOT = Path(__file__).resolve().parents[2] KERNEL_STRATEGY = "utama_core.strategy.kernel_strategy" ROUND_ROBIN_ENTRY = "tools.evaluation.match" -BENCH_ENTRY = "utama_core.scenario_bench.scenario_scorer" -# `scenario_bench._runs` plays a start through these two; the bench CLI itself only reports. -BENCH_ENTRIES = (BENCH_ENTRY, "utama_core.scenario_bench.start") # Code that runs but is not imported: `robosim_wrapper.py` starts it as a script in the # `robosim` pixi environment. @@ -90,6 +89,19 @@ def _sha(data: bytes) -> str: return hashlib.sha256(data).hexdigest() +def _code_sha(code: bytes) -> str: + """Hash of a module's syntax tree without docstrings, so comments, docstrings and formatting + don't move a fingerprint.""" + tree = ast.parse(code) + for node in ast.walk(tree): + if isinstance(node, (ast.Module, ast.ClassDef, ast.FunctionDef, ast.AsyncFunctionDef)): + body = node.body + if body and isinstance(body[0], ast.Expr) and isinstance(body[0].value, ast.Constant): + if isinstance(body[0].value.value, str): + node.body = body[1:] or [ast.Pass()] + return _sha(ast.dump(tree).encode()) + + def _digest(parts: Iterable[str]) -> str: return _sha("\n".join(parts).encode()) @@ -201,7 +213,7 @@ def file_digests(self, names: Iterable[str]) -> list[str]: mod = self.module(name) if mod is not None and mod.path is not None: code = _VERSION_LINE.sub(b"", mod.source) - rows.append(f"{mod.path.relative_to(self.root).as_posix()} {_sha(code)}") + rows.append(f"{mod.path.relative_to(self.root).as_posix()} {_code_sha(code)}") return sorted(rows) def data_files(self, names: Iterable[str]) -> list[str]: @@ -490,9 +502,12 @@ def match_key( fuzz_seed: Optional[int] = None, fuzz_interval_s: tuple[float, float] = (25.0, 45.0), recorded: bool = True, + sim_noise: Optional[dict] = None, ) -> str: """Key of one `match.run_match` call: same key, same result. `recorded` is - whether it ran with a `run_dir` (the match log and stats recorder are then live).""" + whether it ran with a `run_dir` (the match log and stats recorder are then live). + `sim_noise` (`SimNoise.to_dict()`) enters the key only when set, so matches without it + keep their keys.""" settings = { "duration_seconds": duration_seconds, "a_is_right": a_is_right, @@ -502,6 +517,8 @@ def match_key( "fuzz_interval_s": list(fuzz_interval_s) if fuzz_seed is not None else None, "recorded": recorded, } + if sim_noise is not None: + settings["sim_noise"] = sim_noise return _digest( [ "round-robin match", @@ -515,23 +532,6 @@ def match_key( ) -def bench_key(graph: CodeGraph, start: dict, candidate: str, opponent: str, *, horizon_s: float) -> str: - """Key of one bench start played by `score_scenario`: `start` is the jittered - `BenchScenario.to_dict()`, so the bank, the start and the jitter seed are all in it.""" - return _digest( - [ - "bench start", - graph.base_fingerprint(BENCH_ENTRIES), - json.dumps(start, sort_keys=True), - candidate, - graph.strategy_fingerprint(candidate), - opponent, - graph.strategy_fingerprint(opponent), - json.dumps({"horizon_s": horizon_s}), - ] - ) - - def config_names(graph: CodeGraph) -> list[str]: """The round-robin's configs, as `match._CONFIG_NAMES` finds them, from source.""" return sorted(n for n in graph.factory_modules if n != "build_default_kernel_strategy") diff --git a/utama_core/replay/scenario.py b/utama_core/replay/scenario.py index b8147b98..ec0a282b 100644 --- a/utama_core/replay/scenario.py +++ b/utama_core/replay/scenario.py @@ -102,6 +102,7 @@ _POSITION_TOLERANCE_M = 0.15 # mirrors teleport_position_accuracy_test.py's POSITION_TOLERANCE _VERIFY_MAX_TICKS = 10 # see apply_scenario's verify=True docstring for why more than 1 tick is needed _SHOWING_WINDOW_S = 0.5 # how far past `t_seconds` `scenario_from_replay(showing=...)` looks +_CLOCK_EPS_S = 1e-6 # float slack when a frame time sits exactly on a window edge # Restarts the referee only awards from STOP (`apply_scenario`). _RESTART_COMMANDS = frozenset( { @@ -173,8 +174,25 @@ def _robot_states(robots: dict, turn: bool = False) -> tuple[RobotState, ...]: ) -def _nearest_frame(replay_path: Path, t_seconds: float) -> GameFrame: - """Load every frame and pick the one whose `ts` is closest to `t_seconds`. +def _frame_origin(replay_path: Path) -> float: + """The `ts` of the replay's first frame. Times on the sidecar's and `replay_trace.py`'s + clock (and the `t_seconds` callers pass, from `fouls` or a StallEvent) count from it, not + from 0: a frame's `ts` starts at one tick (1/60 s in these replays).""" + if replay_path.suffix == ".npz": + from utama_core.replay.columnar_reader import load_columnar_replay + + columnar = load_columnar_replay(replay_path) + if columnar.n_ticks == 0: + raise ValueError(f"Replay {replay_path} contains no frames.") + return float(columnar.ts[0]) + for obj in _load_replay(replay_path): + if isinstance(obj, GameFrame): + return obj.ts + raise ValueError(f"Replay {replay_path} contains no frames.") + + +def _nearest_frame(replay_path: Path, t_seconds: float, origin: float) -> GameFrame: + """Load every frame and pick the one whose time (`ts - origin`) is closest to `t_seconds`. Dispatches on extension like `replay_player.load_frames_in_range`: `.npz` uses the columnar reader (no full-match object reconstruction), `.pkl` @@ -188,7 +206,7 @@ def _nearest_frame(replay_path: Path, t_seconds: float) -> GameFrame: columnar = load_columnar_replay(replay_path) if columnar.n_ticks == 0: raise ValueError(f"Replay {replay_path} contains no frames.") - diffs = abs(columnar.ts - t_seconds) + diffs = abs((columnar.ts - origin) - t_seconds) tick = int(diffs.argmin()) return columnar.frame_at(tick) @@ -197,7 +215,7 @@ def _nearest_frame(replay_path: Path, t_seconds: float) -> GameFrame: for obj in _load_replay(replay_path): if not isinstance(obj, GameFrame): continue - diff = abs(obj.ts - t_seconds) + diff = abs((obj.ts - origin) - t_seconds) if diff < best_diff: best_diff = diff best = obj @@ -282,7 +300,7 @@ def _command_on(frame: GameFrame, replay_path: Path, t_seconds: float) -> Option return _referee_command_at(_sidecar_path(replay_path), t_seconds) -def _first_frame_showing(replay_path: Path, t_seconds: float, command: RefereeCommand) -> GameFrame: +def _first_frame_showing(replay_path: Path, t_seconds: float, command: RefereeCommand, origin: float) -> GameFrame: """The first frame from `t_seconds` on that shows `command`. A replay frame carries the referee's decision on the frame before it (`StrategyRunner` @@ -290,29 +308,32 @@ def _first_frame_showing(replay_path: Path, t_seconds: float, command: RefereeCo for a command is one tick before the first frame showing it: the frame at that time, the nearest, still shows the command before. """ - for frame in load_frames_in_range(replay_path, t_seconds, t_seconds + _SHOWING_WINDOW_S): - if _command_on(frame, replay_path, frame.ts) == command: + start = origin + t_seconds - _CLOCK_EPS_S + for frame in load_frames_in_range(replay_path, start, start + _SHOWING_WINDOW_S + _CLOCK_EPS_S): + if _command_on(frame, replay_path, frame.ts - origin) == command: return frame raise ValueError(f"No frame in {replay_path} shows {command.name} within {_SHOWING_WINDOW_S}s of t={t_seconds}.") def scenario_from_replay(replay_path, t_seconds: float, *, showing: Optional[RefereeCommand] = None) -> Scenario: """Read the frame nearest `t_seconds` from `replay_path` and build a - `Scenario` from it, merging in the referee command from the - `.intentions.jsonl` sidecar (frames themselves carry no referee data — - see module docstring) and the two strategy config names from the - tournament directory's `summary.json`, when available. + `Scenario` from it. `t_seconds` counts from the replay's first frame, the clock the sidecar, + `fouls` and `replay_trace.py` use (see `_frame_origin`). It merges in the referee command from + the `.intentions.jsonl` sidecar (frames themselves carry no referee data — see module + docstring) and the two strategy config names from the tournament directory's `summary.json`, + when available. `showing`: take instead the first frame from `t_seconds` on that shows this referee command (raising `ValueError` if none does within `_SHOWING_WINDOW_S`): for a restart at the time the match log gives it, see `_first_frame_showing`. """ replay_path = Path(replay_path) + origin = _frame_origin(replay_path) if showing is None: - frame = _nearest_frame(replay_path, t_seconds) + frame = _nearest_frame(replay_path, t_seconds, origin) referee_command = _command_on(frame, replay_path, t_seconds) else: - frame = _first_frame_showing(replay_path, t_seconds, showing) + frame = _first_frame_showing(replay_path, t_seconds, showing, origin) referee_command = showing config_a_name, config_b_name = _config_names_from_summary(replay_path) @@ -425,7 +446,7 @@ def apply_scenario(runner, scenario: Scenario, *, verify: bool = True) -> None: restart, its preparation timed from now (a penalty goes live once its kicker has been at the mark for the referee's prepare time), and `NORMAL_START`/`FORCE_START` as live play. The runner's own starting - command (the bench seeds a yellow kickoff) is overwritten either way. + command (a fresh runner seeds a yellow kickoff) is overwritten either way. When `verify` is True (the default), steps the runner (up to `_VERIFY_MAX_TICKS` times, stopping early once every position is diff --git a/utama_core/replay/ssl_log.py b/utama_core/replay/ssl_log.py index 0386648b..1ebfcc29 100644 --- a/utama_core/replay/ssl_log.py +++ b/utama_core/replay/ssl_log.py @@ -49,18 +49,41 @@ Commands keep their protocol values, including `GOAL_*` and, before 2021, `INDIRECT_FREE_*`, with one change: a free kick reads `NORMAL_START` once it is taken (`free_kick_taken`), as in our referee. The real game controller keeps the free-kick command through the play after it. -- `.source.json`: the log, teams, final score, tracker source and field geometry. +- `.source.json`: the log, teams, final score, tracker source and field geometry, and + under `extras` which of the sidecars below were written. + +Sidecars, for measuring physics and vision rather than replaying (`tools/convert_ssl_log.py` +can add them to a replay already converted). Teams are in kick-off colours like the replay +(yellow friendly), positions in metres in field coordinates, times `ts` in seconds on the +replay's clock. The arrays are `np.savez_compressed` archives named `.zip`, so a glob for +replays (`*.npz`) skips them; `np.load` reads them as usual. +- `.kicks.json` (tracker logs): every kick the tracker reported (`KickedBall`), once per + kick: start time, kicker (the last robot the tracker named; `kickers` counts how many it + named), start position, the first and the last (most refined) velocity estimate, on how + many ticks it was reported, and the stop time and position when the tracker gave them. +- `.tracker.zip` (tracker logs): per tick, the `visibility` (0-1) of the ball and every + robot, and robots' angular velocity, NaN where not given. Robot arrays are `[tick, team, id]`, + team 0 yellow and 1 blue, ids 0-15. +Not every autoref's tracker sends kicks or visibility (in 2026 ER-Force sent neither, TIGERs +both), so each comes from the replay's tracker if it sends it, else from the source that sends +it most (`source.json`'s `extras.kicks_source` / `visibility_source`); angular velocity always +from the replay's tracker. +- `.vision.zip` (`raw_vision=True`): every camera detection inside the replay window, + unfiltered: one row per camera frame (`frame_*`: receive time `ts`, capture and send time on + the camera clock, camera id, frame number), per ball detection (`ball_*`) and per robot + detection (`robot_*`), with confidence, pixel position and the row of the frame they came in. """ from __future__ import annotations +import array import dataclasses import gzip import json import math import struct import urllib.parse -from collections import defaultdict +from collections import Counter, defaultdict from pathlib import Path from typing import Iterator, Optional @@ -113,6 +136,7 @@ ) ) IN_PLAY_M = 0.05 # the rulebook's ball in play: moved at least 0.05 m +_SAME_KICK_S = 0.05 # tracker kick reports whose start times differ by less than this are one kick def read_records(path) -> Iterator[tuple[int, int, bytes]]: @@ -206,12 +230,16 @@ def referee_data(msg: Referee, received_s: float, swapped: bool = False) -> Refe class _Scan: """One pass over a log: the referee messages that change something, every tracker source's - frames, the camera detections and the field geometry.""" + frames, the camera detections (kept only without a tracker) and the field geometry; with + `raw_vision`, every camera detection as `_VisionRows` too.""" - def __init__(self, path: Path): + def __init__(self, path: Path, raw_vision: bool = False): self.referee: list[tuple[int, Referee]] = [] self.trackers: dict[str, list[tuple[int, bytes]]] = defaultdict(list) self.vision: dict[int, list[tuple[int, bytes]]] = defaultdict(list) # by record type + self.raw_vision = _VisionRows() if raw_vision else None + self.kick_frames: Counter[str] = Counter() # per tracker source, frames with a `KickedBall` + self.visibility_frames: Counter[str] = Counter() # ... and with the ball's visibility self.geometry = None self.last_ns = 0 last_key = None @@ -227,21 +255,115 @@ def __init__(self, path: Path): packet = TrackerWrapperPacket() packet.ParseFromString(payload) if packet.HasField("tracked_frame"): - self.trackers[packet.source_name or packet.uuid].append((ts_ns, payload)) + name, frame = packet.source_name or packet.uuid, packet.tracked_frame + self.trackers[name].append((ts_ns, payload)) self.vision.clear() + # not every autoref's tracker sends these (2026: ER-Force neither, TIGERs both) + self.kick_frames[name] += frame.HasField("kicked_ball") + self.visibility_frames[name] += bool(frame.balls) and frame.balls[0].HasField("visibility") elif msg_type in (VISION_2010, VISION_2014): if not self.trackers: # camera detections are only read when there is no tracker self.vision[msg_type].append((ts_ns, payload)) - if msg_type == VISION_2014 and self.geometry is None: + if msg_type == VISION_2014 and (self.geometry is None or self.raw_vision is not None): packet = SSL_WrapperPacket() packet.ParseFromString(payload) - if packet.HasField("geometry"): + if packet.HasField("geometry") and self.geometry is None: self.geometry = packet.geometry.field + if packet.HasField("detection") and self.raw_vision is not None: + self.raw_vision.add(ts_ns, packet.detection) else: continue # the index record (type 6) has no meaningful time self.last_ns = max(self.last_ns, ts_ns) +class _VisionRows: + """Camera detections as flat typed columns, appended while scanning: a match's vision packets + kept as messages would not fit in memory. Positions in millimetres, as sent.""" + + FRAME = {"recv_ns": "q", "t_capture": "d", "t_sent": "d", "camera": "i", "number": "q"} + BALL = {"frame": "q", "x": "f", "y": "f", "z": "f", "pixel_x": "f", "pixel_y": "f", "confidence": "f", "area": "i"} + ROBOT = { + "frame": "q", + "team": "b", + "id": "i", + "x": "f", + "y": "f", + "orientation": "f", + "pixel_x": "f", + "pixel_y": "f", + "height": "f", + "confidence": "f", + } + + def __init__(self): + self.columns = { + f"{table}_{name}": array.array(code) + for table, fields in (("frame", self.FRAME), ("ball", self.BALL), ("robot", self.ROBOT)) + for name, code in fields.items() + } + + def add(self, recv_ns: int, detection) -> None: + c = self.columns + row = len(c["frame_recv_ns"]) + for name, value in ( + ("recv_ns", recv_ns), + ("t_capture", detection.t_capture), + ("t_sent", detection.t_sent), + ("camera", detection.camera_id), + ("number", detection.frame_number), + ): + c[f"frame_{name}"].append(value) + for b in detection.balls: + for name, value in ( + ("frame", row), + ("x", b.x), + ("y", b.y), + ("z", b.z), + ("pixel_x", b.pixel_x), + ("pixel_y", b.pixel_y), + ("confidence", b.confidence), + ("area", b.area if b.HasField("area") else -1), + ): + c[f"ball_{name}"].append(value) + for team, robots in ((_YELLOW, detection.robots_yellow), (_BLUE, detection.robots_blue)): + for r in robots: + for name, value in ( + ("frame", row), + ("team", team), + ("id", r.robot_id if r.HasField("robot_id") else -1), + ("x", r.x), + ("y", r.y), + ("orientation", r.orientation if r.HasField("orientation") else math.nan), + ("pixel_x", r.pixel_x), + ("pixel_y", r.pixel_y), + ("height", r.height if r.HasField("height") else math.nan), + ("confidence", r.confidence), + ): + c[f"robot_{name}"].append(value) + + def arrays(self, start_ns: int, end_ns: int, tick_ns: np.ndarray, swapped: np.ndarray) -> dict[str, np.ndarray]: + """The frames received inside [start_ns, end_ns) and their detections: receive times as + `frame_ts` on the replay clock, positions in metres, teams in kick-off colours.""" + a = {name: np.frombuffer(col, dtype=col.typecode) for name, col in self.columns.items()} + recv = a["frame_recv_ns"] + keep = (recv >= start_ns) & (recv < end_ns) + new_row = np.cumsum(keep) - 1 # a kept frame's row among the kept frames + tick = np.clip(np.searchsorted(tick_ns, recv, side="right") - 1, 0, max(len(tick_ns) - 1, 0)) + frame_swapped = swapped[tick] if len(swapped) else np.zeros(len(recv), dtype=bool) + out = {"frame_ts": (recv[keep] - start_ns) / 1e9} + out.update({f"frame_{name}": a[f"frame_{name}"][keep] for name in ("t_capture", "t_sent", "camera", "number")}) + for table, fields in (("ball", self.BALL), ("robot", self.ROBOT)): + rows = a[f"{table}_frame"] + kept = keep[rows] + for name in fields: + column = a[f"{table}_{name}"][kept] + out[f"{table}_{name}"] = column / 1000 if name in ("x", "y", "z") else column + out[f"{table}_frame"] = new_row[rows[kept]] + if table == "robot": + out["robot_team"] = (out["robot_team"] ^ frame_swapped[rows[kept]]).astype(np.int8) + return out + + class _World: """Robots and ball on the tick grid. Robot arrays are [tick, team, id, ...], NaN where absent.""" @@ -252,6 +374,11 @@ def __init__(self, n_ticks: int): self.ball_p = np.full((n_ticks, 3), np.nan) self.ball_v = np.full((n_ticks, 3), np.nan) self.ball_seen = np.zeros(n_ticks, dtype=bool) + # from a tracker only (NaN from camera detections): the `.tracker.zip` and `.kicks.json` sidecars + self.ball_visibility = np.full(n_ticks, np.nan) + self.robot_visibility = np.full((n_ticks, 2, _MAX_ID), np.nan) + self.robot_vel_angular = np.full((n_ticks, 2, _MAX_ID), np.nan) + self.kicks: list[dict] = [] # see `_add_kick` def finish(self) -> None: """Fill what the source did not give: missing velocities from positions, accelerations @@ -303,7 +430,8 @@ def _tracker_world(frames: list[tuple[int, bytes]], tick_ns: np.ndarray) -> tupl for k, i in enumerate(latest): if i < 0: continue - if i != decoded: + new_frame = i != decoded + if new_frame: packet.ParseFromString(frames[i][1]) decoded = i frame = packet.tracked_frame @@ -316,15 +444,52 @@ def _tracker_world(frames: list[tuple[int, bytes]], tick_ns: np.ndarray) -> tupl world.robot_orientation[k, team, rid] = robot.orientation if robot.HasField("vel"): world.robot_v[k, team, rid] = (robot.vel.x, robot.vel.y) + if robot.HasField("vel_angular"): + world.robot_vel_angular[k, team, rid] = robot.vel_angular + if robot.HasField("visibility"): + world.robot_visibility[k, team, rid] = robot.visibility if frame.balls: ball = frame.balls[0] # the primary ball world.ball_p[k] = (ball.pos.x, ball.pos.y, ball.pos.z) world.ball_seen[k] = True if ball.HasField("vel"): world.ball_v[k] = (ball.vel.x, ball.vel.y, ball.vel.z) + if ball.HasField("visibility"): + world.ball_visibility[k] = ball.visibility + if new_frame and frame.HasField("kicked_ball"): # a kick lasts many frames: those between ticks can go + _add_kick(world.kicks, frame.kicked_ball, frames[i][0], frame.timestamp) return world, dropped +def _add_kick(kicks: list[dict], kick, recv_ns: int, frame_timestamp: float) -> None: + """Fold one tracked frame's `KickedBall` into `kicks`. The tracker repeats a kick in every + frame until the ball stops, refining it: a report starting within `_SAME_KICK_S` of the last + kick's start is that kick, with its velocity, stop and kicker updated (the tracker sometimes + gives a kick to another robot a few frames in; `kickers` counts the robots it named). + Times move to the log's receive clock by the frame's receive time minus its tracker time.""" + kicker = (kick.robot_id.team - 1, kick.robot_id.id) if kick.HasField("robot_id") else None + last = kicks[-1] if kicks else None + if last is None or abs(kick.start_timestamp - last["start_timestamp"]) >= _SAME_KICK_S: + last = { + "start_timestamp": kick.start_timestamp, + "start_ns": recv_ns - round((frame_timestamp - kick.start_timestamp) * 1e9), + "kicker": kicker, + "pos": [kick.pos.x, kick.pos.y], + "vel_first": [kick.vel.x, kick.vel.y, kick.vel.z], + "reports": 0, + "kickers": set(), + } + kicks.append(last) + last["kicker"] = kicker + last["kickers"].add(kicker) + last["vel"] = [kick.vel.x, kick.vel.y, kick.vel.z] + last["reports"] += 1 + if kick.HasField("stop_timestamp"): + last["stop_ns"] = recv_ns - round((frame_timestamp - kick.stop_timestamp) * 1e9) + if kick.HasField("stop_pos"): + last["stop_pos"] = [kick.stop_pos.x, kick.stop_pos.y] + + def _vision_world(packets: list[tuple[int, bytes]], tick_ns: np.ndarray) -> tuple[_World, int]: """The world merged from raw camera detections: per tick each camera's newest frame (within `_CAMERA_STALE_S`), a robot seen by several cameras at the confidence-weighted mean, the ball @@ -498,12 +663,19 @@ def _geometry(field) -> Optional[dict]: return geometry -def convert_log(log_path, out_dir, stem: Optional[str] = None) -> dict: - """Convert one game log to `/.npz` (+ `.sparse_referee.pkl`, `.source.json`). - Returns what `source.json` holds. See the module docstring for what the replay contains.""" +def convert_log( + log_path, out_dir, stem: Optional[str] = None, raw_vision: bool = False, write_replay: bool = True +) -> dict: + """Convert one game log to `/.npz` (+ `.sparse_referee.pkl`, `.source.json`) + and the sidecars (`.kicks.json`, `.tracker.zip`, and `.vision.zip` with `raw_vision`). + Returns what `source.json` holds. See the module docstring for what each file contains. + + `write_replay=False` writes only the sidecars, for a replay converted before they existed: + the replay is left as it is, and its `source.json` only gains `extras`. It must be the + replay of this log (same duration), or this raises.""" log_path, out_dir = Path(log_path), Path(out_dir) stem = stem or log_stem(log_path.name) - scan = _Scan(log_path) + scan = _Scan(log_path, raw_vision=raw_vision) if not scan.referee: raise ValueError(f"{log_path}: no referee messages") @@ -529,8 +701,7 @@ def convert_log(log_path, out_dir, stem: Optional[str] = None) -> dict: source = "vision" vision = scan.vision[VISION_2014] or scan.vision[VISION_2010] world, dropped_ids = _vision_world(vision, tick_ns) - for robots in (world.robot_p, world.robot_v, world.robot_orientation): - robots[swapped] = robots[swapped][:, ::-1] # back to the kick-off colours + _to_kick_off_colours(world, swapped) field = _geometry(scan.geometry) walls = ( (field["field_length"] / 2 + field["boundary_width"], field["field_width"] / 2 + field["boundary_width"]) @@ -559,6 +730,16 @@ def convert_log(log_path, out_dir, stem: Optional[str] = None) -> dict: side_is_right ^= inverted out_dir.mkdir(parents=True, exist_ok=True) + duration_s = round(float(ts[-1]) if len(ts) else 0.0, 3) + source_path = out_dir / f"{stem}.source.json" + if not write_replay: + summary = json.loads(source_path.read_text()) + if summary["duration_s"] != duration_s: + raise ValueError(f"{source_path}: {summary['duration_s']} s, but {log_path.name} gives {duration_s} s") + summary["extras"] = _write_extras(out_dir, stem, world, source, scan, start_ns, end_ns, tick_ns, swapped) + source_path.write_text(json.dumps(summary, indent=2) + "\n") + return summary + seen = ~np.isnan(world.robot_p[..., 0]) writer = ColumnarReplayWriter( ColumnarReplayWriterConfig(replay_name=stem, overwrite_existing=True, checkpoint_every_s=0), @@ -604,7 +785,7 @@ def convert_log(log_path, out_dir, stem: Optional[str] = None) -> dict: "score": {"yellow": last.yellow_team.score, "blue": last.blue_team.score}, "colours_swapped": bool(swapped_msg.any()), "start_unix_s": start_ns / 1e9, - "duration_s": round(float(ts[-1]) if len(ts) else 0.0, 3), + "duration_s": duration_s, "world_source": source, "tracker_frames": tracker_frames, "ball_unseen_share": round(float(1 - world.ball_seen.mean()) if len(ts) else 1.0, 4), @@ -612,6 +793,100 @@ def convert_log(log_path, out_dir, stem: Optional[str] = None) -> dict: "sides_from_goalkeepers": bool(unknown.any()), "sides_inverted_share": round(float(inverted.mean()) if len(ts) else 0.0, 4), "field": field, + "extras": _write_extras(out_dir, stem, world, source, scan, start_ns, end_ns, tick_ns, swapped), } - (out_dir / f"{stem}.source.json").write_text(json.dumps(summary, indent=2) + "\n") + source_path.write_text(json.dumps(summary, indent=2) + "\n") return summary + + +def _to_kick_off_colours(world: _World, swapped: np.ndarray) -> None: + for robots in ( + world.robot_p, + world.robot_v, + world.robot_orientation, + world.robot_visibility, + world.robot_vel_angular, + ): + robots[swapped] = robots[swapped][:, ::-1] + + +def _extras_source(frames: Counter, source: str) -> Optional[str]: + """The tracker source to take kicks or visibility from: the replay's own if it sends them, + else the one that sends them in the most frames; None if none does.""" + if frames[source] > 0: + return source + best = max(frames, key=lambda name: (frames[name], name), default=None) + return best if best is not None and frames[best] > 0 else None + + +def _write_extras( + out_dir: Path, stem: str, world: _World, source: str, scan: _Scan, start_ns, end_ns, tick_ns, swapped +) -> dict: + """Write the sidecars (module docstring) this log has data for; returns `source.json`'s + `extras`, which names the tracker source the kicks and the visibility came from. The + replay's tracker (`world`, from `source`) may not send them; another autoref's may.""" + extras = {"kicks": None, "kicks_source": None, "visibility_source": None, "tracker": False, "raw_vision": False} + if scan.trackers: + worlds = {source: world} + + def world_of(name: Optional[str]) -> _World: + if name is None: + return world # NaN visibility and no kicks, as sent + if name not in worlds: + worlds[name] = _tracker_world(scan.trackers[name], tick_ns)[0] + _to_kick_off_colours(worlds[name], swapped) + return worlds[name] + + kicks_source = _extras_source(scan.kick_frames, source) + visibility_source = _extras_source(scan.visibility_frames, source) + kicks = _kick_rows(world_of(kicks_source).kicks, start_ns, end_ns, tick_ns, swapped) + (out_dir / f"{stem}.kicks.json").write_text(json.dumps(kicks, indent=1) + "\n") + shown = world_of(visibility_source) + _save_arrays( + out_dir / f"{stem}.tracker.zip", + ts=(tick_ns - start_ns) / 1e9, + ball_visibility=shown.ball_visibility.astype(np.float32), + robot_visibility=shown.robot_visibility.astype(np.float32), + robot_vel_angular=world.robot_vel_angular.astype(np.float32), + ) + extras.update(kicks=len(kicks), kicks_source=kicks_source, visibility_source=visibility_source, tracker=True) + if scan.raw_vision is not None: + _save_arrays(out_dir / f"{stem}.vision.zip", **scan.raw_vision.arrays(start_ns, end_ns, tick_ns, swapped)) + extras["raw_vision"] = True + return extras + + +def _save_arrays(path: Path, **arrays: np.ndarray) -> None: + """`np.savez_compressed` under `path` exactly (it appends `.npz` to a name that lacks it).""" + with open(path, "wb") as f: + np.savez_compressed(f, **arrays) + + +def _kick_rows(kicks: list[dict], start_ns: int, end_ns: int, tick_ns: np.ndarray, swapped: np.ndarray) -> list[dict]: + """The tracker's kicks (`_add_kick`) that started inside the replay window, as `.kicks.json` rows.""" + rows = [] + for kick in kicks: + if not start_ns <= kick["start_ns"] < end_ns: + continue + team = rid = None + if kick["kicker"] is not None and kick["kicker"][0] in (_YELLOW, _BLUE): + tick = max(int(np.searchsorted(tick_ns, kick["start_ns"], side="right")) - 1, 0) + team = ("yellow", "blue")[kick["kicker"][0] ^ int(swapped[tick])] + rid = kick["kicker"][1] + row = { + "ts": round((kick["start_ns"] - start_ns) / 1e9, 4), + "team": team, + "robot_id": rid, + "pos": [round(v, 4) for v in kick["pos"]], + "vel_first": [round(v, 4) for v in kick["vel_first"]], + "vel": [round(v, 4) for v in kick["vel"]], + "speed": round(math.hypot(*kick["vel"]), 4), + "reports": kick["reports"], + "kickers": len(kick["kickers"]), + } + if "stop_ns" in kick: + row["stop_ts"] = round((kick["stop_ns"] - start_ns) / 1e9, 4) + if "stop_pos" in kick: + row["stop_pos"] = [round(v, 4) for v in kick["stop_pos"]] + rows.append(row) + return rows diff --git a/utama_core/rsoccer_simulator/src/Entities/Robot.py b/utama_core/rsoccer_simulator/src/Entities/Robot.py index cbf91577..23b18b62 100644 --- a/utama_core/rsoccer_simulator/src/Entities/Robot.py +++ b/utama_core/rsoccer_simulator/src/Entities/Robot.py @@ -14,6 +14,7 @@ class Robot: v_theta: float = 0 kick_v_x: float = 0 kick_v_z: float = 0 + kick_angle: float = 0 # radians off the facing direction (`SimNoise` kick direction) dribbler: bool = False infrared: bool = False wheel_speed: bool = False diff --git a/utama_core/rsoccer_simulator/src/Simulators/robosim/robosim_subprocess.py b/utama_core/rsoccer_simulator/src/Simulators/robosim/robosim_subprocess.py index b8945d6a..e972c3f6 100644 --- a/utama_core/rsoccer_simulator/src/Simulators/robosim/robosim_subprocess.py +++ b/utama_core/rsoccer_simulator/src/Simulators/robosim/robosim_subprocess.py @@ -159,6 +159,9 @@ def main(): elif "get_field_params" in cmd: fp = sim.get_field_params() _emit({"field_params": fp}) + elif "set_physics_params" in cmd: + sim.sim.set_physics_params(**cmd["set_physics_params"]) + _emit({"ack": True}) elif "get_state" in cmd: state = sim.get_state() _emit({"state": state}) diff --git a/utama_core/rsoccer_simulator/src/Simulators/robosim/robosim_wrapper.py b/utama_core/rsoccer_simulator/src/Simulators/robosim/robosim_wrapper.py index 95f46ecc..da53582c 100644 --- a/utama_core/rsoccer_simulator/src/Simulators/robosim/robosim_wrapper.py +++ b/utama_core/rsoccer_simulator/src/Simulators/robosim/robosim_wrapper.py @@ -28,6 +28,11 @@ except ImportError: _HAS_NATIVE_ROBOSIM = False +_OLD_BINARY = ( + "this robosim binary has no set_physics_params: ball friction, kick direction and substep settings " + "need one built from vendor/rSim (see vendor/rSim/FORK_NOTES.md)" +) + # Sim reuse (opt-in, `enable_sim_reuse`): a process that plays many short scenarios keeps its # sim subprocesses, since starting one (`pixi run`, imports, world build) costs about as much # CPU as a second of play. Idle sims wait here by their constructor arguments; `acquire` @@ -120,6 +125,11 @@ def reset(self, ball_pos, blue_robots_pos, yellow_robots_pos): def get_field_params(self): return self.sim.get_field_params() + def set_physics_params(self, **params): + if not hasattr(self.sim, "set_physics_params"): + raise RuntimeError(_OLD_BINARY) + self.sim.set_physics_params(**params) + def get_state(self): if hasattr(self.sim, "get_state_array"): return self.sim.get_state_array() @@ -283,6 +293,12 @@ def get_field_params(self): def get_state(self): return self._request({"get_state": True})["state"] + def set_physics_params(self, **params): + try: + self._request({"set_physics_params": params}) + except RuntimeError as e: + raise RuntimeError(f"{_OLD_BINARY} ({e})") from e + def close(self): """Done with this sim: parked for the next `acquire` when reuse is on, else terminated.""" if self._released: diff --git a/utama_core/rsoccer_simulator/src/Simulators/rsim.py b/utama_core/rsoccer_simulator/src/Simulators/rsim.py index d430031f..0ef37b92 100644 --- a/utama_core/rsoccer_simulator/src/Simulators/rsim.py +++ b/utama_core/rsoccer_simulator/src/Simulators/rsim.py @@ -2,7 +2,7 @@ import numpy as np -from utama_core.rsoccer_simulator.src.Entities import Field, Frame, FrameSSL, FrameVSS +from utama_core.rsoccer_simulator.src.Entities import Field, Frame, FrameSSL from utama_core.rsoccer_simulator.src.Simulators.robosim.robosim_wrapper import ( RSimSubprocessWrapper, acquire_simulator, @@ -67,6 +67,17 @@ def get_frame(self) -> Frame: def get_field_params(self): return Field(**self.simulator.get_field_params()) + def set_physics_params( + self, substeps: int, ball_slide_decel: float, ball_roll_decel: float, ball_slide_fraction: float + ): + """The fork's tunable physics (`SSLWorld::setPhysicsParams`); kept across `reset()`.""" + self.simulator.set_physics_params( + substeps=substeps, + ball_slide_decel=ball_slide_decel, + ball_roll_decel=ball_roll_decel, + ball_slide_fraction=ball_slide_fraction, + ) + def _placement_dict_from_frame(self, frame: Frame): replacement_pos: Dict[str, np.ndarray] = {} @@ -105,56 +116,17 @@ def _init_simulator( raise NotImplementedError -class RSimVSS(RSim): - def send_commands(self, commands): - sim_commands = np.zeros((self.n_robots_blue + self.n_robots_yellow, 2), dtype=np.float64) - - for cmd in commands: - if cmd.yellow: - rbt_id = self.n_robots_blue + cmd.id - else: - rbt_id = cmd.id - sim_commands[rbt_id][0] = cmd.v_wheel0 - sim_commands[rbt_id][1] = cmd.v_wheel1 - self._last_state = self.simulator.step(sim_commands) - - def get_frame(self) -> FrameVSS: - # Reuse the state `step()` already returned this tick instead of a - # second, separate `get_state()` round-trip — see the `_last_state` - # comment in `RSim.__init__`/`reset()` for why this is also a - # correctness fix, not just avoiding redundant I/O. Fall back to a - # real fetch when there's no cached step yet (first call after - # construction, or right after a `reset()`). - state = self._last_state if self._last_state is not None else self.simulator.get_state() - # Update frame with new state - frame = FrameVSS() - frame.parse(state, self.n_robots_blue, self.n_robots_yellow) - - return frame - - def _init_simulator( - self, - field_type, - n_robots_blue, - n_robots_yellow, - time_step_ms, - ) -> RSimSubprocessWrapper: - return acquire_simulator( - sim_type="VSS", - n_blue=n_robots_blue, - n_yellow=n_robots_yellow, - field_type=field_type, - time_step_ms=time_step_ms, - ) - - class RSimSSL(RSim): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._sim_cmds = np.zeros((self.n_robots_blue + self.n_robots_yellow, 8), dtype=np.float64) + # With a 9th column (a kick's direction error) only on ticks where some kick has one, so + # every other tick sends rsim exactly what it got before the field existed. + self._sim_cmds_angled = np.zeros((self.n_robots_blue + self.n_robots_yellow, 9), dtype=np.float64) def send_commands(self, commands): - sim_cmds = self._sim_cmds + angled = any(cmd.kick_angle for cmd in commands) + sim_cmds = self._sim_cmds_angled if angled else self._sim_cmds sim_cmds.fill(0.0) for cmd in commands: @@ -179,6 +151,8 @@ def send_commands(self, commands): sim_cmds[rbt_id][5] = cmd.kick_v_x sim_cmds[rbt_id][6] = cmd.kick_v_z sim_cmds[rbt_id][7] = cmd.dribbler + if angled: + sim_cmds[rbt_id][8] = cmd.kick_angle self._last_state = self.simulator.step(sim_cmds) diff --git a/utama_core/rsoccer_simulator/src/Utils/sim_noise.py b/utama_core/rsoccer_simulator/src/Utils/sim_noise.py new file mode 100644 index 00000000..00c71c43 --- /dev/null +++ b/utama_core/rsoccer_simulator/src/Utils/sim_noise.py @@ -0,0 +1,372 @@ +"""Seeded variation of rsim: latency, kicks, ball friction, motion limits and vision noise, all off +by default. + +`SimNoise` holds the ranges and the seed (see `docs/sim_realism.md`, Phase 2). `SimNoise.draw()` +turns them into one match's values: each robot's latency and kick-speed offset, drawn once per +match. Kick-to-kick speed noise and vision noise come from their own seeded streams while the +match plays (`SSLStandardEnv`). + +Every draw is keyed by team colour and robot id, never by which strategy plays, so two matches +with the same seed give the same robots the same physics: an A/B comparison against one +opponent is paired. Each robot's kicks and the vision noise use separate streams, so a strategy +that kicks more often does not shift anyone else's noise. + +Ball friction, kick direction and the physics substep count are applied inside the C++ physics +(`vendor/rSim`: `SSL.set_physics_params` and the kick command's direction field), so they need a +robosim binary built from this repo's fork (`vendor/rSim/FORK_NOTES.md`). +""" + +from __future__ import annotations + +import dataclasses +import math +from collections import deque +from dataclasses import dataclass +from typing import Optional + +import numpy as np + +from utama_core.rsoccer_simulator.src.Utils.gaussian_noise import RsimGaussianNoise + +COLOURS = ("team_blue", "team_yellow") + +# Stream ids under the seed: one for the per-match draw, one for vision, one per robot's kicks. +_DRAW_STREAM = 0 +_VISION_STREAM = 1 +_KICK_STREAM = 2 +# Added later, as their own streams, so the draws above stay what they were for a given seed. +_PHYSICS_STREAM = 3 +_KICK_ANGLE_STREAM = 4 + +Range = Optional[tuple[float, float]] +_RANGE_FIELDS = ("ball_slide_decel", "ball_roll_decel", "max_speed", "max_accel", "max_brake") + + +@dataclass(frozen=True) +class SimNoise: + """Ranges for one match's sim variation; the defaults change nothing. + + Args: + seed: every random value in the match follows from it. + latency_s: each robot's command latency, drawn uniformly from this range once per match + and rounded to whole ticks. A robot's commands reach the sim that many ticks late. + kick_speed_offset_sd: each robot's mean kick speed differs from the nominal speed by a + normal draw with this SD (m/s), once per match. + kick_speed_sd: every kick's speed varies around its robot's mean by this SD (m/s). + vision: Gaussian position and heading noise on every detection. + vanishing: the chance that any one detection is dropped from a frame. + ball_roll_decel: the rolling ball's deceleration (m/s^2), drawn uniformly once per match. + Setting it switches rsim from its constant friction force (about 0.65 m/s^2) to + two-phase friction: after a kick the ball slides at `ball_slide_decel` until its speed + falls to `ball_slide_fraction` of the kick speed, then rolls at this. + ball_slide_decel: the sliding phase's deceleration (m/s^2), drawn once per match. Needs + `ball_roll_decel`; unset, a kicked ball rolls straight away. + ball_slide_fraction: where sliding ends, as a fraction of the kick speed (5/7 for a solid + ball whose spin has caught up with its speed). + kick_angle_offset_sd_deg: each robot's kicks leave its facing direction by a constant normal + draw with this SD (degrees), once per match. + kick_angle_sd_deg: every kick's direction varies around its robot's offset by this SD. + max_speed, max_accel, max_brake: each robot's hardware limits (m/s, m/s^2), drawn uniformly + once per match and applied to the commands the sim acts on, below any planner. Braking is + any command that lowers the robot's speed. + substeps: physics steps per sim tick (rsim's own is 5). + """ + + seed: int = 0 + latency_s: tuple[float, float] = (0.0, 0.0) + kick_speed_offset_sd: float = 0.0 + kick_speed_sd: float = 0.0 + vision: RsimGaussianNoise = RsimGaussianNoise() + vanishing: float = 0.0 + ball_roll_decel: Range = None + ball_slide_decel: Range = None + ball_slide_fraction: float = 5.0 / 7.0 + kick_angle_offset_sd_deg: float = 0.0 + kick_angle_sd_deg: float = 0.0 + max_speed: Range = None + max_accel: Range = None + max_brake: Range = None + substeps: int = 5 + + def __post_init__(self): + lo, hi = self.latency_s + assert 0 <= lo <= hi, "latency_s must be a range of non-negative seconds" + assert self.kick_speed_offset_sd >= 0 and self.kick_speed_sd >= 0 + assert 0 <= self.vanishing < 1 + for name in _RANGE_FIELDS: + r = getattr(self, name) + assert r is None or 0 <= r[0] <= r[1], f"{name} must be a non-negative (low, high) range" + assert ( + self.ball_slide_decel is None or self.ball_roll_decel is not None + ), "ball_slide_decel needs ball_roll_decel" + assert 0 < self.ball_slide_fraction <= 1 + assert self.kick_angle_offset_sd_deg >= 0 and self.kick_angle_sd_deg >= 0 + assert self.substeps >= 1 + + @property + def native_physics(self) -> bool: + """Whether any setting needs the fork's C++ physics (`set_physics_params`).""" + return ( + self.ball_roll_decel is not None + or self.substeps != 5 + or bool(self.kick_angle_offset_sd_deg or self.kick_angle_sd_deg) + ) + + @property + def motion_limits(self) -> bool: + return any(r is not None for r in (self.max_speed, self.max_accel, self.max_brake)) + + @classmethod + def from_dict(cls, d: dict) -> "SimNoise": + d = dict(d) + if "latency_s" in d: + d["latency_s"] = tuple(d["latency_s"]) + if "vision" in d: + d["vision"] = RsimGaussianNoise(**d["vision"]) + for name in _RANGE_FIELDS: + if d.get(name) is not None: + d[name] = tuple(d[name]) + return cls(**d) + + def to_dict(self) -> dict: + """Every setting, except later ones still at their default, so a match key from before + they existed stays the same.""" + d = dataclasses.asdict(self) + d["latency_s"] = list(self.latency_s) + for name in _LATER_FIELDS: + if getattr(self, name) == _LATER_DEFAULTS[name]: + del d[name] + elif name in _RANGE_FIELDS: + d[name] = list(d[name]) + return d + + def draw(self, n_robots: dict[str, int], time_step: float) -> "SimDraw": + """One match's per-robot values, for `n_robots` robots of each colour (`COLOURS`).""" + rng = np.random.default_rng([self.seed, _DRAW_STREAM]) + lo, hi = self.latency_s + latency_ticks, kick_offset = {}, {} + for colour in COLOURS: # fixed order, and a fixed count of draws per robot + n = n_robots[colour] + lat = rng.uniform(lo, hi, size=n) + off = rng.normal(0.0, 1.0, size=n) * self.kick_speed_offset_sd + latency_ticks[colour] = tuple(int(round(x / time_step)) for x in lat) + kick_offset[colour] = tuple(float(x) for x in off) + + # The later settings come from their own stream: per match first, then per robot. + prng = np.random.default_rng([self.seed, _PHYSICS_STREAM]) + ball = {} + for name in ("ball_slide_decel", "ball_roll_decel"): + r = getattr(self, name) + ball[name] = float(prng.uniform(*r)) if r is not None else None + per_robot = {} + for name in ("kick_angle_offset", "max_speed", "max_accel", "max_brake"): + if name == "kick_angle_offset": + on = bool(self.kick_angle_offset_sd_deg) + else: + on = getattr(self, name) is not None + values = {} + for colour in COLOURS: + n = n_robots[colour] + if name == "kick_angle_offset": + x = prng.normal(0.0, 1.0, size=n) * self.kick_angle_offset_sd_deg + else: + x = prng.uniform(*(getattr(self, name) or (0.0, 0.0)), size=n) + values[colour] = tuple(float(v) for v in x) + per_robot[name] = values if on else None + return SimDraw(latency_ticks=latency_ticks, kick_offset=kick_offset, **ball, **per_robot) + + def vision_rng(self, tick: int) -> np.random.Generator: + """The vision noise for one tick: its own stream, so a tick's noise doesn't depend on how + many detections earlier ticks drew.""" + return np.random.default_rng([self.seed, _VISION_STREAM, tick]) + + def kick_rngs(self, n_robots: dict[str, int]) -> dict[str, list[np.random.Generator]]: + return { + colour: [np.random.default_rng([self.seed, _KICK_STREAM, c, i]) for i in range(n_robots[colour])] + for c, colour in enumerate(COLOURS) + } + + def kick_angle_rngs(self, n_robots: dict[str, int]) -> dict[str, list[np.random.Generator]]: + return { + colour: [np.random.default_rng([self.seed, _KICK_ANGLE_STREAM, c, i]) for i in range(n_robots[colour])] + for c, colour in enumerate(COLOURS) + } + + +_LATER_FIELDS = ( + "ball_roll_decel", + "ball_slide_decel", + "ball_slide_fraction", + "kick_angle_offset_sd_deg", + "kick_angle_sd_deg", + "max_speed", + "max_accel", + "max_brake", + "substeps", +) +_LATER_DEFAULTS = {f.name: f.default for f in dataclasses.fields(SimNoise) if f.name in _LATER_FIELDS} + + +@dataclass(frozen=True) +class SimDraw: + """One match's drawn per-robot values, by colour (`COLOURS`) then robot id.""" + + latency_ticks: dict[str, tuple[int, ...]] + kick_offset: dict[str, tuple[float, ...]] + # The later settings; None when off. + ball_slide_decel: Optional[float] = None + ball_roll_decel: Optional[float] = None + kick_angle_offset: Optional[dict[str, tuple[float, ...]]] = None # degrees + max_speed: Optional[dict[str, tuple[float, ...]]] = None + max_accel: Optional[dict[str, tuple[float, ...]]] = None + max_brake: Optional[dict[str, tuple[float, ...]]] = None + + def to_dict(self) -> dict: + d = { + "latency_ticks": {k: list(v) for k, v in self.latency_ticks.items()}, + "kick_offset": {k: list(v) for k, v in self.kick_offset.items()}, + } + for name in ("ball_slide_decel", "ball_roll_decel"): + if getattr(self, name) is not None: + d[name] = getattr(self, name) + for name in ("kick_angle_offset", "max_speed", "max_accel", "max_brake"): + v = getattr(self, name) + if v is not None: + d[name] = {k: list(x) for k, x in v.items()} + return d + + +class CommandDelay: + """Delays each robot's command by its own number of ticks. + + `push(action)` takes this tick's commands ({colour: [command per robot]}) and returns the + ones the sim should act on now: robot i's from `latency_ticks[colour][i]` ticks ago, or an + all-zero command (stand still, no kick) before that many ticks have passed. + """ + + def __init__(self, latency_ticks: dict[str, tuple[int, ...]]): + self._latency = latency_ticks + depth = max((t for ticks in latency_ticks.values() for t in ticks), default=0) + self._history: deque = deque(maxlen=depth + 1) + + def reset(self) -> None: + self._history.clear() + + def push(self, action: dict) -> dict: + self._history.append(action) + out = {} + for colour, commands in action.items(): + ticks = self._latency.get(colour, ()) + delayed = [] + for i, cmd in enumerate(commands): + lag = ticks[i] if i < len(ticks) else 0 + if lag < len(self._history): + delayed.append(self._history[-1 - lag][colour][i]) + else: + delayed.append(np.zeros_like(cmd)) + out[colour] = tuple(delayed) + return out + + +class KickSpeedNoise: + """Each kick's speed: nominal + the robot's offset + fresh noise, drawn when a kick command + starts and held while it lasts (the kicker fires once per command, however many ticks it is + held). Never below `min_speed`.""" + + def __init__( + self, + offsets: dict[str, tuple[float, ...]], + sd: float, + rngs: dict[str, list[np.random.Generator]], + min_speed: float = 0.5, + ): + self._offsets = offsets + self._sd = sd + self._rngs = rngs + self._min = min_speed + self._held: dict[tuple[str, int], Optional[float]] = {} + + def reset(self) -> None: + self._held.clear() + + def speed(self, colour: str, robot_id: int, kicking: bool, nominal: float) -> float: + key = (colour, robot_id) + if not kicking: + self._held[key] = None + return 0.0 + if self._held.get(key) is None: + noise = self._rngs[colour][robot_id].normal() * self._sd + self._held[key] = max(self._min, nominal + self._offsets[colour][robot_id] + noise) + return self._held[key] + + +class KickAngleNoise: + """Each kick's direction error (radians): the robot's offset + fresh noise, drawn when a kick + command starts and held while it lasts, like `KickSpeedNoise`.""" + + def __init__( + self, + offsets_deg: Optional[dict[str, tuple[float, ...]]], + sd_deg: float, + rngs: dict[str, list[np.random.Generator]], + ): + self._offsets = offsets_deg + self._sd = sd_deg + self._rngs = rngs + self._held: dict[tuple[str, int], Optional[float]] = {} + + def reset(self) -> None: + self._held.clear() + + def angle(self, colour: str, robot_id: int, kicking: bool) -> float: + key = (colour, robot_id) + if not kicking: + self._held[key] = None + return 0.0 + if self._held.get(key) is None: + offset = self._offsets[colour][robot_id] if self._offsets else 0.0 + noise = self._rngs[colour][robot_id].normal() * self._sd + self._held[key] = math.radians(offset + noise) + return self._held[key] + + +class MotionLimits: + """Per-robot hardware limits on the commands the sim acts on, below any planner. + + Each tick a robot's commanded velocity is brought within `max_accel * dt` (`max_brake * dt` when + the command lowers its speed) of its actual velocity, then within `max_speed`. Commands are in + the robot's own frame and velocities in the world frame, so both are compared in the world + frame using the robot's heading. + """ + + def __init__(self, draw: SimDraw, time_step: float): + self._speed = draw.max_speed + self._accel = draw.max_accel + self._brake = draw.max_brake + self._dt = time_step + + @staticmethod + def _value(values: Optional[dict[str, tuple[float, ...]]], colour: str, robot_id: int) -> float: + return values[colour][robot_id] if values else math.inf + + def apply(self, colour: str, robot_id: int, command, robot) -> None: + """Limits `command` (an rsim `Robot` command: local `v_x`, `v_y`) in place, given the robot's + actual state `robot` (world `v_x`, `v_y`, heading `theta` in degrees).""" + th = math.radians(robot.theta) + c, s = math.cos(th), math.sin(th) + gx = c * command.v_x - s * command.v_y + gy = s * command.v_x + c * command.v_y + ax, ay = robot.v_x, robot.v_y + dx, dy = gx - ax, gy - ay + braking = math.hypot(gx, gy) < math.hypot(ax, ay) + rate = self._value(self._brake if braking else self._accel, colour, robot_id) + change = math.hypot(dx, dy) + if change > rate * self._dt: + k = rate * self._dt / change + gx, gy = ax + dx * k, ay + dy * k + top = self._value(self._speed, colour, robot_id) + speed = math.hypot(gx, gy) + if speed > top: + gx, gy = gx * top / speed, gy * top / speed + command.v_x = c * gx + s * gy + command.v_y = -s * gx + c * gy diff --git a/utama_core/rsoccer_simulator/src/ssl/envs/standard_ssl.py b/utama_core/rsoccer_simulator/src/ssl/envs/standard_ssl.py index def12cc6..6088faaa 100644 --- a/utama_core/rsoccer_simulator/src/ssl/envs/standard_ssl.py +++ b/utama_core/rsoccer_simulator/src/ssl/envs/standard_ssl.py @@ -25,6 +25,13 @@ from utama_core.rsoccer_simulator.src.ssl.ssl_gym_base import SSLBaseEnv from utama_core.rsoccer_simulator.src.Utils import KDTree from utama_core.rsoccer_simulator.src.Utils.gaussian_noise import RsimGaussianNoise +from utama_core.rsoccer_simulator.src.Utils.sim_noise import ( + CommandDelay, + KickAngleNoise, + KickSpeedNoise, + MotionLimits, + SimNoise, +) logger = logging.getLogger(__name__) @@ -57,6 +64,9 @@ class SSLStandardEnv(SSLBaseEnv): Type: float Description: When running in rsim, cause robots and ball to vanish with the given probability. Defaults to 0. + sim_noise + Seeded latency, kick speed noise and vision noise (`SimNoise`). Replaces `gaussian_noise` + and `vanishing`, which draw from the unseeded global RNG. Defaults to None (off). Observation: Type: Tuple[FrameData, List[RobotInfo], List[RobotInfo]] @@ -88,6 +98,7 @@ def __init__( ball_starting_position: Optional[Tuple[float, float]] = None, gaussian_noise: RsimGaussianNoise = RsimGaussianNoise(), vanishing: float = 0, + sim_noise: Optional[SimNoise] = None, ): render_field_overrides = None if full_field_dims is not None: @@ -152,6 +163,45 @@ def __init__( assert vanishing >= 0, "Negative vanishing probability not allowed" self.vanishing = vanishing + # Seeded variation (`SimNoise`); None keeps the sim exactly as without it. + self.sim_noise = sim_noise + self.sim_draw = None + self._command_delay: Optional[CommandDelay] = None + self._kick_noise: Optional[KickSpeedNoise] = None + self._kick_angle: Optional[KickAngleNoise] = None + self._motion_limits: Optional[MotionLimits] = None + self._noise_rng = None # None: `gaussian_noise`/`vanishing` use the global RNG, as before + if sim_noise is not None: + assert gaussian_noise == RsimGaussianNoise() and not vanishing, "pass vision noise in sim_noise only" + n_robots = {"team_blue": self.n_robots_blue, "team_yellow": self.n_robots_yellow} + self.sim_draw = sim_noise.draw(n_robots, self.time_step) + self.gaussian_noise = sim_noise.vision + self.vanishing = sim_noise.vanishing + self._noise_rng = ( + sim_noise.vision_rng if sim_noise.vision != RsimGaussianNoise() or sim_noise.vanishing else None + ) + if any(t for ticks in self.sim_draw.latency_ticks.values() for t in ticks): + self._command_delay = CommandDelay(self.sim_draw.latency_ticks) + if sim_noise.kick_speed_offset_sd or sim_noise.kick_speed_sd: + self._kick_noise = KickSpeedNoise( + self.sim_draw.kick_offset, sim_noise.kick_speed_sd, sim_noise.kick_rngs(n_robots) + ) + if sim_noise.kick_angle_offset_sd_deg or sim_noise.kick_angle_sd_deg: + self._kick_angle = KickAngleNoise( + self.sim_draw.kick_angle_offset, sim_noise.kick_angle_sd_deg, sim_noise.kick_angle_rngs(n_robots) + ) + if sim_noise.motion_limits: + self._motion_limits = MotionLimits(self.sim_draw, self.time_step) + if sim_noise.native_physics: + roll = self.sim_draw.ball_roll_decel + slide = self.sim_draw.ball_slide_decel + self.rsim.set_physics_params( + substeps=sim_noise.substeps, + ball_slide_decel=(slide if slide is not None else roll) if roll is not None else -1.0, + ball_roll_decel=roll if roll is not None else -1.0, + ball_slide_fraction=sim_noise.ball_slide_fraction, + ) + def reset(self, *, seed=None, options=None): self.reward_shaping_total = None # Reset dribbler tracking state @@ -165,6 +215,12 @@ def reset(self, *, seed=None, options=None): self.kick_persist_yellow[i] = (0.0, 0) self.latest_observation = (-1, None) + if self._command_delay is not None: + self._command_delay.reset() + if self._kick_noise is not None: + self._kick_noise.reset() + if self._kick_angle is not None: + self._kick_angle.reset() return super().reset(seed=seed, options=options) def step(self, action): @@ -175,10 +231,14 @@ def step(self, action): """ # Increment step counter and build low-level simulator commands self.steps += 1 + if self._command_delay is not None: + action = self._command_delay.push(action) commands = self._get_commands(action) # Apply any implicit kicks caused by dribbler state transitions. self._apply_dribbler_release_kicks(commands) + if self._motion_limits is not None: + self._apply_motion_limits(commands) # Send command to simulator self.rsim.send_commands(commands) @@ -213,12 +273,17 @@ def _frame_to_observations( if self.latest_observation[0] == self.steps: return self.latest_observation[1] + # Seeded noise: a fresh stream per tick, so the same tick gets the same noise whatever + # happened earlier in the match. + self._frame_rng = self._noise_rng(self.steps) if self._noise_rng is not None else None + # Ball observation shared by all robots if self._vanishing(): ball_obs = [] else: - SSLStandardEnv._add_gaussian_noise_ball(self.frame.ball, self.gaussian_noise) - ball_obs = [RawBallData(self.frame.ball.x, -self.frame.ball.y, self.frame.ball.z, 1.0)] + dx, dy, _ = self._noise_offsets(with_heading=False) + ball = self.frame.ball # noise goes on the observation only, never on the sim's own frame + ball_obs = [RawBallData(ball.x + dx, -(ball.y + dy), ball.z, 1.0)] # Robots observation (Blue + Yellow) blue_obs = [] @@ -260,9 +325,9 @@ def _frame_to_observations( return result def _get_robot_observation(self, robot): - SSLStandardEnv._add_gaussian_noise_robot(robot, self.gaussian_noise) - - robot_pos = RawRobotData(robot.id, robot.x, -robot.y, -float(deg_to_rad(robot.theta)), 1) + dx, dy, dth = self._noise_offsets(with_heading=True) + theta = normalise_heading_deg(robot.theta + dth) if dth else robot.theta + robot_pos = RawRobotData(robot.id, robot.x + dx, -(robot.y + dy), -float(deg_to_rad(theta)), 1) robot_info = RobotResponse(robot.id, robot.infrared) return robot_pos, robot_info @@ -276,7 +341,8 @@ def _get_commands(self, actions) -> list[Robot]: v_theta = actions["team_blue"][i][2] dribbler = actions["team_blue"][i][4] > 0 - kick_v_x = RSIM_PARAMS.KICK_SPD if actions["team_blue"][i][3] > 0 else 0.0 + kick_v_x = self._kick_speed("team_blue", i, actions["team_blue"][i][3] > 0) + kick_angle = self._kick_direction("team_blue", i, actions["team_blue"][i][3] > 0) cmd = Robot( yellow=False, # Blue team @@ -285,6 +351,7 @@ def _get_commands(self, actions) -> list[Robot]: v_y=v_y, v_theta=v_theta, kick_v_x=kick_v_x, + kick_angle=kick_angle, dribbler=dribbler, ) commands.append(cmd) @@ -296,7 +363,8 @@ def _get_commands(self, actions) -> list[Robot]: v_theta = actions["team_yellow"][i][2] dribbler = actions["team_yellow"][i][4] > 0 - kick_v_x = RSIM_PARAMS.KICK_SPD if actions["team_yellow"][i][3] > 0 else 0.0 + kick_v_x = self._kick_speed("team_yellow", i, actions["team_yellow"][i][3] > 0) + kick_angle = self._kick_direction("team_yellow", i, actions["team_yellow"][i][3] > 0) cmd = Robot( yellow=True, # Yellow team @@ -305,12 +373,30 @@ def _get_commands(self, actions) -> list[Robot]: v_y=v_y, v_theta=v_theta, kick_v_x=kick_v_x, + kick_angle=kick_angle, dribbler=dribbler, ) commands.append(cmd) return commands + def _kick_direction(self, colour: str, robot_id: int, kicking: bool) -> float: + if self._kick_angle is not None: + return self._kick_angle.angle(colour, robot_id, kicking) + return 0.0 + + def _apply_motion_limits(self, commands: list[Robot]) -> None: + for cmd in commands: + colour, robots = ( + ("team_yellow", self.frame.robots_yellow) if cmd.yellow else ("team_blue", self.frame.robots_blue) + ) + self._motion_limits.apply(colour, cmd.id, cmd, robots[cmd.id]) + + def _kick_speed(self, colour: str, robot_id: int, kicking: bool) -> float: + if self._kick_noise is not None: + return self._kick_noise.speed(colour, robot_id, kicking, RSIM_PARAMS.KICK_SPD) + return RSIM_PARAMS.KICK_SPD if kicking else 0.0 + def _apply_dribbler_release_kicks(self, commands: list[Robot]) -> None: """Approximate kicks when dribblers turn off for robots that had possession. @@ -465,43 +551,20 @@ def in_gk_area(obj): def _vanishing(self) -> bool: """Determines whether a frame should vanish. Only runs after Game Gater is passed""" - return self.steps > 0 and self.vanishing and (random.random() < self.vanishing) - - @staticmethod - def _add_gaussian_noise_ball(ball: Ball, noise: RsimGaussianNoise): - """ - When running in rsim, add Gaussian noise to ball with the given standard deviations. - Mutates the Robot object in place. - - Args: - noise (RsimGaussianNoise): The 3 parameters are for x (in m), y (in m), and orientation (in degrees) respectively. - Defaults to 0 for each. - """ - - if noise.x_stddev: - ball.x += normal(scale=noise.x_stddev) - - if noise.y_stddev: - ball.y += normal(scale=noise.y_stddev) - - # No noise addition for z, since rSim is 2-D - - @staticmethod - def _add_gaussian_noise_robot(robot: Robot, noise: RsimGaussianNoise): - """ - When running in rsim, add Gaussian noise to robot with the given standard deviations. - Mutates the Robot object in place. - - Args: - noise (RsimGaussianNoise): The 3 parameters are for x (in m), y (in m), and orientation (in degrees) respectively. - Defaults to 0 for each. - """ - - if noise.x_stddev: - robot.x += normal(scale=noise.x_stddev) - - if noise.y_stddev: - robot.y += normal(scale=noise.y_stddev) - - if noise.th_stddev_deg: - robot.theta = normalise_heading_deg(robot.theta + normal(scale=noise.th_stddev_deg)) + if not (self.steps > 0 and self.vanishing): + return False + u = self._frame_rng.random() if self._frame_rng is not None else random.random() + return u < self.vanishing + + def _noise_offsets(self, with_heading: bool) -> Tuple[float, float, float]: + """Gaussian vision noise for one detection: x and y in m (sim frame), heading in degrees. + Returned rather than added to the sim's frame: the frame is the sim's truth, which + teleports write back into the physics. No noise for z, since rSim is 2-D.""" + noise = self.gaussian_noise + if self._frame_rng is not None: # a fixed number of draws per detection + z = self._frame_rng.normal(size=3 if with_heading else 2) + return z[0] * noise.x_stddev, z[1] * noise.y_stddev, (z[2] * noise.th_stddev_deg if with_heading else 0.0) + dx = normal(scale=noise.x_stddev) if noise.x_stddev else 0.0 + dy = normal(scale=noise.y_stddev) if noise.y_stddev else 0.0 + dth = normal(scale=noise.th_stddev_deg) if with_heading and noise.th_stddev_deg else 0.0 + return dx, dy, dth diff --git a/utama_core/run/live_status.py b/utama_core/run/live_status.py new file mode 100644 index 00000000..e4a7be7d --- /dev/null +++ b/utama_core/run/live_status.py @@ -0,0 +1,72 @@ +"""Live terminal status panel for `StrategyRunner`, mixed in as `LiveStatusMixin`: one rich `Live` +line with FPS, referee command, stage, score and time left, refreshed every `FPS_PRINT_INTERVAL` seconds.""" + +import time + +from rich.text import Text + +from utama_core.config.settings import FPS_PRINT_INTERVAL +from utama_core.custom_referee import CustomReferee +from utama_core.run.referee_source import OfficialReferee + + +class LiveStatusMixin: + """`StrategyRunner` methods for the live status panel; they read runner state set in `__init__`.""" + + def _stop_fps_live(self): + if self._fps_live: + self._fps_live.stop() + self._fps_live = None + + def _update_live_status(self, frame_start: float) -> None: + """Count this frame and, once per `FPS_PRINT_INTERVAL`, refresh the live status line. + + Called at the end of `_run_step` only when `show_live_status` is set. + """ + frame_end = time.perf_counter() + frame_dt = frame_end - frame_start + + self.elapsed_time += frame_dt + self.num_frames_elapsed += 1 + + if self.elapsed_time >= FPS_PRINT_INTERVAL: + fps = self.num_frames_elapsed / self.elapsed_time + + ref = self.referee_refiner + stage_secs = ref.stage_time_left + stage_min = int(stage_secs // 60) + stage_sec = int(stage_secs % 60) + display = Text() + display.append(f"FPS: {fps:.1f}", style="bold cyan") + display.append(" | ") + display.append(ref.last_command.name, style="bold yellow") + if ref.last_next_command: + display.append(" -> ") + display.append(ref.last_next_command.name, style="yellow") + display.append(" | ") + display.append(ref.stage.name.replace("_", " ").title()) + display.append(" | Blue ") + display.append(str(ref.blue_team.score), style="bold blue") + display.append(" - ") + display.append(str(ref.yellow_team.score), style="bold yellow") + display.append(" Yellow") + display.append(f" | {stage_min}:{stage_sec:02d} left") + display.append(" | Ref: ") + if isinstance(self.referee, CustomReferee): + display.append("custom", style="bold magenta") + display.append(" (") + display.append(self.referee.profile_name, style="magenta") + display.append(")") + elif isinstance(self.referee, OfficialReferee): + display.append("official", style="bold magenta") + else: + display.append("none", style="bold magenta") + + if ref.last_status_message: + display.append(f" | {ref.last_status_message}", style="dim") + + self._fps_live.update(display) + self._fps_live.refresh() + + self.elapsed_time = 0.0 + self.num_frames_elapsed = 0 diff --git a/utama_core/run/side_runtime.py b/utama_core/run/side_runtime.py new file mode 100644 index 00000000..e768ca7b --- /dev/null +++ b/utama_core/run/side_runtime.py @@ -0,0 +1,73 @@ +"""Per-side runtime state (`SideRuntime`) and the frame transforms used when the teams change +ends (`_turned`) or when the opponent's view of a frame is needed in sim PVP (`_mirrored`).""" + +import dataclasses +import math +from dataclasses import dataclass, field +from typing import Optional + +from utama_core.data_processing.refiners import ( + PositionRefiner, + RobotInfoRefiner, + VelocityRefiner, +) +from utama_core.engine.abstract_strategy import AbstractStrategy +from utama_core.entities.data.vector import Vector2D +from utama_core.entities.game import Game, GameFrame, GameHistory, Robot +from utama_core.entities.game.field import FieldBounds +from utama_core.motion_planning.src.common.motion_controller import MotionController + + +def _turned(robot: Robot, bounds: FieldBounds) -> Robot: + """`robot` half a turn about the centre spot, at rest: where it stands after changing ends. + Kept within `bounds`: a robot in the run-off past a line comes back onto it, since the sim + refuses to teleport a robot outside the field (a match crashed at half-time that way).""" + (left, top), (right, bottom) = bounds.top_left, bounds.bottom_right + return dataclasses.replace( + robot, + p=Vector2D(min(max(-robot.p.x, left), right), min(max(-robot.p.y, bottom), top)), + v=Vector2D(0.0, 0.0), + a=Vector2D(0.0, 0.0), + orientation=math.remainder(robot.orientation + math.pi, 2 * math.pi), + ) + + +def _mirrored(frame: GameFrame) -> GameFrame: + """`frame` as the other team sees it: the same absolute coordinates, friendly and enemy swapped.""" + + def flip(robots: dict[int, Robot]) -> dict[int, Robot]: + return {i: Robot(r.id, not r.is_friendly, r.has_ball, r.p, r.v, r.a, r.orientation) for i, r in robots.items()} + + return GameFrame( + frame.ts, + not frame.my_team_is_yellow, + not frame.my_team_is_right, + flip(frame.enemy_robots), + flip(frame.friendly_robots), + frame.ball, + frame.referee, + ) + + +@dataclass(slots=True) +class SideRuntime: + """Encapsulates all per-side (my team / opponent) runtime state. + + Args: + strategy (AbstractStrategy): The strategy for this side. + position_refiner (PositionRefiner): Position refiner for this side. + velocity_refiner (VelocityRefiner): Velocity refiner for this side. + robot_info_refiner (RobotInfoRefiner): Robot info refiner for this side. + motion_controller (type[MotionController]): Motion controller factory for this side. + """ + + strategy: AbstractStrategy + position_refiner: PositionRefiner + velocity_refiner: VelocityRefiner + robot_info_refiner: RobotInfoRefiner + motion_controller: type[MotionController] + control_scheme: str = "fpp" + + game: Optional[Game] = field(init=False, default=None) + game_history: Optional[GameHistory] = field(init=False, default=None) + current_game_frame: Optional[GameFrame] = field(init=False, default=None) diff --git a/utama_core/run/sim_recovery.py b/utama_core/run/sim_recovery.py new file mode 100644 index 00000000..a3d9c729 --- /dev/null +++ b/utama_core/run/sim_recovery.py @@ -0,0 +1,213 @@ +"""Sim-only recovery from quirks of the native rsim engine, mixed into `StrategyRunner` +(`SimRecoveryMixin`): settling a teleported ball, holding a placement teleport off robots, and the +frozen-field watchdog. Each constant's rationale is in its comment below and on the method it serves.""" + +import math + +from utama_core.config.enums import Mode +from utama_core.config.physical_constants import BALL_RADIUS, ROBOT_RADIUS +from utama_core.entities.referee.referee_command import RefereeCommand + +# sim-only: `sim_controller.teleport_ball()` (used to instantly simulate a +# ball placement/free-kick reposition, since robots cannot physically +# retrieve an out-of-bounds ball in simulation) is meant to place the ball at +# rest at the designated position. Traced live in a tournament run +# (replays/tournament_20260905_002133/counter_flow_vs_zone_fluid_LK, 2026-09-05): +# the underlying native reset (`SSLWorld::replace()`) does correctly zero the +# ball's ODE body velocity at the instant of reset, but the very next physics +# step afterward produces a large spurious velocity (tens of m/s observed) — +# a contact-resolution artifact, not something teleport_ball's caller +# controls. That single spike is enough to send the ball rolling metres away +# from the placement target before it settles, so +# `GameStateMachine._ball_placement_done()` (gated purely on ball-to-target +# distance) never sees the ball close enough to auto-advance the restart — +# the match then sits in BALL_PLACEMENT_*/DIRECT_FREE_* for the rest of the +# match (RESTART_STALL in MatchStats.stall_events). Re-pinning the ball to +# the same target for a few ticks after every teleport absorbs that one-tick +# transient before it can accumulate into real displacement, without needing +# a fix in the vendored native engine. +_TELEPORT_SETTLE_TICKS = 3 +# Ball speed (m/s) below which the settle window is allowed to close -- +# i.e. the ball is considered genuinely at rest, not merely past the initial +# reset-artifact transient. The native engine's post-teleport spike (tens of +# m/s, see `_TELEPORT_SETTLE_TICKS`'s docstring) decays *through* a range of +# still-real, few-m/s speeds on its way to zero, and releasing the re-pin as +# soon as it first dips low is still enough for the ball to coast meters away +# over the following seconds before `_ball_placement_done()`'s distance gate +# can catch it -- a rolling SSL ball loses speed very slowly. Confirmed live, +# 2026-09-12: a teleport spike of ~13 m/s decayed to 0.83 m/s two ticks +# later (an earlier version of this constant released the window at any +# speed under 3.0 m/s, right where this case landed), and the ball then +# drifted, still slowing but never stopping, for the next ~14 seconds and +# ~2.5m before wedging against the field boundary in a corner -- the actual +# mechanism behind every "double-axis corner overshoot" RESTART_STALL traced +# this session (e.g. overload_flow_vs_tiki_taka's BALL_PLACEMENT_BLUE at +# t=24.2s), not a genuinely unreachable placement target. 0.05 m/s is +# comfortably above rsim's steady-state numerical noise floor (~1e-3 m/s +# once actually at rest, per _SIM_FROZEN_FIELD_VELOCITY_TOL_MPS's docstring) +# while still well below any speed that could carry the ball a visible +# distance before the next tick's re-pin. +_TELEPORT_SETTLE_SPEED_MPS = 0.05 +# Cap on how many times the settle window can be extended by a detected +# spike, so a genuinely fast in-flight ball at the moment of a teleport +# (rather than the reset artifact) can't stall the re-pin loop indefinitely. +_TELEPORT_SETTLE_MAX_EXTENSIONS = 10 +# sim-only: a BALL_PLACEMENT_* teleport is held back while any robot stands +# within this distance of the placement target. Teleporting the ball into a +# robot's body gets it shoved out by contact resolution faster than the +# settle re-pin can hold it: traced in +# tournament_20260924_082230/give_and_go_solo_vs_high_line_zone (t=37.1), +# where the ball landed 0.07m from a blue robot, was pushed off at ~1.5m/s +# and rolled into the goal net behind the line -- unreachable without +# entering the defense area, so DIRECT_FREE_YELLOW stalled for the rest of +# the match. The non-placing team clears the placement area during +# BALL_PLACEMENT_*, so the teleport fires as soon as the spot is free. +_PLACEMENT_TELEPORT_CLEARANCE_M = ROBOT_RADIUS + BALL_RADIUS + 0.02 + +# sim-only: recovery for a rare native-engine hang, distinct from the +# post-teleport velocity spike above. Traced live (tournament_20260905_083358, +# tiki_taka_vs_zone_fluid_Lk, 189s RESTART_STALL during DIRECT_FREE_BLUE): +# immediately after a mid-match `teleport_ball`-triggered `RSim.reset()` +# (from a BALL_PLACEMENT_BLUE placement), every robot AND the ball froze +# bit-for-bit (position std ~1e-13m, velocity exactly 0.0) for the rest of +# the match — the vendored native `robosim` subprocess kept returning valid +# JSON state each `step()` (no exception, no protocol desync), but the +# physics itself silently stopped advancing. Application-level command +# computation was verified correct and unrelated: the kicker's tactic, PID, +# and rotation math all produced real, correctly-directed, nonzero commands +# every tick throughout the stall — the freeze is entirely on the native +# engine's side, downstream of everything this codebase controls. Not +# reproducible live from any cold-teleported starting point (tried both +# mid-stall and pre-foul), so it can't be root-caused or fixed at the +# source — only detected and recovered from, the same trade-off already +# made for `_SIM_HALT_AUTO_RESUME_SECONDS` (no human GC in a sim tournament +# to intervene). If literally every robot and the ball report zero velocity +# for this long during live play (`current_command` neither paused nor mid- +# restart — those already legitimately hold everything still), force a +# fresh `teleport_ball` to the ball's own current position: `teleport_ball` +# always resets the *entire* frame (see `SSLBaseEnv.teleport_ball`'s +# docstring), not just the ball, so this is the cheapest way to re-issue a +# full-frame `RSim.reset()` and give the native engine another chance to +# resume advancing, without changing anyone's actual position. +_SIM_FROZEN_FIELD_AUTO_RECOVER_SECONDS = 8.0 +# Exactly 0.0, not a small tolerance: ordinary rsim noise/refiner jitter +# never lands two consecutive ticks at bit-identical velocity, so this can +# only fire on the genuine native-engine freeze this exists to catch, never +# on a robot/ball that is merely moving slowly. +_SIM_FROZEN_FIELD_VELOCITY_TOL_MPS = 0.0 + + +class SimRecoveryMixin: + """`StrategyRunner` methods for the sim-only recovery above; they read and write runner state set in `__init__`.""" + + def _teleport_ball_and_settle(self, x: float, y: float) -> None: + """`sim_controller.teleport_ball(x, y)`, plus arming the re-pin window + `_run_step` uses to counter the native engine's post-reset velocity + spike (see `_TELEPORT_SETTLE_TICKS`'s module docstring). + """ + self.sim_controller.teleport_ball(x, y) + self._teleport_settle_target = (x, y) + self._teleport_settle_ticks_left = _TELEPORT_SETTLE_TICKS + self._teleport_settle_extensions_left = _TELEPORT_SETTLE_MAX_EXTENSIONS + + def _tick_pending_placement_teleport(self) -> None: + """Fire the pending BALL_PLACEMENT_* teleport once no robot is within + `_PLACEMENT_TELEPORT_CLEARANCE_M` of the target (see its docstring).""" + if self._pending_placement_teleport is None: + return + x, y = self._pending_placement_teleport + frame = self.my.current_game_frame + robots = (*frame.friendly_robots.values(), *frame.enemy_robots.values()) + if any(math.hypot(r.p.x - x, r.p.y - y) < _PLACEMENT_TELEPORT_CLEARANCE_M for r in robots): + return + self._pending_placement_teleport = None + self._teleport_ball_and_settle(x, y) + + def _tick_teleport_settle(self) -> None: + """One tick of the re-pin window `_teleport_ball_and_settle` arms. + + Traced live in a tournament run (see `_TELEPORT_SETTLE_TICKS`'s + docstring): the native rsim engine's post-teleport velocity spike + doesn't decay with repeated resets — it simply waits for the first + real physics step *without* a reset immediately before it, however + many resets preceded it. So this doesn't just re-pin for a fixed + number of ticks: it keeps extending the window (bounded by + `_TELEPORT_SETTLE_MAX_EXTENSIONS`) for as long as the ball's most + recent velocity still reads as unsettled, and only stops once a tick + is observed with the window closed AND the ball has actually settled + (`_TELEPORT_SETTLE_SPEED_MPS`'s docstring has the full rationale for + why this needs to be a strict "at rest" check, not just "no longer + looks like the initial reset spike"). + + Reads the ball's velocity via `self.rsim_env.frame.ball` (the raw sim + state, refreshed every physics `step()`), not `current_game_frame` — + the latter is the *refined* frame `_step_game` builds once per tick, + so at the point this runs (after the referee block, before + `_step_game`) it is always one tick stale and would see last tick's + already-corrected velocity instead of this tick's live spike. + """ + if self.sim_controller is None or self._teleport_settle_target is None: + return + raw_ball = self.rsim_env.frame.ball if self.mode == Mode.RSIM and self.rsim_env is not None else None + ball_speed = math.hypot(raw_ball.v_x, raw_ball.v_y) if raw_ball is not None else 0.0 + settled = ball_speed <= _TELEPORT_SETTLE_SPEED_MPS + # The tick whose re-pin drove `_teleport_settle_ticks_left` to 0 + # still needs one more check here — its own `step()` (this tick, + # further down in `_run_step`) is what actually produces or clears + # the spike, and that only becomes observable next tick. So don't + # drop `_teleport_settle_target` (and stop checking) until a tick is + # seen with the window closed AND (settled OR out of extensions — + # a ball that never actually settles must still terminate the loop + # eventually rather than re-pin forever). + if self._teleport_settle_ticks_left <= 0 and (settled or self._teleport_settle_extensions_left <= 0): + self._teleport_settle_target = None + return + if not settled and self._teleport_settle_extensions_left > 0: + self._teleport_settle_ticks_left += 1 + self._teleport_settle_extensions_left -= 1 + x, y = self._teleport_settle_target + self.sim_controller.teleport_ball(x, y) + self._teleport_settle_ticks_left = max(0, self._teleport_settle_ticks_left - 1) + + def _tick_frozen_field_watchdog(self) -> None: + """Recover from the rare native-engine hang described in + `_SIM_FROZEN_FIELD_AUTO_RECOVER_SECONDS`'s module docstring. + + Runs once per tick, after `_step_game` has refreshed + `self.my.current_game_frame` for this tick. Only meaningful in sim + (`Mode.HALT` has its own dedicated auto-resume timer above, and a + real match has no `sim_controller` to recover through — `HALT` is + additionally excluded on purpose, since it's the one live-play state + where every robot legitimately holds zero velocity by design). + """ + if self.sim_controller is None or self.mode != Mode.RSIM: + return + frame = self.my.current_game_frame + if frame is None or frame.ball is None: + self._frozen_field_since = None + return + referee = getattr(frame, "referee", None) + current_command = getattr(referee, "referee_command", None) if referee is not None else None + if current_command == RefereeCommand.HALT: + self._frozen_field_since = None + return + + tol = _SIM_FROZEN_FIELD_VELOCITY_TOL_MPS + all_still = frame.ball.v.mag() <= tol and all( + robot.v.mag() <= tol for robot in (*frame.friendly_robots.values(), *frame.enemy_robots.values()) + ) + if not all_still: + self._frozen_field_since = None + return + + if self._frozen_field_since is None: + self._frozen_field_since = frame.ts + elif frame.ts - self._frozen_field_since >= _SIM_FROZEN_FIELD_AUTO_RECOVER_SECONDS: + self.logger.warning( + "Frozen-field watchdog: every robot and the ball reported exactly zero " + "velocity for %.1fs (native-engine hang, see _SIM_FROZEN_FIELD_AUTO_RECOVER_SECONDS) " + "— forcing a full-frame reset via teleport_ball to recover.", + frame.ts - self._frozen_field_since, + ) + self.sim_controller.teleport_ball(frame.ball.p.x, frame.ball.p.y) + self._frozen_field_since = None diff --git a/utama_core/run/strategy_runner.py b/utama_core/run/strategy_runner.py index 5696a9f2..211a0167 100644 --- a/utama_core/run/strategy_runner.py +++ b/utama_core/run/strategy_runner.py @@ -1,29 +1,22 @@ import cProfile import dataclasses +import json import logging -import math import signal import threading import time import warnings from collections import deque -from dataclasses import dataclass, field +from dataclasses import dataclass from typing import TYPE_CHECKING, FrozenSet, List, Optional, Tuple, Union from rich.live import Live -from rich.text import Text from utama_core.config.enums import Mode, mode_str_to_enum from utama_core.config.field_params import STANDARD_FIELD_DIMS, FieldDimensions from utama_core.config.formations import FormationType, get_formations -from utama_core.config.physical_constants import ( - BALL_RADIUS, - MAX_ROBOT_ID, - MAX_ROBOTS, - ROBOT_RADIUS, -) +from utama_core.config.physical_constants import MAX_ROBOT_ID, MAX_ROBOTS from utama_core.config.settings import ( - FPS_PRINT_INTERVAL, MAX_CAMERAS, MAX_GAME_HISTORY, ROBOT_FEEDBACK_CONNECTION_TIMEOUT_SECONDS, @@ -42,8 +35,7 @@ from utama_core.engine.match_stats import MatchStatsAccumulator from utama_core.entities.data.command import RobotCommand, RobotResponse from utama_core.entities.data.raw_vision import RawVisionData -from utama_core.entities.data.vector import Vector2D -from utama_core.entities.game import Game, GameFrame, GameHistory, Robot +from utama_core.entities.game import Game, GameFrame, GameHistory from utama_core.entities.game.field import Field, FieldBounds from utama_core.entities.referee.referee_command import RefereeCommand from utama_core.global_utils.mapping_utils import ( @@ -53,7 +45,6 @@ ) from utama_core.global_utils.math_utils import assert_valid_bounding_box from utama_core.motion_planning.src.common.control_schemes import get_control_scheme -from utama_core.motion_planning.src.common.motion_controller import MotionController from utama_core.replay.columnar_writer import ( ColumnarReplayWriter, ColumnarReplayWriterConfig, @@ -61,9 +52,20 @@ from utama_core.replay.replay_writer import ReplayWriter, ReplayWriterConfig from utama_core.rsoccer_simulator.src.ssl.envs import SSLStandardEnv from utama_core.rsoccer_simulator.src.Utils.gaussian_noise import RsimGaussianNoise +from utama_core.rsoccer_simulator.src.Utils.sim_noise import SimNoise from utama_core.run import GameGater +from utama_core.run.live_status import LiveStatusMixin from utama_core.run.referee_source import OfficialReferee, RefereeSource +from utama_core.run.side_runtime import SideRuntime, _mirrored, _turned +from utama_core.run.sim_recovery import _PLACEMENT_TELEPORT_CLEARANCE_M # noqa: F401 +from utama_core.run.sim_recovery import _TELEPORT_SETTLE_MAX_EXTENSIONS # noqa: F401 +from utama_core.run.sim_recovery import _TELEPORT_SETTLE_TICKS # noqa: F401 +from utama_core.run.sim_recovery import ( # noqa: F401; noqa: F401 (re-exported for tests) + _SIM_FROZEN_FIELD_AUTO_RECOVER_SECONDS, + SimRecoveryMixin, +) from utama_core.run.vision_stream import GameFrameRenderer, RSimVisionStreamServer +from utama_core.run.vision_stream_status import VisionStreamStatusMixin from utama_core.shared.pass_and_score_geometry import reset_possession_state from utama_core.skills.src.shielding import reset_shield_state from utama_core.team_controller.src.controllers import ( @@ -84,7 +86,6 @@ from utama_core.entities.data.referee import RefereeData _GEOMETRY_MATCH_TOLERANCE_M = 0.001 # mm-precision integers from vision → 1 mm tolerance -_VS_KICK_THRESHOLD = 0.5 # m/s — ball speed above this triggers kick commentary _SIM_HALT_AUTO_RESUME_SECONDS = 5.0 # sim-only: no human GC to resume a HALT, see below # sim-only: a second grace window after a HALT auto-resumes into STOP, # before forcing NORMAL_START — gives StopStep a few ticks to actively clear @@ -98,93 +99,6 @@ # window closed), so this carries real margin rather than the bare minimum. _SIM_HALT_RESUME_CLEAR_SECONDS = 4.0 -# sim-only: `sim_controller.teleport_ball()` (used to instantly simulate a -# ball placement/free-kick reposition, since robots cannot physically -# retrieve an out-of-bounds ball in simulation) is meant to place the ball at -# rest at the designated position. Traced live in a tournament run -# (replays/tournament_20260905_002133/counter_flow_vs_zone_fluid_LK, 2026-09-05): -# the underlying native reset (`SSLWorld::replace()`) does correctly zero the -# ball's ODE body velocity at the instant of reset, but the very next physics -# step afterward produces a large spurious velocity (tens of m/s observed) — -# a contact-resolution artifact, not something teleport_ball's caller -# controls. That single spike is enough to send the ball rolling metres away -# from the placement target before it settles, so -# `GameStateMachine._ball_placement_done()` (gated purely on ball-to-target -# distance) never sees the ball close enough to auto-advance the restart — -# the match then sits in BALL_PLACEMENT_*/DIRECT_FREE_* for the rest of the -# match (RESTART_STALL in MatchStats.stall_events). Re-pinning the ball to -# the same target for a few ticks after every teleport absorbs that one-tick -# transient before it can accumulate into real displacement, without needing -# a fix in the vendored native engine. -_TELEPORT_SETTLE_TICKS = 3 -# Ball speed (m/s) below which the settle window is allowed to close -- -# i.e. the ball is considered genuinely at rest, not merely past the initial -# reset-artifact transient. The native engine's post-teleport spike (tens of -# m/s, see `_TELEPORT_SETTLE_TICKS`'s docstring) decays *through* a range of -# still-real, few-m/s speeds on its way to zero, and releasing the re-pin as -# soon as it first dips low is still enough for the ball to coast meters away -# over the following seconds before `_ball_placement_done()`'s distance gate -# can catch it -- a rolling SSL ball loses speed very slowly. Confirmed live, -# 2026-09-12: a teleport spike of ~13 m/s decayed to 0.83 m/s two ticks -# later (an earlier version of this constant released the window at any -# speed under 3.0 m/s, right where this case landed), and the ball then -# drifted, still slowing but never stopping, for the next ~14 seconds and -# ~2.5m before wedging against the field boundary in a corner -- the actual -# mechanism behind every "double-axis corner overshoot" RESTART_STALL traced -# this session (e.g. overload_flow_vs_tiki_taka's BALL_PLACEMENT_BLUE at -# t=24.2s), not a genuinely unreachable placement target. 0.05 m/s is -# comfortably above rsim's steady-state numerical noise floor (~1e-3 m/s -# once actually at rest, per _SIM_FROZEN_FIELD_VELOCITY_TOL_MPS's docstring) -# while still well below any speed that could carry the ball a visible -# distance before the next tick's re-pin. -_TELEPORT_SETTLE_SPEED_MPS = 0.05 -# Cap on how many times the settle window can be extended by a detected -# spike, so a genuinely fast in-flight ball at the moment of a teleport -# (rather than the reset artifact) can't stall the re-pin loop indefinitely. -_TELEPORT_SETTLE_MAX_EXTENSIONS = 10 -# sim-only: a BALL_PLACEMENT_* teleport is held back while any robot stands -# within this distance of the placement target. Teleporting the ball into a -# robot's body gets it shoved out by contact resolution faster than the -# settle re-pin can hold it: traced in -# tournament_20260924_082230/give_and_go_solo_vs_high_line_zone (t=37.1), -# where the ball landed 0.07m from a blue robot, was pushed off at ~1.5m/s -# and rolled into the goal net behind the line -- unreachable without -# entering the defense area, so DIRECT_FREE_YELLOW stalled for the rest of -# the match. The non-placing team clears the placement area during -# BALL_PLACEMENT_*, so the teleport fires as soon as the spot is free. -_PLACEMENT_TELEPORT_CLEARANCE_M = ROBOT_RADIUS + BALL_RADIUS + 0.02 - -# sim-only: recovery for a rare native-engine hang, distinct from the -# post-teleport velocity spike above. Traced live (tournament_20260905_083358, -# tiki_taka_vs_zone_fluid_Lk, 189s RESTART_STALL during DIRECT_FREE_BLUE): -# immediately after a mid-match `teleport_ball`-triggered `RSim.reset()` -# (from a BALL_PLACEMENT_BLUE placement), every robot AND the ball froze -# bit-for-bit (position std ~1e-13m, velocity exactly 0.0) for the rest of -# the match — the vendored native `robosim` subprocess kept returning valid -# JSON state each `step()` (no exception, no protocol desync), but the -# physics itself silently stopped advancing. Application-level command -# computation was verified correct and unrelated: the kicker's tactic, PID, -# and rotation math all produced real, correctly-directed, nonzero commands -# every tick throughout the stall — the freeze is entirely on the native -# engine's side, downstream of everything this codebase controls. Not -# reproducible live from any cold-teleported starting point (tried both -# mid-stall and pre-foul), so it can't be root-caused or fixed at the -# source — only detected and recovered from, the same trade-off already -# made for `_SIM_HALT_AUTO_RESUME_SECONDS` (no human GC in a sim tournament -# to intervene). If literally every robot and the ball report zero velocity -# for this long during live play (`current_command` neither paused nor mid- -# restart — those already legitimately hold everything still), force a -# fresh `teleport_ball` to the ball's own current position: `teleport_ball` -# always resets the *entire* frame (see `SSLBaseEnv.teleport_ball`'s -# docstring), not just the ball, so this is the cheapest way to re-issue a -# full-frame `RSim.reset()` and give the native engine another chance to -# resume advancing, without changing anyone's actual position. -_SIM_FROZEN_FIELD_AUTO_RECOVER_SECONDS = 8.0 -# Exactly 0.0, not a small tolerance: ordinary rsim noise/refiner jitter -# never lands two consecutive ticks at bit-identical velocity, so this can -# only fire on the genuine native-engine freeze this exists to catch, never -# on a robot/ball that is merely moving slowly. -_SIM_FROZEN_FIELD_VELOCITY_TOL_MPS = 0.0 logging.basicConfig( filename="Utama.log", @@ -260,62 +174,7 @@ def _build_robot_feedback_snapshot( return snapshot -def _turned(robot: Robot, bounds: FieldBounds) -> Robot: - """`robot` half a turn about the centre spot, at rest: where it stands after changing ends. - Kept within `bounds`: a robot in the run-off past a line comes back onto it, since the sim - refuses to teleport a robot outside the field (a match crashed at half-time that way).""" - (left, top), (right, bottom) = bounds.top_left, bounds.bottom_right - return dataclasses.replace( - robot, - p=Vector2D(min(max(-robot.p.x, left), right), min(max(-robot.p.y, bottom), top)), - v=Vector2D(0.0, 0.0), - a=Vector2D(0.0, 0.0), - orientation=math.remainder(robot.orientation + math.pi, 2 * math.pi), - ) - - -def _mirrored(frame: GameFrame) -> GameFrame: - """`frame` as the other team sees it: the same absolute coordinates, friendly and enemy swapped.""" - - def flip(robots: dict[int, Robot]) -> dict[int, Robot]: - return {i: Robot(r.id, not r.is_friendly, r.has_ball, r.p, r.v, r.a, r.orientation) for i, r in robots.items()} - - return GameFrame( - frame.ts, - not frame.my_team_is_yellow, - not frame.my_team_is_right, - flip(frame.enemy_robots), - flip(frame.friendly_robots), - frame.ball, - frame.referee, - ) - - -@dataclass(slots=True) -class SideRuntime: - """Encapsulates all per-side (my team / opponent) runtime state. - - Args: - strategy (AbstractStrategy): The strategy for this side. - position_refiner (PositionRefiner): Position refiner for this side. - velocity_refiner (VelocityRefiner): Velocity refiner for this side. - robot_info_refiner (RobotInfoRefiner): Robot info refiner for this side. - motion_controller (type[MotionController]): Motion controller factory for this side. - """ - - strategy: AbstractStrategy - position_refiner: PositionRefiner - velocity_refiner: VelocityRefiner - robot_info_refiner: RobotInfoRefiner - motion_controller: type[MotionController] - control_scheme: str = "fpp" - - game: Optional[Game] = field(init=False, default=None) - game_history: Optional[GameHistory] = field(init=False, default=None) - current_game_frame: Optional[GameFrame] = field(init=False, default=None) - - -class StrategyRunner: +class StrategyRunner(SimRecoveryMixin, VisionStreamStatusMixin, LiveStatusMixin): """Main class to run the robot controller and strategy. Args: @@ -362,6 +221,10 @@ class StrategyRunner: Defaults to 0 for each. rsim_vanishing (float, optional): When running in rsim, cause robots and ball to vanish with the given probability. Defaults to 0. + sim_noise (SimNoise, optional): When running in rsim, seeded latency, kick and ball physics, motion limits + and vision noise + (`utama_core/rsoccer_simulator/src/Utils/sim_noise.py`); replaces `rsim_noise` and `rsim_vanishing`. + Recorded in a columnar replay. Defaults to None (off). filtering (bool, optional): Turn on Kalman smoothing and vanished-robot imputation. Defaults to True. referee (RefereeSource, optional): Referee source. Pass a ``CustomReferee`` instance to use the in-process referee, ``OfficialReferee()`` to consume @@ -409,6 +272,7 @@ def __init__( stats_path: Optional[str] = None, rsim_noise: RsimGaussianNoise = RsimGaussianNoise(), rsim_vanishing: float = 0, + sim_noise: Optional[SimNoise] = None, filtering: bool = True, referee: RefereeSource = None, referee_initial_command: Optional[RefereeCommand] = None, @@ -522,7 +386,7 @@ def __init__( ### functions below rely on self.my and self.opp ### - self.rsim_env, self.sim_controller = self._load_sim(rsim_noise, rsim_vanishing) + self.rsim_env, self.sim_controller = self._load_sim(rsim_noise, rsim_vanishing, sim_noise) self._assert_exp_robots_and_ball(exp_friendly, exp_enemy, exp_ball) # Set before `_load_robot_controllers()`, which wires `self.match_log` @@ -586,6 +450,10 @@ def __init__( # module docstring for why the columnar format exists). if isinstance(replay_writer_config, ColumnarReplayWriterConfig): self.replay_writer = ColumnarReplayWriter(replay_writer_config, my_team_is_yellow, exp_friendly, exp_enemy) + if sim_noise is not None and self.rsim_env is not None: + self.replay_writer.sim_noise_json = json.dumps( + {"settings": sim_noise.to_dict(), "draw": self.rsim_env.sim_draw.to_dict()}, sort_keys=True + ) elif replay_writer_config: self.replay_writer = ReplayWriter(replay_writer_config, my_team_is_yellow, exp_friendly, exp_enemy) else: @@ -614,22 +482,6 @@ def __init__( self.profiler_name = profiler_name self.profiler = cProfile.Profile() if profiler_name else None - def _start_vision_stream(self, http_port: int) -> None: - """Start the browser stream that mirrors refined game frames.""" - try: - self._vision_stream_renderer = GameFrameRenderer(self.full_field_dims, scale=300.0) - self.vision_stream = RSimVisionStreamServer( - http_port=http_port, - ) - self.vision_stream.start() - self._publish_vision_stream_frame() - self.logger.info("Vision stream available at %s", self.vision_stream.url) - print(f"Vision stream available at {self.vision_stream.url}") - except Exception: - self.vision_stream = None - self._vision_stream_renderer = None - self.logger.warning("Vision stream could not be started; continuing without browser video.", exc_info=True) - def _validate_vision_to_cmd_mapping(self, mapping: Optional[dict[int, int]], is_yellow: bool) -> dict[int, int]: if self.mode == Mode.REAL: is_my_team_color = is_yellow == self.my_team_is_yellow @@ -722,11 +574,6 @@ def _handle_sigint(self, sig, frame): self._stop_fps_live() print("\nStopping gracefully. Press Ctrl+C again to force quit.", flush=True) - def _stop_fps_live(self): - if self._fps_live: - self._fps_live.stop() - self._fps_live = None - def _load_mode(self, mode_str: str) -> Mode: """Convert a mode string to a Mode enum value. @@ -929,118 +776,6 @@ def _push_robot_feedback_to_referee(self) -> None: if isinstance(self.referee, CustomReferee): self.referee.set_robot_feedback_data(self._robot_feedback_snapshot) - def _teleport_ball_and_settle(self, x: float, y: float) -> None: - """`sim_controller.teleport_ball(x, y)`, plus arming the re-pin window - `_run_step` uses to counter the native engine's post-reset velocity - spike (see `_TELEPORT_SETTLE_TICKS`'s module docstring). - """ - self.sim_controller.teleport_ball(x, y) - self._teleport_settle_target = (x, y) - self._teleport_settle_ticks_left = _TELEPORT_SETTLE_TICKS - self._teleport_settle_extensions_left = _TELEPORT_SETTLE_MAX_EXTENSIONS - - def _tick_pending_placement_teleport(self) -> None: - """Fire the pending BALL_PLACEMENT_* teleport once no robot is within - `_PLACEMENT_TELEPORT_CLEARANCE_M` of the target (see its docstring).""" - if self._pending_placement_teleport is None: - return - x, y = self._pending_placement_teleport - frame = self.my.current_game_frame - robots = (*frame.friendly_robots.values(), *frame.enemy_robots.values()) - if any(math.hypot(r.p.x - x, r.p.y - y) < _PLACEMENT_TELEPORT_CLEARANCE_M for r in robots): - return - self._pending_placement_teleport = None - self._teleport_ball_and_settle(x, y) - - def _tick_teleport_settle(self) -> None: - """One tick of the re-pin window `_teleport_ball_and_settle` arms. - - Traced live in a tournament run (see `_TELEPORT_SETTLE_TICKS`'s - docstring): the native rsim engine's post-teleport velocity spike - doesn't decay with repeated resets — it simply waits for the first - real physics step *without* a reset immediately before it, however - many resets preceded it. So this doesn't just re-pin for a fixed - number of ticks: it keeps extending the window (bounded by - `_TELEPORT_SETTLE_MAX_EXTENSIONS`) for as long as the ball's most - recent velocity still reads as unsettled, and only stops once a tick - is observed with the window closed AND the ball has actually settled - (`_TELEPORT_SETTLE_SPEED_MPS`'s docstring has the full rationale for - why this needs to be a strict "at rest" check, not just "no longer - looks like the initial reset spike"). - - Reads the ball's velocity via `self.rsim_env.frame.ball` (the raw sim - state, refreshed every physics `step()`), not `current_game_frame` — - the latter is the *refined* frame `_step_game` builds once per tick, - so at the point this runs (after the referee block, before - `_step_game`) it is always one tick stale and would see last tick's - already-corrected velocity instead of this tick's live spike. - """ - if self.sim_controller is None or self._teleport_settle_target is None: - return - raw_ball = self.rsim_env.frame.ball if self.mode == Mode.RSIM and self.rsim_env is not None else None - ball_speed = math.hypot(raw_ball.v_x, raw_ball.v_y) if raw_ball is not None else 0.0 - settled = ball_speed <= _TELEPORT_SETTLE_SPEED_MPS - # The tick whose re-pin drove `_teleport_settle_ticks_left` to 0 - # still needs one more check here — its own `step()` (this tick, - # further down in `_run_step`) is what actually produces or clears - # the spike, and that only becomes observable next tick. So don't - # drop `_teleport_settle_target` (and stop checking) until a tick is - # seen with the window closed AND (settled OR out of extensions — - # a ball that never actually settles must still terminate the loop - # eventually rather than re-pin forever). - if self._teleport_settle_ticks_left <= 0 and (settled or self._teleport_settle_extensions_left <= 0): - self._teleport_settle_target = None - return - if not settled and self._teleport_settle_extensions_left > 0: - self._teleport_settle_ticks_left += 1 - self._teleport_settle_extensions_left -= 1 - x, y = self._teleport_settle_target - self.sim_controller.teleport_ball(x, y) - self._teleport_settle_ticks_left = max(0, self._teleport_settle_ticks_left - 1) - - def _tick_frozen_field_watchdog(self) -> None: - """Recover from the rare native-engine hang described in - `_SIM_FROZEN_FIELD_AUTO_RECOVER_SECONDS`'s module docstring. - - Runs once per tick, after `_step_game` has refreshed - `self.my.current_game_frame` for this tick. Only meaningful in sim - (`Mode.HALT` has its own dedicated auto-resume timer above, and a - real match has no `sim_controller` to recover through — `HALT` is - additionally excluded on purpose, since it's the one live-play state - where every robot legitimately holds zero velocity by design). - """ - if self.sim_controller is None or self.mode != Mode.RSIM: - return - frame = self.my.current_game_frame - if frame is None or frame.ball is None: - self._frozen_field_since = None - return - referee = getattr(frame, "referee", None) - current_command = getattr(referee, "referee_command", None) if referee is not None else None - if current_command == RefereeCommand.HALT: - self._frozen_field_since = None - return - - tol = _SIM_FROZEN_FIELD_VELOCITY_TOL_MPS - all_still = frame.ball.v.mag() <= tol and all( - robot.v.mag() <= tol for robot in (*frame.friendly_robots.values(), *frame.enemy_robots.values()) - ) - if not all_still: - self._frozen_field_since = None - return - - if self._frozen_field_since is None: - self._frozen_field_since = frame.ts - elif frame.ts - self._frozen_field_since >= _SIM_FROZEN_FIELD_AUTO_RECOVER_SECONDS: - self.logger.warning( - "Frozen-field watchdog: every robot and the ball reported exactly zero " - "velocity for %.1fs (native-engine hang, see _SIM_FROZEN_FIELD_AUTO_RECOVER_SECONDS) " - "— forcing a full-frame reset via teleport_ball to recover.", - frame.ts - self._frozen_field_since, - ) - self.sim_controller.teleport_ball(frame.ball.p.x, frame.ball.p.y) - self._frozen_field_since = None - def _remove_rsim_ball(self): """Removes the ball from the RSim environment by teleporting it off-field.""" self.sim_controller.remove_ball() @@ -1050,6 +785,7 @@ def _load_sim( self, rsim_noise: RsimGaussianNoise, rsim_vanishing: float, + sim_noise: Optional[SimNoise] = None, ) -> Tuple[Optional[SSLStandardEnv], Optional[AbstractSimController]]: """Mode RSIM: Loads the RSim environment with the expected number of robots and corresponding sim controller. Mode GRSIM: Loads corresponding sim controller and teleports robots in GRSim to ensure the expected number of @@ -1095,6 +831,7 @@ def _load_sim( ball_starting_position=self.field_bounds.center, gaussian_noise=rsim_noise, vanishing=rsim_vanishing, + sim_noise=sim_noise, ) if self.opp: @@ -1943,234 +1680,7 @@ def _run_step(self): # --- end of frame --- if self.show_live_status: - frame_end = time.perf_counter() - frame_dt = frame_end - frame_start - - self.elapsed_time += frame_dt - self.num_frames_elapsed += 1 - - if self.elapsed_time >= FPS_PRINT_INTERVAL: - fps = self.num_frames_elapsed / self.elapsed_time - - ref = self.referee_refiner - stage_secs = ref.stage_time_left - stage_min = int(stage_secs // 60) - stage_sec = int(stage_secs % 60) - display = Text() - display.append(f"FPS: {fps:.1f}", style="bold cyan") - display.append(" | ") - display.append(ref.last_command.name, style="bold yellow") - if ref.last_next_command: - display.append(" -> ") - display.append(ref.last_next_command.name, style="yellow") - display.append(" | ") - display.append(ref.stage.name.replace("_", " ").title()) - display.append(" | Blue ") - display.append(str(ref.blue_team.score), style="bold blue") - display.append(" - ") - display.append(str(ref.yellow_team.score), style="bold yellow") - display.append(" Yellow") - display.append(f" | {stage_min}:{stage_sec:02d} left") - display.append(" | Ref: ") - if isinstance(self.referee, CustomReferee): - display.append("custom", style="bold magenta") - display.append(" (") - display.append(self.referee.profile_name, style="magenta") - display.append(")") - elif isinstance(self.referee, OfficialReferee): - display.append("official", style="bold magenta") - else: - display.append("none", style="bold magenta") - - if ref.last_status_message: - display.append(f" | {ref.last_status_message}", style="dim") - - self._fps_live.update(display) - self._fps_live.refresh() - - self.elapsed_time = 0.0 - self.num_frames_elapsed = 0 - - def _publish_vision_stream_frame(self) -> None: - """Publish the latest refined game frame to the browser stream.""" - if self.vision_stream is None or self._vision_stream_renderer is None or self.my.current_game_frame is None: - return - if not self.vision_stream.is_due(): - return - self.vision_stream.publish_status(self._vision_stream_status()) - frame = self._vision_stream_renderer.render(self.my.current_game_frame) - self.vision_stream.publish_rgb_frame(frame) - - _VS_TEAM_NAMES = [ - "FC Recursion", - "Real Segfault", - "Borussia Debugmund", - "Manchester Bytecode", - "Inter Malloc", - "Atletico del Stack", - "Null Pointer United", - "Schalke 0x04", - "Deportivo Kernel", - "Racing Club de Runtime", - "Galactic Overhead", - "Ajax Exception", - "Off-by-One City", - "SV Deadlock", - "Infinite Loop FC", - ] - - _VS_IDLE_LINES = [ - "The robots are thinking...", - "Calculating optimal trajectory", - "Both sides plotting their next move", - "The crowd holds its breath", - "Pure silicon determination out there", - "No human reflexes required", - "Running at full clock speed", - "Algorithms at war", - "404: defence not found", - "This is peak robot football", - ] - - _VS_KICK_LINES = [ - "What a strike!", - "They've let it fly!", - "Big boot from the robot!", - "The ball is moving!", - "Powerful kick!", - "Sending it downfield!", - ] - - _VS_GOAL_LINES_YELLOW = [ - "GOAL! Yellow draws blood!", - "Yellow scores! Unbelievable!", - "The yellow machine delivers!", - "Yellow puts it in the net!", - ] - - _VS_GOAL_LINES_BLUE = [ - "GOAL! Blue strikes back!", - "Blue finds the net!", - "Brilliant from the blue side!", - "Blue pulls one back!", - ] - - _VS_FOOTBALLER_NAMES = [ - "Martin", - "Fred", - "Joel", - "Louis", - ] - - @staticmethod - def _assign_team_names() -> tuple[str, str]: - import random - - pool = list(StrategyRunner._VS_TEAM_NAMES) - random.shuffle(pool) - return pool[0], pool[1] - - def _update_vs_commentary(self, ball_speed: float | None, score_blue: int, score_yellow: int) -> None: - import random - - now = time.monotonic() - score = (score_blue, score_yellow) - - # Goal scored — highest priority - if score != self._vs_prev_score: - if score[1] > self._vs_prev_score[1]: - line = random.choice(self._VS_GOAL_LINES_YELLOW) - else: - line = random.choice(self._VS_GOAL_LINES_BLUE) - self._vs_commentary = line - self._vs_commentary_until = now + 5.0 - self._vs_prev_score = score - self._vs_prev_ball_speed = ball_speed or 0.0 - return - - self._vs_prev_score = score - - # Kick detected - prev_spd = self._vs_prev_ball_speed - cur_spd = ball_speed or 0.0 - self._vs_prev_ball_speed = cur_spd - if cur_spd > _VS_KICK_THRESHOLD and prev_spd <= _VS_KICK_THRESHOLD: - if now >= self._vs_commentary_until: - self._vs_commentary = random.choice(self._VS_KICK_LINES) - self._vs_commentary_until = now + 2.5 - - # Idle rotation - if now >= self._vs_idle_next: - if now >= self._vs_commentary_until: - self._vs_commentary = self._VS_IDLE_LINES[self._vs_idle_index % len(self._VS_IDLE_LINES)] - self._vs_idle_index += 1 - self._vs_idle_next = now + 6.0 - - def _vision_stream_status(self) -> dict[str, object]: - """Build status metadata shown above the browser stream.""" - stage_secs = max(0.0, self.referee_refiner.stage_time_left) - stage_min = int(stage_secs // 60) - stage_sec = int(stage_secs % 60) - - blue = self.referee_refiner.blue_team - yellow = self.referee_refiner.yellow_team - - ball = self.my.current_game_frame.ball if self.my.current_game_frame else None - ball_speed = (ball.v.x**2 + ball.v.y**2) ** 0.5 if ball is not None else None - - name_yellow, name_blue = ( - self._vs_team_names if self.my_team_is_yellow else (self._vs_team_names[1], self._vs_team_names[0]) - ) - self._update_vs_commentary(ball_speed, blue.score, yellow.score) - - return { - "time_left": f"{stage_min}:{stage_sec:02d}", - "score_blue": blue.score, - "score_yellow": yellow.score, - "yellow_cards_blue": blue.yellow_cards, - "yellow_cards_yellow": yellow.yellow_cards, - "red_cards_blue": blue.red_cards, - "red_cards_yellow": yellow.red_cards, - "team_blue": name_blue, - "team_yellow": name_yellow, - "mode": self.mode.value, - "ball_speed": round(ball_speed, 2) if ball_speed is not None else None, - "commentary": self._vs_commentary, - "annotations": self._vision_stream_annotations(), - "roster": self._vision_stream_roster(), - } - - def _get_robot_name(self, is_friendly: bool, robot_id: int) -> str: - key = (is_friendly, robot_id) - if key not in self._vs_robot_names: - used = set(self._vs_robot_names.values()) - pool = [n for n in self._VS_FOOTBALLER_NAMES if n not in used] - if not pool: - pool = self._VS_FOOTBALLER_NAMES - import random - - self._vs_robot_names[key] = random.choice(pool) - return self._vs_robot_names[key] - - def _vision_stream_annotations(self) -> list[dict]: - """Build per-robot label annotations (footballer name) for the overlay canvas.""" - renderer = self._vision_stream_renderer - game_frame = self.my.current_game_frame - if renderer is None or game_frame is None: - return [] - - annotations = [] - for robot in game_frame.friendly_robots.values(): - name = self._get_robot_name(True, robot.id) - px, py = renderer._pos_transform(robot.p.x, -robot.p.y) - annotations.append({"id": robot.id, "team": "friendly", "px": px, "py": py, "label": name}) - - for robot in game_frame.enemy_robots.values(): - name = self._get_robot_name(False, robot.id) - px, py = renderer._pos_transform(robot.p.x, -robot.p.y) - annotations.append({"id": robot.id, "team": "enemy", "px": px, "py": py, "label": name}) - - return annotations + self._update_live_status(frame_start) def _push_debug_status_to_referee(self) -> None: """Push per-robot debug status to CustomReferee for GUI display. @@ -2235,56 +1745,6 @@ def _committed_tactics(self) -> Optional[dict]: if info["committed"] and info["robots"] } - def _vision_stream_roster(self) -> list[dict]: - """Build the player roster list shown below the scoreboard.""" - game_frame = self.my.current_game_frame - if game_frame is None: - return [] - - _ROLE_LABELS = { - "GOALKEEPER": "Goalkeeper", - "DEFENDER": "Defender", - "STRIKER": "Striker", - "MIDFIELDER": "Midfielder", - "UNASSIGNED": "On field", - } - - role_map: dict = {} - passer_id: int | None = None - receiver_id: int | None = None - try: - bb = self.my.strategy.blackboard - if bb is not None: - role_map = bb.role_map or {} - passer_id = bb.get("passer_id") - receiver_id = bb.get("receiver_id") - except Exception: - pass - - roster = [] - for robot in game_frame.friendly_robots.values(): - name = self._get_robot_name(True, robot.id) - if robot.id == passer_id: - status = "Passing" - elif robot.id == receiver_id: - status = "Receiving" - else: - role = role_map.get(robot.id) - status = _ROLE_LABELS.get(role.name, "On field") if role else "On field" - roster.append( - {"name": name, "team": "yellow" if game_frame.my_team_is_yellow else "blue", "status": status} - ) - - for robot in game_frame.enemy_robots.values(): - name = self._get_robot_name(False, robot.id) - role = role_map.get(robot.id) - status = _ROLE_LABELS.get(role.name, "On field") if role else "On field" - roster.append( - {"name": name, "team": "blue" if game_frame.my_team_is_yellow else "yellow", "status": status} - ) - - return roster - def _draw_rsim_field_bounds_overlay(self) -> None: """Draw active field bounds overlay in RSIM human render mode.""" if self.mode != Mode.RSIM or not self.rsim_env: diff --git a/utama_core/run/vision_stream_status.py b/utama_core/run/vision_stream_status.py new file mode 100644 index 00000000..c2acb61a --- /dev/null +++ b/utama_core/run/vision_stream_status.py @@ -0,0 +1,260 @@ +"""Browser vision stream for `StrategyRunner`, mixed in as `VisionStreamStatusMixin`: starting it, +and the status it shows (scoreboard, commentary, footballer names and roster) built from the referee +and the current game frame.""" + +import time + +from utama_core.run.vision_stream import GameFrameRenderer, RSimVisionStreamServer + +_VS_KICK_THRESHOLD = 0.5 # m/s — ball speed above this triggers kick commentary + + +class VisionStreamStatusMixin: + """`StrategyRunner` methods for the browser vision stream; they read runner state set in `__init__`.""" + + def _start_vision_stream(self, http_port: int) -> None: + """Start the browser stream that mirrors refined game frames.""" + try: + self._vision_stream_renderer = GameFrameRenderer(self.full_field_dims, scale=300.0) + self.vision_stream = RSimVisionStreamServer( + http_port=http_port, + ) + self.vision_stream.start() + self._publish_vision_stream_frame() + self.logger.info("Vision stream available at %s", self.vision_stream.url) + print(f"Vision stream available at {self.vision_stream.url}") + except Exception: + self.vision_stream = None + self._vision_stream_renderer = None + self.logger.warning("Vision stream could not be started; continuing without browser video.", exc_info=True) + + def _publish_vision_stream_frame(self) -> None: + """Publish the latest refined game frame to the browser stream.""" + if self.vision_stream is None or self._vision_stream_renderer is None or self.my.current_game_frame is None: + return + if not self.vision_stream.is_due(): + return + self.vision_stream.publish_status(self._vision_stream_status()) + frame = self._vision_stream_renderer.render(self.my.current_game_frame) + self.vision_stream.publish_rgb_frame(frame) + + _VS_TEAM_NAMES = [ + "FC Recursion", + "Real Segfault", + "Borussia Debugmund", + "Manchester Bytecode", + "Inter Malloc", + "Atletico del Stack", + "Null Pointer United", + "Schalke 0x04", + "Deportivo Kernel", + "Racing Club de Runtime", + "Galactic Overhead", + "Ajax Exception", + "Off-by-One City", + "SV Deadlock", + "Infinite Loop FC", + ] + + _VS_IDLE_LINES = [ + "The robots are thinking...", + "Calculating optimal trajectory", + "Both sides plotting their next move", + "The crowd holds its breath", + "Pure silicon determination out there", + "No human reflexes required", + "Running at full clock speed", + "Algorithms at war", + "404: defence not found", + "This is peak robot football", + ] + + _VS_KICK_LINES = [ + "What a strike!", + "They've let it fly!", + "Big boot from the robot!", + "The ball is moving!", + "Powerful kick!", + "Sending it downfield!", + ] + + _VS_GOAL_LINES_YELLOW = [ + "GOAL! Yellow draws blood!", + "Yellow scores! Unbelievable!", + "The yellow machine delivers!", + "Yellow puts it in the net!", + ] + + _VS_GOAL_LINES_BLUE = [ + "GOAL! Blue strikes back!", + "Blue finds the net!", + "Brilliant from the blue side!", + "Blue pulls one back!", + ] + + _VS_FOOTBALLER_NAMES = [ + "Martin", + "Fred", + "Joel", + "Louis", + ] + + @staticmethod + def _assign_team_names() -> tuple[str, str]: + import random + + pool = list(VisionStreamStatusMixin._VS_TEAM_NAMES) + random.shuffle(pool) + return pool[0], pool[1] + + def _update_vs_commentary(self, ball_speed: float | None, score_blue: int, score_yellow: int) -> None: + import random + + now = time.monotonic() + score = (score_blue, score_yellow) + + # Goal scored — highest priority + if score != self._vs_prev_score: + if score[1] > self._vs_prev_score[1]: + line = random.choice(self._VS_GOAL_LINES_YELLOW) + else: + line = random.choice(self._VS_GOAL_LINES_BLUE) + self._vs_commentary = line + self._vs_commentary_until = now + 5.0 + self._vs_prev_score = score + self._vs_prev_ball_speed = ball_speed or 0.0 + return + + self._vs_prev_score = score + + # Kick detected + prev_spd = self._vs_prev_ball_speed + cur_spd = ball_speed or 0.0 + self._vs_prev_ball_speed = cur_spd + if cur_spd > _VS_KICK_THRESHOLD and prev_spd <= _VS_KICK_THRESHOLD: + if now >= self._vs_commentary_until: + self._vs_commentary = random.choice(self._VS_KICK_LINES) + self._vs_commentary_until = now + 2.5 + + # Idle rotation + if now >= self._vs_idle_next: + if now >= self._vs_commentary_until: + self._vs_commentary = self._VS_IDLE_LINES[self._vs_idle_index % len(self._VS_IDLE_LINES)] + self._vs_idle_index += 1 + self._vs_idle_next = now + 6.0 + + def _vision_stream_status(self) -> dict[str, object]: + """Build status metadata shown above the browser stream.""" + stage_secs = max(0.0, self.referee_refiner.stage_time_left) + stage_min = int(stage_secs // 60) + stage_sec = int(stage_secs % 60) + + blue = self.referee_refiner.blue_team + yellow = self.referee_refiner.yellow_team + + ball = self.my.current_game_frame.ball if self.my.current_game_frame else None + ball_speed = (ball.v.x**2 + ball.v.y**2) ** 0.5 if ball is not None else None + + name_yellow, name_blue = ( + self._vs_team_names if self.my_team_is_yellow else (self._vs_team_names[1], self._vs_team_names[0]) + ) + self._update_vs_commentary(ball_speed, blue.score, yellow.score) + + return { + "time_left": f"{stage_min}:{stage_sec:02d}", + "score_blue": blue.score, + "score_yellow": yellow.score, + "yellow_cards_blue": blue.yellow_cards, + "yellow_cards_yellow": yellow.yellow_cards, + "red_cards_blue": blue.red_cards, + "red_cards_yellow": yellow.red_cards, + "team_blue": name_blue, + "team_yellow": name_yellow, + "mode": self.mode.value, + "ball_speed": round(ball_speed, 2) if ball_speed is not None else None, + "commentary": self._vs_commentary, + "annotations": self._vision_stream_annotations(), + "roster": self._vision_stream_roster(), + } + + def _get_robot_name(self, is_friendly: bool, robot_id: int) -> str: + key = (is_friendly, robot_id) + if key not in self._vs_robot_names: + used = set(self._vs_robot_names.values()) + pool = [n for n in self._VS_FOOTBALLER_NAMES if n not in used] + if not pool: + pool = self._VS_FOOTBALLER_NAMES + import random + + self._vs_robot_names[key] = random.choice(pool) + return self._vs_robot_names[key] + + def _vision_stream_annotations(self) -> list[dict]: + """Build per-robot label annotations (footballer name) for the overlay canvas.""" + renderer = self._vision_stream_renderer + game_frame = self.my.current_game_frame + if renderer is None or game_frame is None: + return [] + + annotations = [] + for robot in game_frame.friendly_robots.values(): + name = self._get_robot_name(True, robot.id) + px, py = renderer._pos_transform(robot.p.x, -robot.p.y) + annotations.append({"id": robot.id, "team": "friendly", "px": px, "py": py, "label": name}) + + for robot in game_frame.enemy_robots.values(): + name = self._get_robot_name(False, robot.id) + px, py = renderer._pos_transform(robot.p.x, -robot.p.y) + annotations.append({"id": robot.id, "team": "enemy", "px": px, "py": py, "label": name}) + + return annotations + + def _vision_stream_roster(self) -> list[dict]: + """Build the player roster list shown below the scoreboard.""" + game_frame = self.my.current_game_frame + if game_frame is None: + return [] + + _ROLE_LABELS = { + "GOALKEEPER": "Goalkeeper", + "DEFENDER": "Defender", + "STRIKER": "Striker", + "MIDFIELDER": "Midfielder", + "UNASSIGNED": "On field", + } + + role_map: dict = {} + passer_id: int | None = None + receiver_id: int | None = None + try: + bb = self.my.strategy.blackboard + if bb is not None: + role_map = bb.role_map or {} + passer_id = bb.get("passer_id") + receiver_id = bb.get("receiver_id") + except Exception: + pass + + roster = [] + for robot in game_frame.friendly_robots.values(): + name = self._get_robot_name(True, robot.id) + if robot.id == passer_id: + status = "Passing" + elif robot.id == receiver_id: + status = "Receiving" + else: + role = role_map.get(robot.id) + status = _ROLE_LABELS.get(role.name, "On field") if role else "On field" + roster.append( + {"name": name, "team": "yellow" if game_frame.my_team_is_yellow else "blue", "status": status} + ) + + for robot in game_frame.enemy_robots.values(): + name = self._get_robot_name(False, robot.id) + role = role_map.get(robot.id) + status = _ROLE_LABELS.get(role.name, "On field") if role else "On field" + roster.append( + {"name": name, "team": "blue" if game_frame.my_team_is_yellow else "yellow", "status": status} + ) + + return roster diff --git a/utama_core/scenario_bench/banks/bank_v5.json b/utama_core/scenario_bench/banks/bank_v5.json deleted file mode 100644 index 478acb36..00000000 --- a/utama_core/scenario_bench/banks/bank_v5.json +++ /dev/null @@ -1,105757 +0,0 @@ -{ - "bank_id": "bank_v5", - "schema_version": 1, - "n_scenarios": 846, - "scenarios": [ - { - "scenario_id": "kickoff_center_v1", - "lifecycle": "candidate", - "lead_in_s": 0.0, - "provenance": { - "source_run_id": "hand_authored", - "evaluator_version": "53a03c17", - "trigger": "hand_authored", - "family": "kickoff", - "anchor_tick": null, - "source_replay": null, - "perspective": "candidate_kicking" - }, - "scenario": { - "sim_time": 0.0, - "ball_x": 0.0, - "ball_y": 0.0, - "ball_vx": 0.0, - "ball_vy": 0.0, - "friendly_robots": [ - { - "id": 0, - "x": -4.2, - "y": 0.0, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 1, - "x": -0.5, - "y": 0.0, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 2, - "x": -1.5, - "y": 1.5, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 3, - "x": -1.5, - "y": -1.5, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 4, - "x": -3.0, - "y": 1.0, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 5, - "x": -3.0, - "y": -1.0, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - } - ], - "enemy_robots": [ - { - "id": 0, - "x": 4.2, - "y": 0.0, - "orientation": 3.14159, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 1, - "x": 1.5, - "y": 0.0, - "orientation": 3.14159, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 2, - "x": 1.5, - "y": 1.5, - "orientation": 3.14159, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 3, - "x": 1.5, - "y": -1.5, - "orientation": 3.14159, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 4, - "x": 3.0, - "y": 1.0, - "orientation": 3.14159, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 5, - "x": 3.0, - "y": -1.0, - "orientation": 3.14159, - "vx": 0.0, - "vy": 0.0 - } - ], - "referee_command": "NORMAL_START", - "config_a_name": null, - "config_b_name": null, - "frame_ts": 0.0 - } - }, - { - "scenario_id": "direct_free_defending_near_box_v1", - "lifecycle": "candidate", - "lead_in_s": 0.0, - "provenance": { - "source_run_id": "hand_authored", - "evaluator_version": "53a03c17", - "trigger": "hand_authored", - "family": "direct_free_defending", - "anchor_tick": null, - "source_replay": null, - "perspective": "candidate_defending" - }, - "scenario": { - "sim_time": 0.0, - "ball_x": -2.3, - "ball_y": 0.0, - "ball_vx": 0.0, - "ball_vy": 0.0, - "friendly_robots": [ - { - "id": 0, - "x": -4.2, - "y": 0.0, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 1, - "x": -3.3, - "y": 0.6, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 2, - "x": -3.3, - "y": -0.6, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 3, - "x": -1.0, - "y": 1.5, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 4, - "x": -0.5, - "y": -2.0, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 5, - "x": 1.0, - "y": 0.0, - "orientation": 0.0, - "vx": 0.0, - "vy": 0.0 - } - ], - "enemy_robots": [ - { - "id": 0, - "x": 4.2, - "y": 0.0, - "orientation": 3.14159, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 1, - "x": -2.3, - "y": 0.0, - "orientation": 3.14159, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 2, - "x": -1.5, - "y": 2.0, - "orientation": 3.14159, - "vx": 0.0, - "vy": 0.0 - }, - { - "id": 3, - "x": -1.5, - "y": -2.0, - "orientation": 3.14159, - "vx": 0.0, 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diff --git a/utama_core/scenario_bench/banks/situation_rates_real.json b/utama_core/scenario_bench/banks/situation_rates_real.json deleted file mode 100644 index f74033d5..00000000 --- a/utama_core/scenario_bench/banks/situation_rates_real.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "matches": 118, - "live_min_per_match": 7.430567325800376, - "per_team_live_min": { - "ball won, attacking third": 0.829144892848261, - "ball won, middle third": 1.2254693086182344, - "ball won, defensive third": 1.2545521074732973, - "free kick, attacking third": 0.810896862194104, - "free kick, middle third": 0.5685402050685806, - "free kick, defensive third": 0.7852355690866956, - "kickoff": 0.40944018780264885, - "penalty": 0.012545521074732972, - "force start": 1.0412782492028367 - }, - "inputs": [ - "2022", - "2023", - "2024", - "2025", - "2026" - ], - "command": "python -m utama_core.analysis.goal_ledger /2022 /2023 /2024 /2025 /2026 --out /real_ledger.json --rates utama_core/scenario_bench/banks/situation_rates_real.json --workers 8" -} diff --git a/utama_core/scenario_bench/banks/situation_rates_sim.json b/utama_core/scenario_bench/banks/situation_rates_sim.json deleted file mode 100644 index ee18a0eb..00000000 --- a/utama_core/scenario_bench/banks/situation_rates_sim.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "matches": 153, - "live_min_per_match": 8.987654320987653, - "per_team_live_min": { - "ball won, attacking third": 0.4046945701357466, - "ball won, middle third": 0.46759857789269554, - "ball won, defensive third": 1.7311328377504849, - "free kick, attacking third": 0.5854072398190046, - "free kick, middle third": 0.14362475759534582, - "free kick, defensive third": 0.22761797026502908, - "kickoff": 0.6414027149321267, - "penalty": 0.05490465416936005, - "force start": 0.17525856496444733 - }, - "inputs": [ - "tournament_20261011_230119" - ], - "command": "python -m utama_core.analysis.goal_ledger replays/tournament_20261011_230119 --out /sim_ledger.json --rates utama_core/scenario_bench/banks/situation_rates_sim.json --workers 8" -} diff --git a/utama_core/scenario_bench/hand_authored_scenarios.py b/utama_core/scenario_bench/hand_authored_scenarios.py deleted file mode 100644 index 3f42347d..00000000 --- a/utama_core/scenario_bench/hand_authored_scenarios.py +++ /dev/null @@ -1,272 +0,0 @@ -"""Hand-authored canonical bench scenarios. - -These are the ~20 (4 to start) fixed anchors from roadmap item 14's bank v1 -shape: scenarios that never move, don't depend on any match or replay, and -act as a stable reference when the harvested part of the bank turns over -between versions. Every scenario here has `ScenarioTrigger.HAND_AUTHORED` -and `anchor_tick=None` (see `start.ScenarioProvenance`). - -Each scenario below is written with friendly defending the left goal and attacking +x, -in plain pitch-frame metres; `all_hand_authored_scenarios` mirrors them (`_mirrored`) into -the frame every start is played in, where friendly (the candidate) is the +x/right side, -matching `tournament.run_match`'s `my_team_is_right=True`. They were once returned -unmirrored, which put the candidate's keeper in the goal it attacks. Half length 4.5m, half width 3.0m -(`STANDARD_FIELD_DIMS`). Robot 0 is the goalkeeper on each side (pinned -separately by the kernel strategy, see `engine/strategy.py`); 1-5 are -outfield, matching `match`'s `OUTFIELD_ROBOT_IDS`. - -Run this module directly to print the static-screen result for every -scenario it defines (`pixi run python -m utama_core.scenario_bench.hand_authored_scenarios`). -""" - -from __future__ import annotations - -import dataclasses -import math -import subprocess -from pathlib import Path - -from utama_core.entities.referee.referee_command import RefereeCommand -from utama_core.replay.scenario import RobotState, Scenario -from utama_core.scenario_bench.start import ( - BenchScenario, - ScenarioFamily, - ScenarioProvenance, - ScenarioTrigger, - static_screen, -) - -_HALF_LEN = 4.5 -_HALF_WIDTH = 3.0 - - -def _git_revision() -> str: - try: - result = subprocess.run( - ["git", "rev-parse", "--short", "HEAD"], - check=True, - capture_output=True, - text=True, - ) - return result.stdout.strip() or "unknown" - except (OSError, subprocess.CalledProcessError): - return "unknown" - - -def _rs(id_: int, x: float, y: float, orientation: float = 0.0) -> RobotState: - return RobotState(id=id_, x=x, y=y, orientation=orientation, vx=0.0, vy=0.0) - - -def _provenance(family: ScenarioFamily, perspective: str = "candidate_kicking") -> ScenarioProvenance: - return ScenarioProvenance( - source_run_id="hand_authored", - evaluator_version=_git_revision(), - trigger=ScenarioTrigger.HAND_AUTHORED, - family=family, - anchor_tick=None, - source_replay=None, - perspective=perspective, - ) - - -def _kickoff_center() -> BenchScenario: - """Standard kickoff formation, candidate (friendly) taking the kick. - Mirrors the default formation `StrategyRunner` constructs matches with.""" - friendly = ( - _rs(0, -_HALF_LEN + 0.3, 0.0), # goalkeeper - _rs(1, -0.5, 0.0, orientation=0.0), # kicker, just behind center - _rs(2, -1.5, 1.5), - _rs(3, -1.5, -1.5), - _rs(4, -3.0, 1.0), - _rs(5, -3.0, -1.0), - ) - enemy = ( - _rs(0, _HALF_LEN - 0.3, 0.0, orientation=3.14159), - _rs(1, 1.5, 0.0, orientation=3.14159), - _rs(2, 1.5, 1.5, orientation=3.14159), - _rs(3, 1.5, -1.5, orientation=3.14159), - _rs(4, 3.0, 1.0, orientation=3.14159), - _rs(5, 3.0, -1.0, orientation=3.14159), - ) - scenario = Scenario( - sim_time=0.0, - ball_x=0.0, - ball_y=0.0, - ball_vx=0.0, - ball_vy=0.0, - friendly_robots=friendly, - enemy_robots=enemy, - referee_command=RefereeCommand.PREPARE_KICKOFF_YELLOW, - source_replay=Path("hand_authored"), - config_a_name=None, - config_b_name=None, - frame_ts=0.0, - ) - return BenchScenario( - scenario_id="kickoff_center_v1", - scenario=scenario, - provenance=_provenance(ScenarioFamily.KICKOFF), - ) - - -def _direct_free_defending_near_box() -> BenchScenario: - """Enemy direct free kick just outside the candidate's own defense - area — one of "the most important families" per the design discussion: - defensive free kicks near the candidate's own box.""" - friendly = ( - _rs(0, -_HALF_LEN + 0.3, 0.0), - _rs(1, -_HALF_LEN + 1.2, 0.6), # wall/marker - _rs(2, -_HALF_LEN + 1.2, -0.6), - _rs(3, -1.0, 1.5), - _rs(4, -0.5, -2.0), - _rs(5, 1.0, 0.0), - ) - enemy = ( - _rs(0, _HALF_LEN - 0.3, 0.0, orientation=3.14159), - _rs(1, -_HALF_LEN + 2.2, 0.0, orientation=3.14159), # kicker - _rs(2, -1.5, 2.0, orientation=3.14159), - _rs(3, -1.5, -2.0, orientation=3.14159), - _rs(4, 0.0, 1.0, orientation=3.14159), - _rs(5, 2.0, 0.5, orientation=3.14159), - ) - scenario = Scenario( - sim_time=0.0, - ball_x=-_HALF_LEN + 2.2, - ball_y=0.0, - ball_vx=0.0, - ball_vy=0.0, - friendly_robots=friendly, - enemy_robots=enemy, - referee_command=RefereeCommand.DIRECT_FREE_BLUE, - source_replay=Path("hand_authored"), - config_a_name=None, - config_b_name=None, - frame_ts=0.0, - ) - return BenchScenario( - scenario_id="direct_free_defending_near_box_v1", - scenario=scenario, - provenance=_provenance(ScenarioFamily.DIRECT_FREE_DEFENDING, perspective="candidate_defending"), - ) - - -def _direct_free_attacking_near_box() -> BenchScenario: - """Candidate's direct free kick just outside the enemy's defense area — - the mirror image of the above; restarts are asymmetric so both - perspectives are kept as separate scenarios (item 14).""" - friendly = ( - _rs(0, -_HALF_LEN + 0.3, 0.0), - _rs(1, _HALF_LEN - 2.2, 0.0), # kicker - _rs(2, 1.5, 2.0), - _rs(3, 1.5, -2.0), - _rs(4, 0.0, 1.0), - _rs(5, -2.0, 0.5), - ) - enemy = ( - _rs(0, _HALF_LEN - 0.3, 0.0, orientation=3.14159), - _rs(1, _HALF_LEN - 1.2, 0.6, orientation=3.14159), # wall/marker - _rs(2, _HALF_LEN - 1.2, -0.6, orientation=3.14159), - _rs(3, 1.0, 1.5, orientation=3.14159), - _rs(4, 0.5, -2.0, orientation=3.14159), - _rs(5, -1.0, 0.0, orientation=3.14159), - ) - scenario = Scenario( - sim_time=0.0, - ball_x=_HALF_LEN - 2.2, - ball_y=0.0, - ball_vx=0.0, - ball_vy=0.0, - friendly_robots=friendly, - enemy_robots=enemy, - referee_command=RefereeCommand.DIRECT_FREE_YELLOW, - source_replay=Path("hand_authored"), - config_a_name=None, - config_b_name=None, - frame_ts=0.0, - ) - return BenchScenario( - scenario_id="direct_free_attacking_near_box_v1", - scenario=scenario, - provenance=_provenance(ScenarioFamily.DIRECT_FREE_ATTACKING, perspective="candidate_kicking"), - ) - - -def _open_play_3v2_counter() -> BenchScenario: - """Candidate has a 3v2 numerical advantage on a counter-attack in the - enemy half, ball loose just ahead of the lead attacker. Event-triggered - family (numerical-advantage detector, item 14 sec 2), hand-authored here - as a fixed anchor rather than harvested.""" - friendly = ( - _rs(0, -_HALF_LEN + 0.3, 0.0), - _rs(1, 1.5, 0.0), # lead attacker, on the ball - _rs(2, 0.5, 1.8), - _rs(3, 0.5, -1.8), - _rs(4, -2.0, 0.0), - _rs(5, -3.5, 1.0), - ) - enemy = ( - _rs(0, _HALF_LEN - 0.3, 0.0, orientation=3.14159), - _rs(1, 2.5, 0.5, orientation=3.14159), # retreating defender - _rs(2, 2.5, -0.5, orientation=3.14159), - _rs(3, 3.5, 0.0, orientation=3.14159), - _rs(4, 4.0, 1.5, orientation=3.14159), - _rs(5, 4.0, -1.5, orientation=3.14159), - ) - scenario = Scenario( - sim_time=0.0, - ball_x=1.7, - ball_y=0.0, - ball_vx=1.5, - ball_vy=0.0, - friendly_robots=friendly, - enemy_robots=enemy, - referee_command=RefereeCommand.FORCE_START, - source_replay=Path("hand_authored"), - config_a_name=None, - config_b_name=None, - frame_ts=0.0, - ) - return BenchScenario( - scenario_id="open_play_3v2_counter_v1", - scenario=scenario, - provenance=_provenance(ScenarioFamily.OPEN_PLAY_COUNTER, perspective="candidate_kicking"), - ) - - -def _mirrored(bench_scenario: BenchScenario) -> BenchScenario: - """Reflect a start across the halfway line (x -> -x), so the side defending the left goal - defends the right one. A robot facing +x then faces -x.""" - - def robot(r: RobotState) -> RobotState: - return dataclasses.replace(r, x=-r.x, vx=-r.vx, orientation=math.pi - r.orientation) - - s = bench_scenario.scenario - scenario = dataclasses.replace( - s, - ball_x=-s.ball_x, - ball_vx=-s.ball_vx, - friendly_robots=tuple(robot(r) for r in s.friendly_robots), - enemy_robots=tuple(robot(r) for r in s.enemy_robots), - ) - return dataclasses.replace(bench_scenario, scenario=scenario) - - -def all_hand_authored_scenarios() -> tuple[BenchScenario, ...]: - return tuple( - _mirrored(bs) - for bs in ( - _kickoff_center(), - _direct_free_defending_near_box(), - _direct_free_attacking_near_box(), - _open_play_3v2_counter(), - ) - ) - - -if __name__ == "__main__": - for bs in all_hand_authored_scenarios(): - result = static_screen(bs.scenario) - status = "OK" if result.ok else "FAIL" - print(f"[{status}] {bs.scenario_id} ({bs.provenance.family.value})") - for violation in result.violations: - print(f" - {violation}") diff --git a/utama_core/scenario_bench/scenario_harvester.py b/utama_core/scenario_bench/scenario_harvester.py deleted file mode 100644 index e0bf8801..00000000 --- a/utama_core/scenario_bench/scenario_harvester.py +++ /dev/null @@ -1,410 +0,0 @@ -"""Harvest `BenchScenario`s from a tagged tournament run, one per situation start. - -A start is a situation of the goal ledger (`utama_core.analysis.goal_ledger`), from config_a's -(the candidate's) view, and carries its type (`ScenarioProvenance.situation`, one of -`goal_ledger.TYPES`): - -- A restart, from the moment the referee awards it: `→ PREPARE_KICKOFF_*`, - `→ PREPARE_PENALTY_*`, `STOP/BALL_PLACEMENT_* → DIRECT_FREE_*`, and `STOP → FORCE_START`. - Played from there, the referee runs the restart as in the match (`apply_scenario`): the - kickoff or penalty is prepared and goes live, the free kick is taken. Kickoffs and penalties - were once anchored at the `NORMAL_START` that ends their preparation, which a bench runner - could only replay as its own kickoff. Each start's perspective (candidate kicking or - defending) is the colour the command names: restarts are asymmetric. A free kick's third is - where the ball is, in the kicking side's attack frame, as the ledger's. -- A ball won in open play (`ball_won=True`): the start of each of the ledger's possession spells - that is not a restart being taken, at the frame nearest it, if play is live there. Trigger - `EVENT`; family `OPEN_PLAY_POSSESSION` when the candidate won the ball, `OPEN_PLAY_COUNTER` - when the opponent did. - -`harvest_run_dir` harvests every start of every trustworthy match, then keeps up to -`per_situation` of each type (`sample_by_situation`), so that rare situations are not -swamped by common ones; how often each happens is the bench's weights, not the bank's mix. - -Match-level trust gate, per that same design pass: a match's replay is only -harvested from when its `.stats.json` (written by -`match.run_match`'s `stats_path`, see `MatchStats.to_json`) exists -and reports `stall_events == []`. A match with no stats file, or any stall -event, contributes nothing — this is the gate that would have caught the -925-file pre-fix contaminated replay run this session found (every one of -those matches was stuck at `PREPARE_KICKOFF_YELLOW` from t=0, so it either -has a `RESTART_STALL` recorded or, for even older runs with no watchdog at -all, no `.stats.json` to check — both fail closed here). - -This module does NOT run a tournament itself — `harvest_run_dir` takes an -already-completed run directory (e.g. `replays/tournament_.../`) and is a -pure read; see `tools/scenario_bench.py` for the harvest → screen → score → -report pipeline this feeds. -""" - -from __future__ import annotations - -import collections -import functools -import json -import logging -import random -from concurrent.futures import ProcessPoolExecutor -from dataclasses import dataclass -from pathlib import Path -from typing import Optional - -from utama_core.analysis.goal_ledger import TYPES, match_ledger, situation_type, third -from utama_core.entities.referee.referee_command import RefereeCommand -from utama_core.replay.scenario import Scenario, scenario_from_replay -from utama_core.scenario_bench.start import ( - BenchScenario, - ScenarioFamily, - ScenarioProvenance, - ScenarioTrigger, - is_near_duplicate, - static_screen, -) - -logger = logging.getLogger(__name__) - -# Restart families keyed by the referee command that is transitioned INTO. -# `perspective` is candidate-relative and resolved per-match against which -# colour the candidate is playing — see `_perspective_family`. -_PENALTY_COMMANDS = frozenset({RefereeCommand.PREPARE_PENALTY_YELLOW, RefereeCommand.PREPARE_PENALTY_BLUE}) -_KICKOFF_COMMANDS = frozenset({RefereeCommand.PREPARE_KICKOFF_YELLOW, RefereeCommand.PREPARE_KICKOFF_BLUE}) -_DIRECT_FREE_COMMANDS = frozenset({RefereeCommand.DIRECT_FREE_YELLOW, RefereeCommand.DIRECT_FREE_BLUE}) -_LIVE_PLAY_COMMANDS = frozenset({RefereeCommand.NORMAL_START, RefereeCommand.FORCE_START}) -# A free kick follows STOP, or ball placement when the ball had to be moved first -# (`CustomReferee`'s usual sequence after the ball goes out). -_DIRECT_FREE_PREV_COMMANDS = frozenset( - {RefereeCommand.STOP, RefereeCommand.BALL_PLACEMENT_YELLOW, RefereeCommand.BALL_PLACEMENT_BLUE} -) - -# A restart ceremony resolving in under this many sim seconds after the -# previous transition is treated as noise (e.g. two sidecar rows for the -# same instant) rather than two independent restarts. -_MIN_RESTART_GAP_S = 0.5 - - -@dataclass(frozen=True) -class RestartTransition: - """One restart awarded (see module docstring), from a `.intentions.jsonl` - sidecar's referee-event rows.""" - - sim_time: float - prev_command: Optional[RefereeCommand] - command: RefereeCommand - # Which colour (True=yellow) takes the restart, as `command` names it; None for a - # FORCE_START, which names no colour. - kicking_is_yellow: Optional[bool] - - -def _referee_rows(sidecar_path: Path) -> list[dict]: - if not sidecar_path.exists(): - return [] - rows = [] - with open(sidecar_path, "r") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - row = json.loads(line) - except json.JSONDecodeError: - continue - if row.get("event") != "referee": - continue - rows.append(row) - return rows - - -def _kicking_is_yellow(command: RefereeCommand) -> Optional[bool]: - if command in (_KICKOFF_COMMANDS | _PENALTY_COMMANDS | _DIRECT_FREE_COMMANDS): - return command.name.endswith("_YELLOW") - return None - - -def find_restart_transitions(sidecar_path: Path) -> list[RestartTransition]: - """Scan a `.intentions.jsonl` sidecar for every restart awarded (see module docstring): - `-> PREPARE_KICKOFF_*/PREPARE_PENALTY_*`, `STOP/BALL_PLACEMENT_* -> DIRECT_FREE_*`, - `STOP -> FORCE_START`. A match's opening kickoff is its first row, after no command. - - Returns transitions in ascending `sim_time` order. A transition whose - `sim_time` is within `_MIN_RESTART_GAP_S` of the previous one found is - dropped as sidecar noise, not a second independent restart. - """ - rows = _referee_rows(sidecar_path) - transitions: list[RestartTransition] = [] - prev_command: Optional[RefereeCommand] = None - last_transition_ts = float("-inf") - - for row in rows: - command_name = row.get("command") - ts = row.get("sim_time") - if command_name is None or ts is None: - continue - try: - command = RefereeCommand[command_name] - except KeyError: - continue - - is_restart = ( - (command in (_KICKOFF_COMMANDS | _PENALTY_COMMANDS) and prev_command != command) - or (command in _DIRECT_FREE_COMMANDS and prev_command in _DIRECT_FREE_PREV_COMMANDS) - or (command == RefereeCommand.FORCE_START and prev_command == RefereeCommand.STOP) - ) - - if is_restart and (ts - last_transition_ts) >= _MIN_RESTART_GAP_S: - transitions.append( - RestartTransition( - sim_time=float(ts), - prev_command=prev_command, - command=command, - kicking_is_yellow=_kicking_is_yellow(command), - ) - ) - last_transition_ts = ts - - prev_command = command - - return transitions - - -def _family_for(transition: RestartTransition) -> ScenarioFamily: - if transition.command in _KICKOFF_COMMANDS: - return ScenarioFamily.KICKOFF - if transition.command in _PENALTY_COMMANDS: - return ScenarioFamily.PENALTY - if transition.command in _DIRECT_FREE_COMMANDS: - # Perspective (attacking/defending) is resolved per-candidate by the - # caller (`_perspective_family`), not here — a DIRECT_FREE transition - # alone doesn't know which side is "the candidate". - return ScenarioFamily.DIRECT_FREE_ATTACKING - return ScenarioFamily.OPEN_PLAY_COUNTER # FORCE_START from STOP with no free-kick context - - -def _perspective_family( - transition: RestartTransition, family: ScenarioFamily, candidate_is_yellow: bool -) -> tuple[ScenarioFamily, str]: - if transition.kicking_is_yellow is None: - return family, "candidate_kicking" - if transition.kicking_is_yellow == candidate_is_yellow: - return family, "candidate_kicking" - if family == ScenarioFamily.DIRECT_FREE_ATTACKING: - return ScenarioFamily.DIRECT_FREE_DEFENDING, "candidate_defending" - return family, "candidate_defending" - - -def restart_situation(transition: RestartTransition, candidate_is_yellow: bool, scenario: Scenario) -> str: - """A harvested restart's situation type from the candidate's view (`goal_ledger.TYPES`). The - scenario is the candidate's view, defending the right goal: the candidate attacks -x.""" - if transition.command in _KICKOFF_COMMANDS: - kind = "kickoff" - elif transition.command in _PENALTY_COMMANDS: - kind = "penalty" - elif transition.command in _DIRECT_FREE_COMMANDS: - kind = "free kick" - else: - kind = "force start" - side = None - if transition.kicking_is_yellow is not None: - side = "friendly" if transition.kicking_is_yellow == candidate_is_yellow else "enemy" - attack = -1.0 if side == "friendly" else 1.0 - return situation_type({"kind": kind, "side": side, "third": third(attack * scenario.ball_x)}, "friendly") - - -def match_is_trustworthy(stats_path: Path) -> bool: - """The match-level harvest gate: a `.stats.json` must exist and report - zero stall events. Missing or unparseable files fail closed (not - trustworthy) — see module docstring.""" - if not stats_path.exists(): - return False - try: - data = json.loads(stats_path.read_text()) - except (json.JSONDecodeError, OSError): - return False - stall_events = data.get("stall_events") - return stall_events is not None and len(stall_events) == 0 - - -def _stats_path_for(replay_path: Path) -> Path: - return replay_path.with_name(f"{replay_path.stem}.stats.json") - - -def _sidecar_path_for(replay_path: Path) -> Path: - return replay_path.with_name(f"{replay_path.stem}.intentions.jsonl") - - -def _harvested(replay_path: Path, t: float, showing: Optional[RefereeCommand] = None) -> Optional[Scenario]: - """The replay's field state at `t` (at the first frame showing `showing`, when given; see - `scenario_from_replay`), or None if it can't be read or fails the static screen.""" - try: - scenario = scenario_from_replay(replay_path, t, showing=showing) - except ValueError: - logger.warning("Could not load frame at t=%.3f from %s", t, replay_path) - return None - screen = static_screen(scenario) - if not screen.ok: - logger.info( - "Dropping start at t=%.3f in %s: static screen failed (%s)", t, replay_path, "; ".join(screen.violations) - ) - return None - return scenario - - -def _slug(situation: str) -> str: - return situation.replace(" third", "").replace(", ", "_").replace(" ", "_") - - -def harvest_replay( - replay_path: Path, - *, - source_run_id: str, - evaluator_version: str, - candidate_is_yellow: bool = True, - ball_won: bool = False, -) -> list[BenchScenario]: - """Harvest every restart-transition `BenchScenario` from one replay and, with `ball_won`, - every ball-won start (see module docstring). - - Does NOT check `match_is_trustworthy` itself — callers (typically - `harvest_run_dir`) are expected to have already gated on it, since - checking per-replay here would silently produce an empty list for an - untrustworthy match instead of letting the caller report *why* nothing - was harvested. - """ - sidecar_path = _sidecar_path_for(replay_path) - transitions = find_restart_transitions(sidecar_path) - scenarios: list[BenchScenario] = [] - - for index, transition in enumerate(transitions): - # The opening kickoff is the same situation in every match (both teams in their - # kickoff formation): harvested from a whole run it swamps the bank. - if index == 0 and _family_for(transition) == ScenarioFamily.KICKOFF: - continue - scenario = _harvested(replay_path, transition.sim_time, showing=transition.command) - if scenario is None: - continue - - base_family = _family_for(transition) - family, perspective = _perspective_family(transition, base_family, candidate_is_yellow) - - scenario_id = f"{replay_path.stem}_t{transition.sim_time:.1f}_{perspective}" - provenance = ScenarioProvenance( - source_run_id=source_run_id, - evaluator_version=evaluator_version, - trigger=ScenarioTrigger.RESTART, - family=family, - anchor_tick=transition.sim_time, - source_replay=replay_path, - perspective=perspective, - situation=restart_situation(transition, candidate_is_yellow, scenario), - ) - scenarios.append(BenchScenario(scenario_id=scenario_id, scenario=scenario, provenance=provenance)) - - # the ledger's friendly side is the replay's, config_a: the candidate - if ball_won and replay_path.suffix == ".npz": - for spell in match_ledger(replay_path)["spells"]: - if spell["takes_restart"]: - continue - scenario = _harvested(replay_path, spell["t"]) - if scenario is None or scenario.referee_command not in _LIVE_PLAY_COMMANDS: - continue - own = spell["side"] == "friendly" - situation = situation_type({**spell, "kind": "ball won"}, "friendly") - provenance = ScenarioProvenance( - source_run_id=source_run_id, - evaluator_version=evaluator_version, - trigger=ScenarioTrigger.EVENT, - family=ScenarioFamily.OPEN_PLAY_POSSESSION if own else ScenarioFamily.OPEN_PLAY_COUNTER, - anchor_tick=spell["t"], - source_replay=replay_path, - perspective="candidate_in_possession" if own else "candidate_defending", - situation=situation, - ) - scenario_id = f"{replay_path.stem}_t{spell['t']:.1f}_{_slug(situation)}" - scenarios.append(BenchScenario(scenario_id=scenario_id, scenario=scenario, provenance=provenance)) - - return scenarios - - -def sample_by_situation(scenarios: list[BenchScenario], per_situation: int, *, seed: int = 0) -> list[BenchScenario]: - """Up to `per_situation` starts of each situation type, kickoffs half as many (their - formations recur): drawn one match at a time in a seeded order, so a type's starts come - from as many matches as it has, skipping any near-duplicate of a start already drawn.""" - rng = random.Random(seed) - by_type: dict[str, dict[Path, list[BenchScenario]]] = collections.defaultdict(dict) - for s in scenarios: - by_type[s.provenance.situation].setdefault(s.provenance.source_replay, []).append(s) - picked: list[BenchScenario] = [] - for typ in sorted(by_type, key=lambda t: TYPES.index(t) if t in TYPES else len(TYPES)): - matches = [by_type[typ][m] for m in sorted(by_type[typ], key=str)] - rng.shuffle(matches) - for starts in matches: - rng.shuffle(starts) - cap = per_situation // 2 if typ.endswith("kickoff") else per_situation - kept: list[BenchScenario] = [] - for round_ in range(max(len(m) for m in matches)): - for starts in matches: - if ( - len(kept) < cap - and round_ < len(starts) - and not any(is_near_duplicate(starts[round_], k) for k in kept) - ): - kept.append(starts[round_]) - picked += kept - return picked - - -def _replay_files_in(run_dir: Path) -> list[Path]: - # `.sparse_referee.pkl` sits next to each replay; it is not a replay itself. - pkls = [p for p in run_dir.glob("*.pkl") if not p.name.endswith(".sparse_referee.pkl")] - return sorted({*run_dir.glob("*.npz"), *pkls}) - - -def harvest_run_dir( - run_dir: Path, - *, - evaluator_version: str, - candidate_is_yellow: bool = True, - per_situation: Optional[int] = None, - seed: int = 0, - workers: int = 1, -) -> tuple[list[BenchScenario], dict]: - """Harvest every start (restarts and balls won) from every trustworthy match in `run_dir` - (a completed `round_robin.py` run directory), then, with `per_situation`, keep a sample - of each situation type (`sample_by_situation`). - - Returns `(scenarios, report)` where `report` is a small summary dict - (`matches_seen`, `matches_trusted`, `matches_untrusted`, - `scenarios_harvested`, and per situation type how many starts the run had and how - many were kept) — a caller (the bench CLI) uses this to print "harvested N scenarios - from M/K trustworthy matches" rather than only a bare list. - """ - replay_paths = _replay_files_in(run_dir) - trusted = [p for p in replay_paths if match_is_trustworthy(_stats_path_for(p))] - for p in sorted(set(replay_paths) - set(trusted)): - logger.info("Skipping untrusted match %s (missing/nonzero-stall .stats.json)", p.name) - - harvest = functools.partial( - harvest_replay, - source_run_id=run_dir.name, - evaluator_version=evaluator_version, - candidate_is_yellow=candidate_is_yellow, - ball_won=True, - ) - if workers > 1: - with ProcessPoolExecutor(max_workers=workers) as pool: - per_match = list(pool.map(harvest, trusted)) - else: - per_match = [harvest(p) for p in trusted] - scenarios = [s for match in per_match for s in match] - found = collections.Counter(s.provenance.situation for s in scenarios) - if per_situation is not None: - scenarios = sample_by_situation(scenarios, per_situation, seed=seed) - kept = collections.Counter(s.provenance.situation for s in scenarios) - - report = { - "matches_seen": len(replay_paths), - "matches_trusted": len(trusted), - "matches_untrusted": len(replay_paths) - len(trusted), - "scenarios_harvested": len(scenarios), - "situations": {typ: {"found": found[typ], "kept": kept[typ]} for typ in TYPES if found[typ]}, - } - return scenarios, report diff --git a/utama_core/scenario_bench/scenario_scorer.py b/utama_core/scenario_bench/scenario_scorer.py deleted file mode 100644 index 31a8953b..00000000 --- a/utama_core/scenario_bench/scenario_scorer.py +++ /dev/null @@ -1,336 +0,0 @@ -"""Score one `BenchScenario` for a candidate strategy against an opponent -strategy — the "outer loop, fast half" scorer from `docs/roadmap.md` item 14. - -Builds a fresh headless `StrategyRunner` (same construction pattern -`repro_from_replay.py` uses), teleports it into the scenario's field state -via `apply_scenario`, ticks forward `horizon_s` sim seconds, and reads the -result off `MatchStats` — never inventing a new metric here, only reusing -the counters `utama_core.engine.match_stats` already accumulates live (see -that module's docstring for where each one came from). - -Per-scenario outcome is a fixed ordinal scale, from the CANDIDATE's -perspective, per the design pass in item 14: - goal_for > shot_on_target > entry_retained > neutral > - turnover > shot_conceded > goal_against -plus independent `foul` and `stalled` flags (a stall is reported, never ranked: -it can be the strategy's fault or the planner's/referee's). TURNOVER counts real -ball losses only, the same rule as `turnover_breakdown`: the opponent kept the -ball for more than `FLICKER_S` (raw `MatchStats.turnovers` is mostly two robots -on one ball flipping "nearest"), or a restart was given to the opponent while we -had the ball. `score_scenario` returns the raw -`MatchStats` for both the candidate-as-friendly run so a caller (the bench -CLI) can compute paired differentials against a baseline strategy on the -same scenario+seed, not just look at one run in isolation — a single run's -absolute outcome is not the bench's unit of comparison (see item 14's -"always as differentials vs the opponent, never absolute"). - -Each start also measures `SIGNALS`, the candidate's side of the signals in -`docs/signals.md` over the scored window, from the same `ChanceTracker` the round-robin -analysis uses: an outcome says how a start ended, these say what happened in it. -""" - -from __future__ import annotations - -import math -from dataclasses import dataclass -from enum import IntEnum -from typing import Optional - -from utama_core.analysis.chances import ChanceTracker -from utama_core.analysis.turnover_breakdown import ENEMY_RESTARTS, FLICKER_S, LIVE -from utama_core.config.field_params import STANDARD_FIELD_DIMS -from utama_core.config.settings import REPLAY_BASE_PATH -from utama_core.custom_referee import CustomReferee -from utama_core.custom_referee.geometry import RefereeGeometry -from utama_core.engine.abstract_strategy import AbstractStrategy -from utama_core.engine.match_stats import MatchStats -from utama_core.entities.referee.referee_command import RefereeCommand -from utama_core.replay.columnar_writer import ColumnarReplayWriterConfig -from utama_core.replay.scenario import apply_scenario -from utama_core.run import StrategyRunner -from utama_core.scenario_bench.start import BenchScenario -from utama_core.strategy import kernel_strategy - -TICKS_PER_SECOND = 60 # matches round_robin.py/rsim's default step rate -N_OUTFIELD = 5 -OUTFIELD_ROBOT_IDS = tuple(range(1, N_OUTFIELD + 1)) - - -class ScenarioOutcome(IntEnum): - """Ordinal outcome scale, candidate's perspective. Higher is better for - the candidate. `NEUTRAL` is the value when nothing decisive happened — - the fragment simply ran out its horizon.""" - - GOAL_AGAINST = -3 - SHOT_CONCEDED = -2 - TURNOVER = -1 - NEUTRAL = 0 - ENTRY_RETAINED = 1 - SHOT_ON_TARGET = 2 - GOAL_FOR = 3 - - -@dataclass(frozen=True) -class ScenarioScoreResult: - scenario_id: str - outcome: ScenarioOutcome - foul: bool - horizon_s: float - ticks_run: int - stats: MatchStats - error: Optional[str] = None - stalled: bool = False - signals: Optional[dict[str, float]] = None # see `start_signals` - - -# Signal -> +1 where more is usually better for the candidate, -1 where less is. -SIGNALS = { - "shots": 1, - "open_shots": 1, # shots weighted by the share of the goal mouth left open - "regains_to_shot": 1, - "entries": 1, - "real_losses": -1, - "shots_faced": -1, - "open_shots_faced": -1, - "danger_s": -1, # seconds the opponent held the ball in the candidate's defensive third -} - - -def start_signals(chances: dict, *, real_losses: int, entries: int) -> dict[str, float]: - """The candidate's ("friendly") `SIGNALS` over one start, from `ChanceTracker.result()`.""" - shots = [s for s in chances["shots"] if s["side"] == "friendly"] - faced = [s for s in chances["shots"] if s["side"] == "enemy"] - return { - "shots": len(shots), - "open_shots": round(sum(s["open_goal"] for s in shots), 2), - "regains_to_shot": sum(r["shot_after_s"] is not None for r in chances["regains"] if r["side"] == "friendly"), - "entries": entries, - "real_losses": real_losses, - "shots_faced": len(faced), - "open_shots_faced": round(sum(s["open_goal"] for s in faced), 2), - "danger_s": chances["danger"]["friendly"]["s"], - } - - -def _resolve_config_name(name: str) -> str: - if not name.startswith("build_"): - name = f"build_{name}" - if not name.endswith("_kernel_strategy"): - name = f"{name}_kernel_strategy" - if not hasattr(kernel_strategy, name): - raise ValueError(f"Unknown strategy config {name!r}") - return name - - -def _build_runner( - candidate_config: str, opponent_config: str, *, stats_path: str, record: Optional[str] = None -) -> StrategyRunner: - """`record`: a replay name relative to `REPLAY_BASE_PATH`; when set, the run writes - `.npz` (the replay) and `.intentions.jsonl` (the match log) there.""" - build_candidate = getattr(kernel_strategy, _resolve_config_name(candidate_config)) - build_opponent = getattr(kernel_strategy, _resolve_config_name(opponent_config)) - strategy_a = AbstractStrategy(build_kernel_strategy=build_candidate(OUTFIELD_ROBOT_IDS)) - strategy_b = AbstractStrategy(build_kernel_strategy=build_opponent(OUTFIELD_ROBOT_IDS)) - - referee = CustomReferee.from_profile_name( - "simulation", n_robots_yellow=N_OUTFIELD + 1, n_robots_blue=N_OUTFIELD + 1 - ) - - return StrategyRunner( - strategy=strategy_a, - opp_strategy=strategy_b, - my_team_is_yellow=True, - my_team_is_right=True, - mode="rsim", - exp_friendly=N_OUTFIELD + 1, - exp_enemy=N_OUTFIELD + 1, - exp_ball=True, - referee=referee, - enable_vision_stream=False, - referee_initial_command=RefereeCommand.PREPARE_KICKOFF_YELLOW, - # The round-robins the banks are harvested from run fpp (match.run_match); - # a start played with another planner measures something those matches never did. - control_scheme="fpp", - stats_path=stats_path, - **( - { - "replay_writer_config": ColumnarReplayWriterConfig(replay_name=record, overwrite_existing=True), - "match_log_path": str(REPLAY_BASE_PATH / f"{record}.intentions.jsonl"), - } - if record is not None - else {} - ), - ) - - -class _RealLossWatch: - """Counts the candidate's real ball losses tick by tick (see module docstring). - Friendly is always yellow here, so the opponent's restarts are the BLUE ones.""" - - def __init__(self) -> None: - self.losses = 0 - self._pending_since: Optional[float] = None # a live turnover not yet confirmed - self._turnovers = 0 - self._cmd = None - self._we_had_it = False - - def step(self, t: float, cmd, turnovers: int, poss_side: Optional[str]) -> None: - if cmd in LIVE and turnovers > self._turnovers and self._pending_since is None: - self._pending_since = t - if self._pending_since is not None: - if poss_side == "friendly": - self._pending_since = None # won back within the flicker window - elif t - self._pending_since > FLICKER_S: - self.losses += 1 - self._pending_since = None - if cmd in ENEMY_RESTARTS and self._cmd not in ENEMY_RESTARTS and self._we_had_it: - self.losses += 1 - self._pending_since = None # the turnover that led here is this same loss - self._turnovers, self._cmd, self._we_had_it = turnovers, cmd, poss_side == "friendly" - - -def _classify_outcome( - before: MatchStats, after: MatchStats, own_goal: bool, conceded_goal: bool, real_losses: int = 0 -) -> ScenarioOutcome: - """Diff two `MatchStats` snapshots (candidate is always "friendly" — - `apply_scenario`/`_build_runner` always construct the candidate as - `runner.my`/yellow/right) into one ordinal `ScenarioOutcome`. - - Priority order matches the ordinal scale itself: a goal dominates a - shot, a shot dominates an entry/turnover, ties fall to NEUTRAL. Only - the deltas over the scenario's horizon matter, not the absolute counts - (a scenario doesn't start at zero shots if harvested mid-match). - """ - if own_goal: - return ScenarioOutcome.GOAL_FOR - if conceded_goal: - return ScenarioOutcome.GOAL_AGAINST - - d_shots_friendly = after.shots.get("friendly", 0) - before.shots.get("friendly", 0) - d_shots_enemy = after.shots.get("enemy", 0) - before.shots.get("enemy", 0) - if d_shots_friendly > 0: - return ScenarioOutcome.SHOT_ON_TARGET - if d_shots_enemy > 0: - return ScenarioOutcome.SHOT_CONCEDED - - d_entries = after.attacking_third_entries - before.attacking_third_entries - if real_losses > 0: - return ScenarioOutcome.TURNOVER - if d_entries > 0: - return ScenarioOutcome.ENTRY_RETAINED - - return ScenarioOutcome.NEUTRAL - - -def score_scenario( - bench_scenario: BenchScenario, - *, - candidate_config: str, - opponent_config: str, - horizon_s: float = 20.0, - stats_path: str = "/tmp/scenario_bench_stats.json", - record: Optional[str] = None, -) -> ScenarioScoreResult: - """Run `bench_scenario` for `candidate_config` (as the candidate, always - friendly/yellow/right) against `opponent_config`, ticking forward - `horizon_s` sim seconds, and return the ordinal outcome + full - `MatchStats` diff. - - `bench_scenario.lead_in_s` (nonzero for event-triggered scenarios, see - `start.py`'s docstring on the mem-loss lead-in) is ticked - BEFORE the scored window starts, so both policies get a runway to - reconstruct roles — the `MatchStats` "before" snapshot is taken after - the lead-in, not at the raw teleport. - """ - geometry = RefereeGeometry.from_field_dims(STANDARD_FIELD_DIMS) - scenario = bench_scenario.to_scenario() - - try: - runner = _build_runner(candidate_config, opponent_config, stats_path=stats_path, record=record) - except Exception as exc: # noqa: BLE001 - report as a failed score, not a crash (e.g. unknown config name) - return ScenarioScoreResult( - scenario_id=bench_scenario.scenario_id, - outcome=ScenarioOutcome.NEUTRAL, - foul=False, - horizon_s=horizon_s, - ticks_run=0, - stats=MatchStats({}, {}, {}), - error=f"runner construction failed: {exc}", - ) - - try: - apply_scenario(runner, scenario, verify=True) - except Exception as exc: # noqa: BLE001 - report as a failed score, not a crash - runner.close() - return ScenarioScoreResult( - scenario_id=bench_scenario.scenario_id, - outcome=ScenarioOutcome.NEUTRAL, - foul=False, - horizon_s=horizon_s, - ticks_run=0, - stats=runner.match_stats.finalize() if runner.match_stats is not None else MatchStats({}, {}, {}), - error=f"apply_scenario failed: {exc}", - ) - - try: - lead_in_ticks = int(bench_scenario.lead_in_s * TICKS_PER_SECOND) - for _ in range(lead_in_ticks): - runner.step_once() - - before = runner.match_stats.finalize() if runner.match_stats is not None else MatchStats({}, {}, {}) - - own_goal = False - conceded_goal = False - acc = runner.match_stats - loss_watch = _RealLossWatch() - chance_tracker = ChanceTracker() - # Regains count only after a restart into live play, not after the start itself: - # an open-play start is already live, and isn't one. - live_since = -math.inf - prev_cmd = None - horizon_ticks = int(horizon_s * TICKS_PER_SECOND) - ticks_run = 0 - for _ in range(horizon_ticks): - shots_before = dict(acc._shots) if acc is not None else None - runner.step_once() - ticks_run += 1 - frame = runner.my.current_game_frame - if acc is not None: - cmd = frame.referee.referee_command if frame.referee else None - if cmd in LIVE and prev_cmd is not None and prev_cmd not in LIVE: - live_since = frame.ts - prev_cmd = cmd - loss_watch.step(ticks_run / TICKS_PER_SECOND, cmd, acc._turnovers, acc._poss_side) - chance_tracker.step(frame, acc, cmd, live_since, shots_before) - if frame.ball is not None: - bx, by = frame.ball.p.x, frame.ball.p.y - # Friendly is always right-defending (my_team_is_right=True; a 20 s - # start never reaches half-time, where the teams change ends), so a ball in the RIGHT goal is conceded by friendly and a - # ball in the LEFT goal is scored by friendly. - if geometry.is_in_right_goal(bx, by): - conceded_goal = True - break - if geometry.is_in_left_goal(bx, by): - own_goal = True - break - - after = runner.match_stats.finalize() if runner.match_stats is not None else before - foul = sum(after.rule_event_counts.values()) > sum(before.rule_event_counts.values()) - outcome = _classify_outcome(before, after, own_goal, conceded_goal, loss_watch.losses) - - return ScenarioScoreResult( - scenario_id=bench_scenario.scenario_id, - outcome=outcome, - foul=foul, - horizon_s=horizon_s, - ticks_run=ticks_run, - stats=after, - stalled=len(after.stall_events) > len(before.stall_events), - signals=start_signals( - chance_tracker.result(), - real_losses=loss_watch.losses, - entries=after.attacking_third_entries - before.attacking_third_entries, - ), - ) - finally: - runner.close() diff --git a/utama_core/scenario_bench/start.py b/utama_core/scenario_bench/start.py deleted file mode 100644 index 8dde426e..00000000 --- a/utama_core/scenario_bench/start.py +++ /dev/null @@ -1,464 +0,0 @@ -"""Scenario bench data model: provenance, lifecycle, and the static validity -screen shared by every scenario source (hand-authored, harvested, weakness, -event-triggered — see `docs/roadmap.md` item 14's outer-loop fast half). - -This module intentionally does not know how to *run* a scenario — that's -`scenario.apply_scenario` (which `BenchScenario.to_scenario()` feeds into) -plus whatever match-loop the bench tool itself drives. This module only -answers: is this snapshot well-formed, and where did it come from. - -Design constraints carried over from the design conversation that preceded -this module (2026-09-04), not just this file's own choices: -- The bank must be policy-agnostic. `BenchScenario` stores field state only - (positions/velocities/referee command), never tactic `mem` — the same - limitation `scenario.py` already documents for repro use. A candidate - strategy that restructures its tactic layer must still be able to consume - every scenario in the bank. -- Every scenario carries enough provenance to retire it *by provenance* in - one query if a harvest run is later found to be contaminated (see item 15 - and this session's discovery that a 925-file "complete" replay run was - entirely pre-fix data stuck at kickoff). -- Lifecycle is deliberately conservative: `CANDIDATE` scenarios never score. - Only `ACTIVE` scenarios do. Nothing enters the scored bank silently. -""" - -from __future__ import annotations - -import dataclasses -import json -import math -import random -from dataclasses import dataclass, field -from enum import Enum -from pathlib import Path -from typing import Optional - -from utama_core.config.field_params import STANDARD_FIELD_DIMS -from utama_core.config.physical_constants import ROBOT_RADIUS -from utama_core.entities.referee.referee_command import RefereeCommand -from utama_core.replay.scenario import RobotState, Scenario - -# A robot/ball placed further than this outside the nominal field rectangle -# is not "just off by a little", it's a fabrication or coordinate-frame bug. -_OUT_OF_BOUNDS_MARGIN_M = 0.5 - -# Two robots (or a robot and the ball) closer than this are overlapping, -# not just close — 2x ROBOT_RADIUS plus a small slack for float noise. -_MIN_ROBOT_SEPARATION_M = 2 * ROBOT_RADIUS - 0.01 - -# Hand-authored scenarios don't have a previous tick to compare against, so -# the physical-plausibility speed check only applies when a scenario states -# nonzero velocity. Generous — this is a sanity check, not a controller limit. -_MAX_PLAUSIBLE_SPEED_MPS = 6.0 - - -class ScenarioTrigger(Enum): - """How this scenario's anchor moment was identified.""" - - HAND_AUTHORED = "hand_authored" - RESTART = "restart" # a restart, from when the referee awards it (see scenario_harvester) - EVENT = "event" # MatchStats-detected: possession change, entry, etc. - PERIODIC = "periodic" # fallback sampling, not trigger-detected - - -class ScenarioFamily(Enum): - """Coarse situational grouping, used for frequency weighting and - per-family score aggregation (never a global composite — see roadmap - item 14's "per-family is the unit" scoring note).""" - - KICKOFF = "kickoff" - DIRECT_FREE_ATTACKING = "direct_free_attacking" - DIRECT_FREE_DEFENDING = "direct_free_defending" - PENALTY = "penalty" - BALL_PLACEMENT = "ball_placement" - OPEN_PLAY_COUNTER = "open_play_counter" # just lost the ball; also harvested FORCE_START restarts - OPEN_PLAY_POSSESSION = "open_play_possession" # our possession, a pass still to make - OPEN_PLAY_LOOSE_BALL = "open_play_loose_ball" - WEAKNESS = "weakness" # harvested from a lost play; tagged separately - - -class ScenarioLifecycle(Enum): - """Only ACTIVE scenarios are scored. See module docstring.""" - - CANDIDATE = "candidate" # freshly harvested/authored, not yet screened - VALIDATED = "validated" # passed static + dynamic screen + spot-check - ACTIVE = "active" # in the scored bank - RETIRED = "retired" # excluded; provenance kept for audit - - -@dataclass(frozen=True) -class ScenarioProvenance: - """Where a scenario came from, sufficient to retire an entire batch by - query if its source run is later found to be contaminated.""" - - source_run_id: str # e.g. a tournament directory name, or "hand_authored" - evaluator_version: str # git revision the source run (or authoring) used - trigger: ScenarioTrigger - family: ScenarioFamily - anchor_tick: Optional[float] = None # sim_time of the anchor moment; None for hand-authored - source_replay: Optional[Path] = None - perspective: str = "candidate_kicking" # or "candidate_defending" — restarts are asymmetric (see item 14) - # a harvested start's situation type from the candidate's view, one of `goal_ledger.TYPES` - situation: Optional[str] = None - - -@dataclass(frozen=True) -class BenchScenario: - """A single scenario in the bench, wrapping the field-state `Scenario` - the runner actually consumes with provenance + lifecycle metadata. - - `scenario_id` is a short stable slug (e.g. "kickoff_center_v1") used for - per-scenario quality tracking across bank versions (roadmap item 14's - "ongoing quality tracking" — whether a scenario's bench outcome tracked - the full-game outcome after each ladder run). - """ - - scenario_id: str - scenario: Scenario - provenance: ScenarioProvenance - lifecycle: ScenarioLifecycle = ScenarioLifecycle.CANDIDATE - lead_in_s: float = 0.0 # event-triggered scenarios start lead_in_s before the anchor (see item 14 sec 3) - - def to_scenario(self) -> Scenario: - return self.scenario - - def to_dict(self) -> dict: - """Plain-JSON representation for a persisted bank (see `save_bank`). - - Field state only — no tactic `mem`, matching the module docstring's - policy-agnostic constraint. `source_replay` is stored as a string - (or None) since it's provenance metadata, not a path this process - needs to resolve back into a live `Path` for anything other than - display. - """ - s = self.scenario - return { - "scenario_id": self.scenario_id, - "lifecycle": self.lifecycle.value, - "lead_in_s": self.lead_in_s, - "provenance": { - "source_run_id": self.provenance.source_run_id, - "evaluator_version": self.provenance.evaluator_version, - "trigger": self.provenance.trigger.value, - "family": self.provenance.family.value, - "anchor_tick": self.provenance.anchor_tick, - "source_replay": str(self.provenance.source_replay) if self.provenance.source_replay else None, - "perspective": self.provenance.perspective, - "situation": self.provenance.situation, - }, - "scenario": { - "sim_time": s.sim_time, - "ball_x": s.ball_x, - "ball_y": s.ball_y, - "ball_vx": s.ball_vx, - "ball_vy": s.ball_vy, - "friendly_robots": [dataclasses.asdict(r) for r in s.friendly_robots], - "enemy_robots": [dataclasses.asdict(r) for r in s.enemy_robots], - "referee_command": s.referee_command.name if s.referee_command is not None else None, - "config_a_name": s.config_a_name, - "config_b_name": s.config_b_name, - "frame_ts": s.frame_ts, - }, - } - - @staticmethod - def from_dict(d: dict) -> "BenchScenario": - """Inverse of `to_dict`. `source_replay` round-trips to a `Path` - wrapping whatever string was stored (or `Path(".")` when it was - None, matching `Scenario.source_replay`'s non-Optional type — the - original replay a persisted scenario came from is provenance, not a - file this process needs to still exist).""" - sd = d["scenario"] - prov = d["provenance"] - scenario = Scenario( - sim_time=sd["sim_time"], - ball_x=sd["ball_x"], - ball_y=sd["ball_y"], - ball_vx=sd["ball_vx"], - ball_vy=sd["ball_vy"], - friendly_robots=tuple(RobotState(**r) for r in sd["friendly_robots"]), - enemy_robots=tuple(RobotState(**r) for r in sd["enemy_robots"]), - referee_command=RefereeCommand[sd["referee_command"]] if sd["referee_command"] is not None else None, - source_replay=Path(prov["source_replay"]) if prov["source_replay"] else Path("."), - config_a_name=sd.get("config_a_name"), - config_b_name=sd.get("config_b_name"), - frame_ts=sd.get("frame_ts", 0.0), - ) - provenance = ScenarioProvenance( - source_run_id=prov["source_run_id"], - evaluator_version=prov["evaluator_version"], - trigger=ScenarioTrigger(prov["trigger"]), - family=ScenarioFamily(prov["family"]), - anchor_tick=prov["anchor_tick"], - source_replay=Path(prov["source_replay"]) if prov["source_replay"] else None, - perspective=prov["perspective"], - situation=prov.get("situation"), - ) - return BenchScenario( - scenario_id=d["scenario_id"], - scenario=scenario, - provenance=provenance, - lifecycle=ScenarioLifecycle(d["lifecycle"]), - lead_in_s=d.get("lead_in_s", 0.0), - ) - - -@dataclass(frozen=True) -class StaticScreenResult: - ok: bool - violations: tuple[str, ...] = field(default_factory=tuple) - - -def _in_bounds(x: float, y: float, margin: float = _OUT_OF_BOUNDS_MARGIN_M) -> bool: - half_len = STANDARD_FIELD_DIMS.full_field_half_length + margin - half_width = STANDARD_FIELD_DIMS.full_field_half_width + margin - return -half_len <= x <= half_len and -half_width <= y <= half_width - - -def _speed(vx: float, vy: float) -> float: - return math.hypot(vx, vy) - - -def static_screen(scenario: Scenario) -> StaticScreenResult: - """Cheap, self-contained checks on a single snapshot — no play-forward. - - Mirrors roadmap item 14's static-check list: in-bounds, no overlap, - physically plausible speeds, at least one robot per team present. Does - NOT check "ball not stationary too long" or "no stall in surrounding - window" — those need the source replay's surrounding ticks, which this - function deliberately doesn't take, so those checks belong to whatever - harvester builds the `BenchScenario` (it already has the replay open). - """ - violations: list[str] = [] - - if not _in_bounds(scenario.ball_x, scenario.ball_y): - violations.append(f"ball out of bounds at ({scenario.ball_x:.2f}, {scenario.ball_y:.2f})") - if _speed(scenario.ball_vx, scenario.ball_vy) > _MAX_PLAUSIBLE_SPEED_MPS * 2: - # Balls legitimately travel faster than robots (a hard kick), so this - # gets a looser multiplier than the robot check below. - violations.append(f"implausible ball speed {_speed(scenario.ball_vx, scenario.ball_vy):.2f} m/s") - - all_robots: list[tuple[str, RobotState]] = [("friendly", r) for r in scenario.friendly_robots] + [ - ("enemy", r) for r in scenario.enemy_robots - ] - - if not scenario.friendly_robots: - violations.append("no friendly robots present") - if not scenario.enemy_robots: - violations.append("no enemy robots present") - - for side, robot in all_robots: - # no margin: the sim refuses to teleport a robot past the field lines, and a - # start that can't be set up would be scored as if nothing happened - if not _in_bounds(robot.x, robot.y, margin=0.0): - violations.append(f"{side} robot {robot.id} out of bounds at ({robot.x:.2f}, {robot.y:.2f})") - speed = _speed(robot.vx, robot.vy) - if speed > _MAX_PLAUSIBLE_SPEED_MPS: - violations.append(f"{side} robot {robot.id} implausible speed {speed:.2f} m/s") - - for i in range(len(all_robots)): - side_i, robot_i = all_robots[i] - for j in range(i + 1, len(all_robots)): - side_j, robot_j = all_robots[j] - dist = math.hypot(robot_i.x - robot_j.x, robot_i.y - robot_j.y) - if dist < _MIN_ROBOT_SEPARATION_M: - violations.append( - f"{side_i} robot {robot_i.id} overlaps {side_j} robot {robot_j.id} " - f"(dist={dist:.3f}m < {_MIN_ROBOT_SEPARATION_M:.3f}m)" - ) - - return StaticScreenResult(ok=not violations, violations=tuple(violations)) - - -# rsim is deterministic, so one run per scenario is one sample and says nothing -# about noise. `jittered` gives each repeat of a scenario a slightly different -# start: robots moved up to this far, and turned up to this much. -_JITTER_POS_M = 0.05 -_JITTER_OREN_RAD = 0.1 -# Robots this close to the ball keep their exact pose: their relation to the ball -# (on the dribbler, lined up to take a restart) is what the scenario is about. -_JITTER_KEEP_NEAR_BALL_M = 0.3 - - -def jittered(bench_scenario: "BenchScenario", seed: int) -> "BenchScenario": - """`bench_scenario` with every robot not near the ball nudged by a small random - offset, reproducible from (`scenario_id`, `seed`). Seed 0 is the scenario as - authored. A draw that fails `static_screen` (an overlap) is redrawn.""" - if seed == 0: - return bench_scenario - rng = random.Random(f"{bench_scenario.scenario_id}:{seed}") - sc = bench_scenario.scenario - - def nudge(r: RobotState) -> RobotState: - if math.hypot(r.x - sc.ball_x, r.y - sc.ball_y) < _JITTER_KEEP_NEAR_BALL_M: - return r - return dataclasses.replace( - r, - x=r.x + rng.uniform(-_JITTER_POS_M, _JITTER_POS_M), - y=r.y + rng.uniform(-_JITTER_POS_M, _JITTER_POS_M), - orientation=r.orientation + rng.uniform(-_JITTER_OREN_RAD, _JITTER_OREN_RAD), - ) - - for _ in range(20): - candidate = dataclasses.replace( - sc, - friendly_robots=tuple(nudge(r) for r in sc.friendly_robots), - enemy_robots=tuple(nudge(r) for r in sc.enemy_robots), - ) - if static_screen(candidate).ok: - return dataclasses.replace(bench_scenario, scenario=candidate) - return bench_scenario - - -# Bumped whenever `BenchScenario.to_dict`'s schema changes in a way old -# banks can't be read back with (a field renamed/removed, not just added) — -# `load_bank` refuses to load a mismatched major version rather than -# silently misinterpreting an old file. -_BANK_SCHEMA_VERSION = 1 - - -def save_bank(scenarios: list["BenchScenario"], path: Path, *, bank_id: str) -> None: - """Write `scenarios` to `path` as a single JSON manifest. - - Per roadmap item 14: "Immutable per bank version — adding scenarios - makes a new bank ID and forces a champion re-baseline." `bank_id` is - caller-chosen (e.g. "v1", or a date-stamped tag) and stored alongside a - schema version and scenario count so a stale/mismatched bank fails - loudly on load rather than being silently treated as compatible. This - function does not enforce immutability itself (nothing stops a second - `save_bank` call to the same path) — that discipline is a caller/process - convention (new scenarios -> new path/bank_id), not a file-format one. - """ - payload = { - "bank_id": bank_id, - "schema_version": _BANK_SCHEMA_VERSION, - "n_scenarios": len(scenarios), - "scenarios": [s.to_dict() for s in scenarios], - } - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text(json.dumps(payload, indent=2)) - - -def load_bank(path: Path) -> tuple[str, list["BenchScenario"]]: - """Inverse of `save_bank`. Returns `(bank_id, scenarios)`. - - Raises `ValueError` if the file's `schema_version` doesn't match this - process's `_BANK_SCHEMA_VERSION` — a version bump means the dict shape - changed incompatibly, and guessing at a mismatched shape would produce - a wrong-but-not-crashing bank silently, which is exactly the failure - mode item 14's contamination-audit design exists to avoid. - """ - payload = json.loads(path.read_text()) - version = payload.get("schema_version") - if version != _BANK_SCHEMA_VERSION: - raise ValueError( - f"Bank {path} has schema_version={version}, this process expects " - f"{_BANK_SCHEMA_VERSION}. Re-harvest and re-save rather than loading it as-is." - ) - scenarios = [BenchScenario.from_dict(d) for d in payload["scenarios"]] - return payload["bank_id"], scenarios - - -# Two starts this close are the same situation twice (kickoff formations recur match after -# match): keeping both adds count to a bank, not independent evidence, and inflates its t. -_DUPLICATE_BALL_M = 0.10 -_DUPLICATE_ROBOT_M = 0.15 - - -def _positions(scenario: Scenario) -> dict[tuple[str, int], tuple[float, float]]: - return {("friendly", r.id): (r.x, r.y) for r in scenario.friendly_robots} | { - ("enemy", r.id): (r.x, r.y) for r in scenario.enemy_robots - } - - -def is_near_duplicate(a: "BenchScenario", b: "BenchScenario") -> bool: - """Same family, perspective and referee command, ball within `_DUPLICATE_BALL_M` and - every robot (by team and id) within `_DUPLICATE_ROBOT_M`.""" - sa, sb = a.scenario, b.scenario - if (a.provenance.family, a.provenance.perspective, sa.referee_command) != ( - b.provenance.family, - b.provenance.perspective, - sb.referee_command, - ): - return False - if math.dist((sa.ball_x, sa.ball_y), (sb.ball_x, sb.ball_y)) > _DUPLICATE_BALL_M: - return False - pa, pb = _positions(sa), _positions(sb) - return pa.keys() == pb.keys() and all(math.dist(pa[k], pb[k]) <= _DUPLICATE_ROBOT_M for k in pa) - - -def drop_near_duplicates( - scenarios: list["BenchScenario"], *, keep: list["BenchScenario"] = () -) -> list["BenchScenario"]: - """`scenarios` minus near-duplicates of `keep` (an existing bank) or of an earlier - scenario in the list. A survivor whose id is already taken gets its source run appended.""" - kept = list(keep) - ids = {s.scenario_id for s in kept} - new: list[BenchScenario] = [] - for s in scenarios: - if any(is_near_duplicate(s, k) for k in kept): - continue - if s.scenario_id in ids: - s = dataclasses.replace(s, scenario_id=f"{s.scenario_id}_{s.provenance.source_run_id}") - ids.add(s.scenario_id) - kept.append(s) - new.append(s) - return new - - -# --- Situation tags (docs/pitch_zones.md) ------------------------------------------------ -# -# Where a start is and who it belongs to, from the candidate's point of view, so a start can -# be found by situation (`scenario_bench.py --where`). Derived from the start's own state, not -# stored in the bank. The candidate is friendly: yellow, defending the right goal, attacking -x. - -_ON_BALL_M = 0.2 # a robot centre this close to the ball has it -NEAR_BALL_M = 1.5 # robots this close to the ball count in the `near` tag -TAGS = ("third", "lane", "restart", "ball", "near") - - -def start_tags(bench_scenario: "BenchScenario") -> dict[str, str]: - """`third`: defensive / middle / attacking. `lane`: left_wing / centre / right_wing (as the - candidate faces the goal it attacks). `restart`: ours / theirs (the team the referee command - gives the kickoff, free kick, penalty or ball placement to), stop, or live. `ball`: ours / - theirs (a robot within `_ON_BALL_M`) or loose. `near`: our and their robots within - `NEAR_BALL_M` of the ball, e.g. "2v3".""" - s = bench_scenario.scenario - half_length = STANDARD_FIELD_DIMS.full_field_half_length - progress = half_length - s.ball_x # metres from our goal line toward the goal we attack - third = 2 * half_length / 3 - left = -s.ball_y # facing -x, our left is -y - centre = STANDARD_FIELD_DIMS.half_defense_area_width - - cmd = s.referee_command - if cmd is None or cmd in (RefereeCommand.NORMAL_START, RefereeCommand.FORCE_START): - restart = "live" - elif cmd.name.endswith("_YELLOW"): - restart = "ours" - elif cmd.name.endswith("_BLUE"): - restart = "theirs" - else: - restart = "stop" - - def dists(robots): - return [math.hypot(r.x - s.ball_x, r.y - s.ball_y) for r in robots] - - ours, theirs = dists(s.friendly_robots), dists(s.enemy_robots) - nearest_ours, nearest_theirs = min(ours, default=math.inf), min(theirs, default=math.inf) - if min(nearest_ours, nearest_theirs) > _ON_BALL_M: - ball = "loose" - else: - ball = "ours" if nearest_ours <= nearest_theirs else "theirs" - - return { - "third": "defensive" if progress < third else "middle" if progress < 2 * third else "attacking", - "lane": "left_wing" if left > centre else "right_wing" if left < -centre else "centre", - "restart": restart, - "ball": ball, - "near": f"{sum(d <= NEAR_BALL_M for d in ours)}v{sum(d <= NEAR_BALL_M for d in theirs)}", - } - - -def matches_where(tags: dict[str, str], where: dict[str, set[str]]) -> bool: - """`where` maps a tag to the values it may take (`--where third=defensive ball=theirs,loose`).""" - return all(tags[key] in values for key, values in where.items()) diff --git a/utama_core/shared/pass_and_score_geometry.py b/utama_core/shared/pass_and_score_geometry.py index 6f71325f..d7829a68 100644 --- a/utama_core/shared/pass_and_score_geometry.py +++ b/utama_core/shared/pass_and_score_geometry.py @@ -208,6 +208,12 @@ def has_ball(game: Game, robot_id: int, visual: bool = False, capture_distance: # Measured, not `MAX_ACCELERATION` (4.0): a DecoyOverload carrier in rsim # braked 1.45 -> 0.6m/s in 0.3s (~2.9m/s^2) with the ball on the dribbler. _CARRY_BRAKE_DECEL_MPS2 = 2.5 +# Time between deciding to stop and the carrier starting to brake: command latency +# (sim noise draws 0.03-0.15s per robot) plus the planner's ramp-down. Without it, in +# tournament_20261017_002337 a ~1.2m/s lure kept accelerating for ~0.1s after the cap +# fired, carried 0.5m more instead of the 0.3m predicted, and fouled: 93 of our 171 +# excessive-dribbling fouls, every one from DecoyOverload's lure. +_CARRY_REACTION_S = 0.15 def carry_origin(game: Game, robot_id: int, prev_origin: Optional[Vector2D]) -> Optional[Vector2D]: @@ -226,15 +232,16 @@ def carry_exhausted(game: Game, origin: Optional[Vector2D], stops_after: bool = carrying further risks an excessive-dribbling foul, so release the ball. `stops_after`: the carrier brakes to a halt with the ball still on the dribbler - once this fires (DecoyOverload's lure), so count the stopping distance too -- - without it a ~1.4m/s lure braked another ~0.4m and fouled at 1.01m anyway. Leave + once this fires (DecoyOverload's lure), so count the stopping distance too, the + reaction time (`_CARRY_REACTION_S`) included -- without it a ~1.4m/s lure braked + another ~0.4m and fouled at 1.01m anyway. Leave False for a carrier that kicks while moving (GiveAndGo): counting it there ended carries ~0.5m early and cost give_and_go_solo half its goals in an A/B (tournament_20260924_092119 bisect).""" if origin is None or game.ball is None: return False speed = math.hypot(game.ball.v.x, game.ball.v.y) if stops_after else 0.0 - stopping = speed**2 / (2 * _CARRY_BRAKE_DECEL_MPS2) + stopping = speed * _CARRY_REACTION_S + speed**2 / (2 * _CARRY_BRAKE_DECEL_MPS2) return game.ball.p.to_2d().distance_to(origin) + stopping >= CARRY_LIMIT_M @@ -283,8 +290,9 @@ def intercept_point( def ball_line_receive_point(game: Game, receiver_id: int) -> Optional[Vector2D]: """Where `receiver_id` meets a ball already rolling towards it: its own position - projected onto the ball's actual path. None while the ball is slower than - `_PASS_ROLLING_MPS` or moving away from the receiver.""" + projected onto the ball's actual path, clamped to the field (a path past a goal line + near a corner would otherwise send the receiver out after the ball). None while the + ball is slower than `_PASS_ROLLING_MPS` or moving away from the receiver.""" ball = game.ball velocity = Vector2D(ball.v.x, ball.v.y) speed = velocity.mag() @@ -294,7 +302,7 @@ def ball_line_receive_point(game: Game, receiver_id: int) -> Optional[Vector2D]: along = (game.friendly_robots[receiver_id].p - ball_pos).dot(velocity) / speed if along <= 0.0: return None - return ball_pos + velocity * (along / speed) + return clamp_to_field(ball_pos + velocity * (along / speed), game) def enemy_positions(game: Game) -> list[Vector2D]: diff --git a/utama_core/skills/src/utils/defense_utils.py b/utama_core/skills/src/utils/defense_utils.py index ef98ed4a..01664498 100644 --- a/utama_core/skills/src/utils/defense_utils.py +++ b/utama_core/skills/src/utils/defense_utils.py @@ -1,12 +1,9 @@ -from typing import Dict, List, NamedTuple, Optional, Tuple +from typing import NamedTuple import numpy as np from utama_core.entities.data.vector import Vector2D -from utama_core.entities.game import Ball, Game, Robot -from utama_core.rsoccer_simulator.src.ssl.envs.standard_ssl import SSLStandardEnv - -EPS = 1e-5 +from utama_core.entities.game import Game CURVE_MARGIN = 0.1 CURVE_SHAPE_R = 2.1 @@ -40,65 +37,6 @@ def _defense_geometry(game: Game) -> DefenseGeometry: ) -def align_defenders( - game: Game, - defender_parametric_pos: float, - attacker_position: Vector2D, - attacker_orientation: Optional[float], - env: Optional[SSLStandardEnv], -) -> Tuple[float, float]: - """Calculates the next point on the defense area that the robots should go to defender_position is in terms of t on - the parametric curve.""" - - NO_MOVE_THRES = 1.5 - - # Start by getting the current position of the defenders - defender_pos = calculate_defense_area(game, defender_parametric_pos) - - geo = _defense_geometry(game) - goal_centre_x = geo.goal_x - - if attacker_orientation is None or attacker_orientation == 0: - # In case there is no ball velocity or attackers, use centre of goal - predicted_goal_position = Vector2D(goal_centre_x, 0) - else: - predicted_goal_position = Vector2D( - goal_centre_x, - clamp_to_goal_width( - predict_goal_y_location(game, attacker_position, attacker_orientation), - geo.goal_half_width, - ), - ) - - if env: - env.draw_line([predicted_goal_position, attacker_position], width=1, color="green") - env.draw_line([predicted_goal_position, defender_pos], width=1, color="yellow") - - poly = [] - for t in range(round(1000 * np.pi / 2), round(1000 * 3 * np.pi / 2) + 1): - poly.append(calculate_defense_area(game, clamp_to_parametric(t / 1000))) - env.draw_polygon(poly, width=3) - - goal_to_defender = defender_pos - predicted_goal_position - goal_to_attacker = attacker_position - predicted_goal_position - - side = ccw(goal_to_defender, goal_to_attacker) - angle = ang_between(goal_to_defender, goal_to_attacker) - - if not game.my_team_is_right: - side *= -1 - - if np.rad2deg(angle) > NO_MOVE_THRES: - # Move to the correct side - next_t = step_curve(defender_parametric_pos, side) - # logger.debug( - # f"RAW NEXT {calculate_defense_area(game, clamp_to_parametric(next_t))} {side} {angle}" - # ) - return calculate_defense_area(game, next_t) - else: - return calculate_defense_area(game, defender_parametric_pos) - - def calculate_defense_area(game: Game, t: float) -> Vector2D: """Defenders' path around the goal in the form of a rounded rectangle. @@ -138,103 +76,11 @@ def make_relative_to_goal_centre(goal_centre_x: float, p: Vector2D) -> Vector2D: return Vector2D(goal_centre_x + p.x, p.y) -def predict_goal_y_location(game: Game, shooter_position: Vector2D, orientation: float) -> float: - dx, dy = np.cos(orientation), np.sin(orientation) - gx, _ = game.field.my_goal_line[0] - if dx == 0: - return float("inf") - t = (gx - shooter_position[0]) / dx - return shooter_position[1] + t * dy - - -def to_defense_parametric(game: Game, p: Vector2D) -> float: - """Given a point p on the defenders' parametric curve (as defined by calculate_defense_area), returns the parameter - value t which would give rise to this point.""" - - # Ternary search the paramater, minimising the Euclidean distance between - # the point corresponding to the predicted t and the actual point. We - # could potentially use length along the curve (I think you can get this - # from polar coordinates? But for a semicircle ish curve this works fine) - lo = np.pi / 2 - hi = 3 * np.pi / 2 - EPS = 1e-6 - - while (hi - lo) > EPS: - mi1 = lo + (hi - lo) / 3 - mi2 = lo + 2 * (hi - lo) / 3 - - pred1 = calculate_defense_area(game, mi1) - pred2 = calculate_defense_area(game, mi2) - - dist1 = p.distance_to(pred1) - dist2 = p.distance_to(pred2) - - if dist1 < dist2: - hi = mi2 - else: - lo = mi1 - - t = lo - return clamp_to_parametric(t) - - -def find_likely_enemy_shooter(enemy_robots: Dict[int, Robot], ball: Ball) -> List[Robot]: - unique_shooters: Dict[int, Robot] = {} - - for robot_id, robot in enemy_robots.items(): - if np.linalg.norm(robot.p - ball.p.to_2d()) < 0.2: - unique_shooters[robot_id] = robot - - return list(unique_shooters.values()) - - -def step_curve(t: float, direction: int): - STEP_SIZE = 0.0872665 * 2 - if direction == 0: - return t - return direction * STEP_SIZE + t - - def clamp_to_goal_width(y: float, goal_half_width: float) -> float: return max(min(y, goal_half_width), -goal_half_width) -def clamp_to_parametric(t: float) -> float: - # parametric is between pi /2 and 3pi / 2 - return min(3 * np.pi / 2, max(t, np.pi / 2)) - - -def ccw(v1: Vector2D, v2: Vector2D) -> int: - # 1 if v1 is c-cw of v2, -1 if v1 is cw of v2, 0 if colinear - mag = np.cross(v1, v2) - if abs(mag) < EPS: - return 0 - if mag > 0: - return 1 - else: - return -1 - - # Repeated I will remove -def ang_between(v1: Vector2D, v2: Vector2D) -> float: - res = np.dot(v1, v2) / (v1.mag() * v2.mag()) - if res > 0: - res -= EPS - else: - res += EPS - assert -1 <= res <= 1, f"{v1} {v2} {res}" - - return np.arccos(res) - - -def velocity_to_orientation(p: Tuple[float, float]) -> float: - # Takes a velocity and converts to orientation in radians identical to robot orientation - res = np.atan2(round(p[1], 3), round(p[0], 3)) - if res < 0: - res += 2 * np.pi - return res - - # --------------------------------------------------------------------------- # Shared shadow-geometry helpers (used by both goalkeep and defend_parameter) # --------------------------------------------------------------------------- diff --git a/utama_core/strategy/score_aware_zone_flow.py b/utama_core/strategy/score_aware_zone_flow.py index b7b5aafb..f87a06da 100644 --- a/utama_core/strategy/score_aware_zone_flow.py +++ b/utama_core/strategy/score_aware_zone_flow.py @@ -26,8 +26,8 @@ # # Every existing picker reads possession/ball-zone but none reads the # scoreline itself, even though `game.referee.{yellow,blue}_team.score` is -# already populated in any refereed match (`full_match_tournament.py`/ -# `round_robin.py` already read the same fields to report a match's result). +# already populated in any refereed match (`round_robin.py` reads the same fields to report a +# match's result). # This variant is `_zone_flow_picker`'s allocation with one added axis: late # in a half, shift the *size* of the attack/defense split based on whether # we're ahead or behind, instead of holding the same 3/2-ish split regardless diff --git a/utama_core/team_controller/README.md b/utama_core/team_controller/README.md index 10767049..22db8c51 100644 --- a/utama_core/team_controller/README.md +++ b/utama_core/team_controller/README.md @@ -6,6 +6,16 @@ This is the data flow diagram of what will be received and sent by the Team Cont From the image above, the Vision and Referee use UDP multicast addresses. More information is available on the [SSL RoboCup official page](https://ssl.robocup.org/league-software/#:~:text=Simulation%20Protocol.-,Standard%20Network%20Parameters,-Protocol) +## Real robot path + +The path of one real-match tick, as `StrategyRunner` wires it: the receivers fill one-slot buffers, +the refiners build the `GameFrame`, and the real controller sends the commands over the serial radio. +The robots' feedback comes back the same way and reaches `RobotInfoRefiner`. + +![One real-match tick: receivers, refiners, Strategy and the real robot controller](../../docs/img/team_controller/real_robot_path.png) + +Drawn by `docs/img/team_controller/make_real_robot_path.py`; rerun it when a name in it changes. + ## Controllers This folder contains controller classes for communication with both simulated and real components, including: diff --git a/utama_core/tests/analysis/test_findings.py b/utama_core/tests/analysis/test_findings.py new file mode 100644 index 00000000..217f8afb --- /dev/null +++ b/utama_core/tests/analysis/test_findings.py @@ -0,0 +1,61 @@ +"""The shared Finding format: JSON round trip and the stdout path (no replay, no simulator).""" + +import json + +from utama_core.analysis.findings import ( + Finding, + from_dict, + read_jsonl, + to_dict, + write_jsonl, +) + + +def _finding(**overrides) -> Finding: + fields = dict( + tool="match_timeline", + kind="tactic_thrash", + replay="replays/run/a_vs_b.npz", + side="friendly", + t0=0.0, + t1=480.0, + robots=(4, 5), + value=6.5, + threshold=4.0, + unit="per live min", + message="R4 changed tactic 13 times in 2.0 min of live play (6.50 per min, threshold 4).", + image=None, + ) + fields.update(overrides) + return Finding(**fields) + + +def test_jsonl_round_trip_keeps_every_field(tmp_path): + findings = [ + _finding(), + _finding(kind="zone_share", replay=None, side=None, t0=None, t1=None, robots=(), threshold=None, image="a.png"), + ] + path = tmp_path / "out.jsonl" + write_jsonl(findings, path) + + assert read_jsonl(path) == findings + lines = path.read_text().splitlines() + assert len(lines) == 2 and json.loads(lines[0])["robots"] == [4, 5] + + +def test_to_dict_lists_robots_and_from_dict_restores_a_tuple(): + d = to_dict(_finding(robots=(7,))) + assert d["robots"] == [7] and json.dumps(d) # JSON-serialisable + assert from_dict(d).robots == (7,) + + +def test_write_jsonl_to_stdout_with_dash(capsys): + write_jsonl([_finding()], "-") + out = capsys.readouterr().out + assert json.loads(out.strip())["kind"] == "tactic_thrash" + + +def test_an_empty_list_writes_an_empty_file(tmp_path): + path = tmp_path / "none.jsonl" + write_jsonl([], path) + assert path.read_text() == "" and read_jsonl(path) == [] diff --git a/utama_core/tests/analysis/test_match_timeline.py b/utama_core/tests/analysis/test_match_timeline.py new file mode 100644 index 00000000..c56d9db9 --- /dev/null +++ b/utama_core/tests/analysis/test_match_timeline.py @@ -0,0 +1,237 @@ +import json + +import numpy as np + +from tools.match_timeline import ( + FLIP_BACKS_PER_MIN, + FLIP_S, + MIN_LIVE_S, + OTHER, + SLOTS, + Span, + Window, + flip_backs, + goalkeeper_ids, + group_spans, + lane_summary, + ref_group, + render_timeline, + runs, + spans_from_changes, + switch_times, + tactic_changes, + tactic_colours, + thrash_findings, +) + + +def _row(t, slot, robots, note): + return {"sim_time": t, "tactic_id": slot, "robot_ids": robots, "note": note} + + +def _spans(rows, robot, t0, t1): + changes = tactic_changes(rows, [robot])[robot] + return spans_from_changes(changes, t0, t1) + + +def test_switching_a_to_b_to_a_gives_two_switches_and_spans_tile_the_window(): + rows = [ + _row(1.0, "attack", [1], "ClassA"), + _row(2.0, "defense", [1], "ClassB"), + _row(3.0, "attack", [1], "ClassA"), + ] + spans = _spans(rows, 1, 0.0, 10.0) + assert [s.label for s in spans] == [None, "ClassA", "ClassB", "ClassA"] + assert switch_times(spans) == [2.0, 3.0] + assert lane_summary(spans, 0.0, 10.0)["switches"] == 2 + # Spans tile [0, 10] with no gap and no overlap. + assert spans[0].t0 == 0.0 and spans[-1].t1 == 10.0 + assert all(a.t1 == b.t0 for a, b in zip(spans, spans[1:])) + assert sum(s.t1 - s.t0 for s in spans) == 10.0 + + +def test_switches_per_minute_use_the_window_length(): + rows = [_row(10.0, "attack", [2], "X"), _row(20.0, "defense", [2], "Y"), _row(30.0, "attack", [2], "X")] + spans = _spans(rows, 2, 0.0, 60.0) + summary = lane_summary(spans, 0.0, 60.0) + assert summary["switches"] == 2 + assert summary["per_min"] == 2.0 + assert summary["longest"] == ("X", 30.0) # X held 10-20 s and 30-60 s; the longest is 30-60 s + + +def test_window_clips_spans_and_only_counts_switches_inside_it(): + rows = [_row(1.0, "attack", [1], "A"), _row(5.0, "defense", [1], "B")] + spans = _spans(rows, 1, 3.0, 4.0) + assert spans == [Span(3.0, 4.0, "A")] + assert switch_times(spans) == [] + + +def test_unassigned_gap_between_the_same_tactic_is_not_a_switch(): + rows = [_row(1.0, "attack", [1], "A"), _row(2.0, "attack", [], "released"), _row(3.0, "attack", [1], "A")] + spans = _spans(rows, 1, 0.0, 5.0) + assert [s.label for s in spans] == [None, "A", None, "A"] + assert switch_times(spans) == [] + + +def test_tactic_name_comes_from_the_class_note_and_survives_a_deadline_release(): + rows = [ + _row(1.0, "attack", [1], "LeadAndSupportTactic"), + _row(2.0, "attack", [1], "deadline release after 3.0s committed, ball moved 0.001m"), + ] + spans = _spans(rows, 1, 0.0, 4.0) + assert [s.label for s in spans] == [None, "LeadAndSupportTactic"] + + +def test_referee_reset_clears_every_slot(): + rows = [ + _row(1.0, "attack", [1], "A"), + _row(4.0, "referee reset", [], "referee restart cleared all tactic state"), + ] + spans = _spans(rows, 1, 0.0, 6.0) + assert [s.label for s in spans] == [None, "A", None] + + +def test_robot_in_no_slot_stays_unassigned(): + rows = [_row(1.0, "attack", [2, 3], "A")] + assert _spans(rows, 1, 0.0, 2.0) == [Span(0.0, 2.0, None)] + + +def test_runs_gives_contiguous_true_spans(): + flag = np.array([False, True, True, False, True, True]) + t = np.arange(6, dtype=float) + assert runs(flag, t, "ball") == [Span(1.0, 3.0, "ball"), Span(4.0, 5.0, "ball")] + + +def test_ref_group_and_group_spans(): + assert ref_group("NORMAL_START") == "live play" + assert ref_group("GOAL_BLUE") == "STOP" + assert ref_group("DIRECT_FREE_YELLOW") == "free kick" + assert ref_group("PREPARE_KICKOFF_BLUE") == "kickoff" + assert ref_group("PREPARE_PENALTY_YELLOW") == "penalty" + assert ref_group("BALL_PLACEMENT_BLUE") == "placement" + assert ref_group("HALT") == "HALT" + assert ref_group("-") == "no referee" + groups = ["STOP", "STOP", "live play", "live play"] + t = np.array([0.0, 1.0, 2.0, 3.0]) + assert group_spans(groups, t, 0.0, 4.0) == [Span(0.0, 2.0, "STOP"), Span(2.0, 4.0, "live play")] + + +def test_tactics_beyond_seven_slots_fold_into_other(): + lanes = [[Span(0.0, 10.0 - i, f"T{i}")] for i in range(8)] + colours = tactic_colours(lanes) + assert len(colours) == len(SLOTS) == 7 + assert "T7" not in colours # the eighth, least-used tactic + assert colours["T0"] == SLOTS[0] + assert OTHER not in colours.values() + + +def test_goalkeeper_ids_read_the_highlights_trace(tmp_path): + npz = tmp_path / "a_vs_b.npz" + rows = [ + {"event": "trace", "sim_time": 0.0, "key": "highlights.goalkeeper", "value": {"0": "keeper"}}, + {"event": "trace", "sim_time": 1.0, "key": "other", "value": {"4": "x"}}, + ] + (tmp_path / "a_vs_b.intentions.jsonl").write_text("\n".join(json.dumps(r) for r in rows) + "\n") + assert goalkeeper_ids(npz) == {0} + + +def test_render_writes_a_png_with_every_panel(tmp_path): + t = np.round(np.arange(0.0, 20.0, 0.1), 3) + robot_ids = [1, 2] + lanes = [ + [Span(0.0, 8.0, None), Span(8.0, 20.0, "LeadAndSupportTactic")], + [Span(0.0, 10.0, "ShadowAndMarkTactic"), Span(10.0, 20.0, "LeadAndSupportTactic")], + ] + possession = [[Span(8.0, 9.0, "ball")], []] + ref = [Span(0.0, 5.0, "live play"), Span(5.0, 20.0, "STOP")] + dist = np.column_stack([np.linspace(6, 0.1, t.size), np.linspace(2, 1, t.size)]) + events = [ + {"kind": "GOAL", "t": 12.0, "side": "theirs", "text": "GOAL theirs"}, + {"kind": "FOUL", "t": 6.0, "side": "ours", "rule": "excessive_dribbling"}, + {"kind": "SHOT", "t": 9.0, "side": "ours"}, + ] + out = tmp_path / "timeline.png" + path = render_timeline( + out, + t0=0.0, + t1=20.0, + side="friendly", + robot_ids=robot_ids, + lanes=lanes, + possession=possession, + ref_spans=ref, + t=t, + dist=dist, + events=events, + keepers={1}, + title="test", + ) + assert path == out + assert out.read_bytes()[:8] == b"\x89PNG\r\n\x1a\n" + assert out.stat().st_size > 5_000 + + +# --- findings ------------------------------------------------------------------------------------- + + +def _window(n_flips: int, live_s: float, robot: int = 4, span_s: float = 0.5) -> Window: + """One robot whose tactic alternates A, B, A, ... in `span_s` spans, so the lane has exactly `n_flips` + flip-backs when `span_s` < FLIP_S.""" + spans = [Span(i * span_s, (i + 1) * span_s, "A" if i % 2 == 0 else "B") for i in range(n_flips + 2)] + return Window( + t0=0.0, + t1=spans[-1].t1, + robot_ids=[robot], + lanes=[spans], + possession=[[]], + ref_spans=[], + tw=np.zeros(0), + dist=np.zeros((0, 1)), + keepers=set(), + live_s=live_s, + notes=[], + z={}, + ) + + +def test_a_flip_back_is_a_span_shorter_than_flip_s_between_two_of_one_other_tactic(): + just_under = FLIP_S - 0.01 + spans = [Span(0.0, 5.0, "A"), Span(5.0, 5.0 + just_under, "B"), Span(5.0 + just_under, 9.0, "A")] + assert flip_backs(spans) == [5.0] + held = [Span(0.0, 5.0, "A"), Span(5.0, 5.0 + FLIP_S, "B"), Span(5.0 + FLIP_S, 9.0, "A")] + assert flip_backs(held) == [] # held for FLIP_S: a real switch, not a flip-back + onward = [Span(0.0, 5.0, "A"), Span(5.0, 5.5, "B"), Span(5.5, 9.0, "C")] + assert flip_backs(onward) == [] # A -> B -> C moved on + gap = [Span(0.0, 5.0, "A"), Span(5.0, 5.2, None), Span(5.2, 5.5, "B"), Span(5.5, 9.0, "A")] + assert flip_backs(gap) == [5.2] # an unassigned gap does not hide it + + +def test_thrash_fires_exactly_at_the_threshold(): + # 7 flip-backs over 210 s of live play is exactly FLIP_BACKS_PER_MIN (2.0) per minute. + w = _window(7, live_s=7 * 60.0 / FLIP_BACKS_PER_MIN) + (finding,) = thrash_findings(w, replay="x.npz", side="friendly") + assert finding.kind == "tactic_thrash" and finding.robots == (4,) + assert finding.value == FLIP_BACKS_PER_MIN and finding.threshold == FLIP_BACKS_PER_MIN + + +def test_thrash_does_not_fire_just_under_the_threshold(): + just_under = _window(7, live_s=7 * 60.0 / FLIP_BACKS_PER_MIN + 0.1) + assert thrash_findings(just_under, replay="x.npz", side="friendly") == [] + one_fewer = _window(6, live_s=7 * 60.0 / FLIP_BACKS_PER_MIN) + assert thrash_findings(one_fewer, replay="x.npz", side="friendly") == [] + + +def test_frequent_switches_held_past_flip_s_are_not_thrash(): + # 50 switches a minute, each tactic held FLIP_S: switching on possession, not flipping back. + assert thrash_findings(_window(50, live_s=60.0, span_s=FLIP_S), replay="x.npz", side="friendly") == [] + + +def test_thrash_needs_enough_live_play_for_a_rate(): + assert thrash_findings(_window(50, live_s=MIN_LIVE_S - 10.0), replay="x.npz", side="friendly") == [] + assert len(thrash_findings(_window(50, live_s=MIN_LIVE_S), replay="x.npz", side="friendly")) == 1 + + +def test_thrash_rate_divides_by_live_play_not_by_window_length(): + # The window holds 3 flip-backs and 60 s of live play (its own length, from the lanes, is 2.5 s): 3 per min. + w = _window(3, live_s=60.0) + assert thrash_findings(w, replay="x.npz", side="friendly", threshold=3.0)[0].value == 3.0 diff --git a/utama_core/tests/analysis/test_physics_ranges.py b/utama_core/tests/analysis/test_physics_ranges.py new file mode 100644 index 00000000..d8debcf3 --- /dev/null +++ b/utama_core/tests/analysis/test_physics_ranges.py @@ -0,0 +1,137 @@ +import numpy as np +import pytest + +from utama_core.analysis import physics_ranges as pr + +DT = pr.TICK_S + + +def _rolling_ball(n: int, v0: float, decel: float, start: tuple[float, float] = (-3.0, 0.0)): + """A ball rolling along +x from `start` at `v0`, slowing at a constant `decel` until it stops.""" + t = np.arange(n) * DT + speed = np.maximum(v0 - decel * t, 0.0) + x = start[0] + np.cumsum(speed) * DT + p = np.zeros((n, 3)) + p[:, 0], p[:, 1] = x, start[1] + v = np.zeros((n, 3)) + v[:, 0] = speed + return t, p, v + + +def test_runs_finds_true_ranges_including_the_ends(): + assert pr.runs(np.array([1, 1, 0, 1, 0, 0, 1], dtype=bool)) == [(0, 2), (3, 4), (6, 7)] + assert pr.runs(np.zeros(3, dtype=bool)) == [] + + +def test_window_all_needs_every_tick_of_the_window(): + mask = np.ones(10, dtype=bool) + mask[5] = False + out = pr.window_all(mask, 2) + # t is ok only if [t-2, t+2] avoids tick 5 and the ends + assert out.tolist() == [False, False, True, False, False, False, False, False, False, False] + + +def test_kick_ticks_marks_the_first_tick_of_a_speed_jump_only(): + v = np.zeros((60, 3)) + v[20:, 0] = 4.0 # a kick at tick 20 + kicks = pr.kick_ticks(v) + assert kicks.tolist() == [20] + # a slow push below KICK_SPEED_MPS is not a kick + v[:] = 0.0 + v[20:, 0] = pr.KICK_SPEED_MPS - 0.1 + assert pr.kick_ticks(v).tolist() == [] + + +def test_since_last_counts_seconds_from_the_latest_event(): + out = pr.since_last(np.array([2, 5]), 7) + assert np.isinf(out[:2]).all() + assert out[2:].tolist() == pytest.approx([0, DT, 2 * DT, 0, DT]) + + +def test_fit_segment_recovers_a_constant_deceleration(): + t = np.arange(60) * DT + decel, r2 = pr.fit_segment(t, 2.0 - 0.3 * t) + assert decel == pytest.approx(0.3) + assert r2 == pytest.approx(1.0) + + +def test_free_ball_excludes_near_robots_walls_and_chips(): + ball = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [4.4, 0.0, 0.0], [0.0, 0.0, 0.2]]) + robots = np.full((4, 1, 2), np.nan) + robots[1, 0] = (pr.FREE_ROBOT_M - 0.01, 0.0) # too close + out = pr.free_ball(ball, robots, (9.0, 6.0)) + # free; robot within FREE_ROBOT_M; within WALL_MARGIN_M of the line; in the air + assert out.tolist() == [True, False, False, False] + + +def test_ball_seen_flags_the_converters_held_ball_only(): + p = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.1, 0.0, 0.0]]) + v = np.array([[0.01, 0.0, 0.0], [0.01, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]) + # tick 2: zero velocity and unchanged position (held); tick 3 moved, so it was seen + assert pr.ball_seen(p, v).tolist() == [True, True, False, True] + + +def test_ball_deceleration_recovers_rolling_decel_from_velocity_and_positions(): + n = 300 + t, p, v = _rolling_ball(n, 3.0, 0.4) + free = np.ones(n, dtype=bool) + live = np.ones(n, dtype=bool) + out = pr.ball_deceleration(t, p, v, free, live) + for key in ("by_speed_vel", "by_speed_pos"): + medians = [row["median"] for row in out[key] if row["n"] >= 10] + assert medians and all(m == pytest.approx(0.4, abs=0.02) for m in medians) + (seg,) = out["segments"] # no kick: one segment, until the ball drops below MIN_SPEED_MPS + assert seg["decel"] == pytest.approx(0.4, abs=1e-3) + assert seg["end_mps"] > pr.MIN_SPEED_MPS + + +def test_rolling_segment_starts_slide_s_after_a_kick(): + n = 240 + t, p, v = _rolling_ball(n, 4.0, 0.3) + v[:30] = 0.0 # at rest, then kicked to 4 m/s at tick 30 + v[30:, 0] = np.maximum(4.0 - 0.3 * (t[30:] - t[30]), 0) + free = np.ones(n, dtype=bool) + out = pr.ball_deceleration(t, p, v, free, np.ones(n, dtype=bool)) + (seg,) = out["segments"] + expected_start = 4.0 - 0.3 * round(pr.SLIDE_S / DT) * DT + assert seg["start_mps"] == pytest.approx(expected_start, abs=0.01) + + +def test_robot_motion_percentiles_for_a_known_ramp(): + n = int(30 / DT) + t = np.arange(n) * DT + # accelerate at 2 m/s² to 1.5 m/s, brake at 3 m/s² to 0, repeat + cycle_v = np.r_[np.arange(0, 1.5, 2 * DT), np.full(60, 1.5), np.arange(1.5, 0, -3 * DT), np.zeros(30)] + speed = np.resize(cycle_v, n) + v = np.zeros((n, 1, 2)) + v[:, 0, 0] = speed + p = np.zeros((n, 1, 2)) + p[:, 0, 0] = np.cumsum(speed) * DT + orientation = np.zeros((n, 1)) + orientation[:, 0] = (0.5 * t) % (2 * np.pi) - np.pi # turning at 0.5 rad/s, wrapped + (m,) = pr.robot_motion(p, v, orientation, np.ones(n, dtype=bool)) + assert m["speed_p99"] == pytest.approx(1.5, abs=0.01) + assert m["accel_p95"] == pytest.approx(2.0, abs=0.05) + assert m["brake_p95"] == pytest.approx(3.0, abs=0.05) + assert m["turn_p95"] == pytest.approx(0.5, abs=0.01) # the wrap is unwrapped + + +def test_robot_motion_skips_robots_with_too_little_live_play(): + n = int((pr.MIN_ROBOT_LIVE_S - 1) / DT) + zeros = np.zeros((n, 1, 2)) + assert pr.robot_motion(zeros, zeros, np.zeros((n, 1)), np.ones(n, dtype=bool)) == [None] + + +def test_dropouts_counts_short_robot_gaps_but_not_outages_or_substitutions(): + n = 600 + p = np.zeros((n, 2, 2)) + p[100:110, 0] = np.nan # a 10-tick gap: counted + p[200:210] = np.nan # every robot missing: a tracker outage + p[300:, 1] = np.nan # gone to the end: a substitution + seen = np.ones(n, dtype=bool) + seen[50:53] = False + out = pr.dropouts(seen, p, np.ones(n, dtype=bool)) + assert out["robot"]["run_s"] == pytest.approx([10 * DT]) + assert out["tracker_outage_s"] == pytest.approx(10 * DT, abs=0.01) + assert out["ball"]["run_s"] == pytest.approx([3 * DT]) + assert "ball" not in pr.dropouts(None, p, np.ones(n, dtype=bool)) diff --git a/utama_core/tests/analysis/test_pitch_maps.py b/utama_core/tests/analysis/test_pitch_maps.py new file mode 100644 index 00000000..b7f34e98 --- /dev/null +++ b/utama_core/tests/analysis/test_pitch_maps.py @@ -0,0 +1,200 @@ +"""`tools/pitch_maps.py` on tiny synthetic replays: the side normalisation, possession and the +goalkeeper exclusion, and that each figure is written.""" + +import numpy as np +import pytest + +from tools import pitch_maps as pm +from utama_core.engine.match_stats import _POSSESSION_RADIUS_M +from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.replay.columnar_reader import ColumnarReplay + +NORMAL_START = RefereeCommand.NORMAL_START.value + + +def _replay(friendly_xy, enemy_xy, ball_xy, defends_right=True, command=NORMAL_START, ball_speed=0.0): + """One tick. Friendly ids are 0..k-1, enemy ids start at 10; `defends_right` is the friendly + team's goal (True: it defends +x and attacks toward -x).""" + f = np.array([friendly_xy], dtype=float) + e = np.array([enemy_xy], dtype=float) + k, m = f.shape[1], e.shape[1] + ball_v = np.array([[ball_speed, 0.0, 0.0]]) + return ColumnarReplay( + my_team_is_yellow=True, + my_team_is_right=defends_right, + friendly_ids=np.arange(k), + enemy_ids=np.arange(10, 10 + m), + ts=np.array([0.0]), + friendly_p=f, + friendly_v=np.zeros((1, k, 2)), + friendly_a=np.zeros((1, k, 2)), + friendly_orientation=np.zeros((1, k)), + friendly_has_ball=np.zeros((1, k), dtype=bool), + enemy_p=e, + enemy_v=np.zeros((1, m, 2)), + enemy_a=np.zeros((1, m, 2)), + enemy_orientation=np.zeros((1, m)), + enemy_has_ball=np.zeros((1, m), dtype=bool), + ball_p=np.array([[ball_xy[0], ball_xy[1], 0.0]]), + ball_v=ball_v, + ball_a=np.zeros((1, 3)), + has_referee=np.ones(1, dtype=bool), + referee_command=np.array([command], dtype=np.int8), + stage=np.zeros(1, dtype=np.int8), + designated_position=np.full((1, 2), np.nan), + sparse_referee={}, + side_is_right=np.array([defends_right]), + ) + + +def test_side_normalisation_maps_the_attacking_team_to_plus_x(): + # A robot at x=+3 for a team attacking right -> left maps to x=-3 (and y is rotated too). + assert pm.to_attacking_frame(3.0, 1.0, defends_right=True) == (-3.0, -1.0) + # A team attacking left -> right is already in the frame. + assert pm.to_attacking_frame(3.0, 1.0, defends_right=False) == (3.0, 1.0) + assert pm.attack_sign(True) == -1.0 and pm.attack_sign(False) == 1.0 + + +def test_possession_needs_the_ball_near_a_robot_and_slow(): + near = np.array([[0.0, 0.0]]) + ball = np.array([[_POSSESSION_RADIUS_M * 0.5, 0.0, 0.0]]) + slow = np.zeros((1, 3)) + fast = np.array([[3.0, 0.0, 0.0]]) + live = np.array([True]) + assert pm.possession_codes(near, np.empty((1, 0, 2)), ball, slow, live).tolist() == [1] + assert pm.possession_codes(np.empty((1, 0, 2)), near, ball, slow, live).tolist() == [-1] + assert pm.possession_codes(near, np.empty((1, 0, 2)), ball, fast, live).tolist() == [0] + assert pm.possession_codes(near, np.empty((1, 0, 2)), ball, slow, ~live).tolist() == [0] + far = np.array([[_POSSESSION_RADIUS_M * 2, 0.0, 0.0]]) + assert pm.possession_codes(near, np.empty((1, 0, 2)), far, slow, live).tolist() == [0] + + +def test_keeper_is_the_robot_in_its_own_box(): + # Own goal is at -x in the common frame; robot 1 is the one inside the box. + norm = np.array([[[-1.0, 0.0], [-4.2, 0.1], [2.0, 0.0]]]) + assert pm.keeper_slot(norm) == 1 + + +def test_build_views_normalises_positions_shots_and_fouls(): + # Friendly (config_a) defends +x, so it attacks toward -x and its frame is rotated. + replay = _replay( + friendly_xy=[(4.0, 0.0), (3.0, 1.0), (-1.0, 0.0)], # keeper at +4, outfield at (3,1) and (-1,0) + enemy_xy=[(0.5, 0.0)], + ball_xy=(3.0, 1.0), + ) + shots = [ + {"side": "friendly", "t": 0.0, "xy": [-3.0, 1.0], "scored": True}, + {"side": "enemy", "t": 0.0, "xy": [0.5, 0.0], "scored": False}, + ] + fouls = [ + {"side": "friendly", "rule": "crashing", "sim_time": 0.0, "ball_xy": [3.0, 1.0]}, + {"side": "friendly", "rule": "out_of_bounds", "sim_time": 0.0, "ball_xy": [4.5, 0.0]}, + {"side": "enemy", "rule": "crashing", "sim_time": 0.0, "ball_xy": [0.5, 0.0]}, + ] + friendly, enemy = pm.build_views(replay, {"friendly": "A", "enemy": "B"}, shots, fouls) + + assert friendly.strategy == "A" and enemy.strategy == "B" + # The keeper (robot 0) is left out; the others are rotated into the common frame. + assert sorted(friendly.pos_robot.tolist()) == [1, 2] + by_robot = dict(zip(friendly.pos_robot.tolist(), friendly.pos_x.tolist())) + assert by_robot == pytest.approx({1: -3.0, 2: 1.0}) + assert friendly.pos_in.tolist() == [True, True] # the team has the ball, whichever robot holds it + + own = [s for s in friendly.shots if s["own"]] + faced = [s for s in friendly.shots if not s["own"]] + assert len(own) == 1 and len(faced) == 1 + assert (own[0]["x"], own[0]["y"], own[0]["scored"]) == (3.0, -1.0, True) + assert (faced[0]["x"], faced[0]["y"], faced[0]["scored"]) == (-0.5, 0.0, False) + + # Out of bounds is not a foul; the friendly foul is in the common frame, and enemy fouls stay with enemy. + assert friendly.fouls == [{"x": -3.0, "y": -1.0, "rule": "crashing"}] + assert enemy.fouls == [{"x": 0.5, "y": 0.0, "rule": "crashing"}] + + +def test_enemy_view_uses_the_opposite_frame(): + # The enemy defends -x (friendly defends +x), so it attacks +x and is not rotated. Its robot + # at (-4, 0) is inside its own box (the keeper); its outfield robot at (-3, 1) holds the ball. + replay = _replay(friendly_xy=[(4.0, 0.0)], enemy_xy=[(-4.0, 0.0), (-3.0, 1.0)], ball_xy=(-3.0, 1.0)) + _, enemy = pm.build_views(replay, {"friendly": "A", "enemy": "B"}, [], []) + assert enemy.pos_robot.tolist() == [11] + assert enemy.pos_x.tolist() == pytest.approx([-3.0]) + assert enemy.pos_y.tolist() == pytest.approx([1.0]) + assert enemy.pos_in.tolist() == [True] + + +def test_position_grid_shares_sum_to_one_and_means_are_per_robot(): + views = [ + pm.MatchView( + strategy="A", + pos_x=np.array([0.5, 0.5, -2.0]), + pos_y=np.array([0.0, 0.0, 1.0]), + pos_in=np.array([True, True, False]), + pos_robot=np.array([1, 1, 2]), + ) + ] + grid = pm.position_grid(views, "A", in_possession=True) + assert grid["share"].sum() == pytest.approx(1.0) + assert grid["means"] == [(0.5, 0.0)] + assert pm.position_grid(views, "A", in_possession=False)["means"] == [(-2.0, 1.0)] + + +def _views(): + names = ["build_a_kernel_strategy", "build_b_kernel_strategy"] + shots = [{"x": 2.0, "y": 0.5, "scored": True, "own": True}, {"x": -1.0, "y": 0.0, "scored": False, "own": False}] + fouls = [{"x": 1.0, "y": -1.0, "rule": "crashing"}, {"x": -2.0, "y": 1.0, "rule": "double_touch"}] + views = [] + for name in names: + views.append( + pm.MatchView( + strategy=name, + pos_x=np.array([0.0, 1.0, -1.5]), + pos_y=np.array([0.0, 0.5, -1.0]), + pos_in=np.array([True, False, True]), + pos_robot=np.array([1, 1, 2]), + shots=list(shots), + fouls=list(fouls), + ) + ) + return names, {n: n.removeprefix("build_") for n in names}, views + + +@pytest.mark.parametrize("which", ["positions_attack", "positions_defence", "shots", "fouls_map"]) +def test_each_figure_is_written(tmp_path, which): + order, labels, views = _views() + path = tmp_path / f"{which}.png" + if which == "shots": + pm.plot_shots(views, order, labels, path) + elif which == "fouls_map": + pm.plot_fouls(views, order, labels, path) + else: + pm.plot_positions(views, order, labels, path, in_possession=which == "positions_attack") + assert path.read_bytes()[:8] == b"\x89PNG\r\n\x1a\n" + + +def test_rule_colours_take_fixed_slots_in_rank_order(): + assert pm.rule_colours(["crashing", "double_touch"]) == {"crashing": pm.sr.SLOTS[0], "double_touch": pm.sr.SLOTS[1]} + + +def test_zone_findings_are_six_descriptive_shares_per_strategy_in_the_pitch_zone_names(): + # Common frame: the strategy attacks +x, thirds at x = -1.5 and +1.5 m (docs/pitch_zones.md). + view = pm.MatchView( + strategy="build_a_kernel_strategy", + pos_x=np.array([-2.0, 0.0, 2.0, 2.5]), + pos_y=np.zeros(4), + pos_in=np.array([True, True, True, False]), # three in possession, one out + pos_robot=np.array([1, 2, 3, 1]), + ) + findings = pm.zone_findings([view], ["build_a_kernel_strategy"], {"in possession": "a.png"}) + + assert len(findings) == 6 + assert all(f.kind == "zone_share" and f.threshold is None and f.replay is None for f in findings) + + def find(label: str, zone: str): + return next(f for f in findings if f"{label} samples in the {zone}" in f.message) + + for zone in ("defensive third", "middle third", "attacking third"): + assert find("in possession", zone).value == pytest.approx(1 / 3) + assert find("in possession", "attacking third").image == "a.png" + assert find("out of possession", "attacking third").value == 1.0 + assert find("out of possession", "defensive third").value == 0.0 + assert pm.top_rules(_views()[2], k=1) == ["crashing"] diff --git a/utama_core/tests/analysis/test_plan_snapshot.py b/utama_core/tests/analysis/test_plan_snapshot.py new file mode 100644 index 00000000..9dc818b1 --- /dev/null +++ b/utama_core/tests/analysis/test_plan_snapshot.py @@ -0,0 +1,34 @@ +"""tools/plan_snapshot.py's drawing function, on the kick-off state of +test_fpp_inside_ball_keep_out.py (no simulator).""" + +import math + +import numpy as np + +from utama_core.analysis.plan_snapshot import plan_snapshot +from utama_core.config.referee_constants import BALL_KEEP_OUT_DISTANCE +from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.tests.fixtures.game_builder import build_game, make_ball, make_robot + + +def test_snapshot_draws_the_planners_carrot_and_flags_a_robot_inside_keep_out(tmp_path): + us = (0.762, -0.225) + round_the_circle = (0.8 * math.cos(math.radians(-76)), 0.8 * math.sin(math.radians(-76))) + game = build_game( + {4: make_robot(4, is_friendly=True, x=us[0], y=us[1])}, + {0: make_robot(0, is_friendly=False, x=0.12, y=0.0)}, + make_ball(0.0, 0.0), + my_team_is_right=False, + my_team_is_yellow=False, + referee_command=RefereeCommand.PREPARE_KICKOFF_YELLOW, + ) + out = tmp_path / "kickoff.png" + + snap = plan_snapshot(game, 4, round_the_circle, out, title="kick-off") + + assert out.read_bytes()[:8] == b"\x89PNG\r\n\x1a\n" + assert snap.path == out + assert snap.inside_keep_out is True + assert snap.keeps_off_ball is True + assert snap.carrot.shape == (2,) and np.all(np.isfinite(snap.carrot)) + assert snap.robot_to_ball < BALL_KEEP_OUT_DISTANCE diff --git a/utama_core/tests/analysis/test_plan_trace.py b/utama_core/tests/analysis/test_plan_trace.py new file mode 100644 index 00000000..dc1d5415 --- /dev/null +++ b/utama_core/tests/analysis/test_plan_trace.py @@ -0,0 +1,184 @@ +"""tools/plan_trace.py's trace function on synthetic games over time (no replay, no simulator).""" + +import math + +import pytest + +from utama_core.analysis.plan_trace import ( + PARKED_MIN_S, + TraceRow, + _referee_spans, + carrot_kind, +) +from utama_core.analysis.plan_trace import plan_findings as plan_trace_findings +from utama_core.analysis.plan_trace import plan_trace, sample_times, short_carrot_label +from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.tests.fixtures.game_builder import build_game, make_ball, make_robot + +BALL = (0.0, -1.5) +ENEMY = (2.0, 1.5) + + +def _game(t: float, referee: RefereeCommand = RefereeCommand.NORMAL_START): + """Our robot 4 drives along the x axis at 0.5 m/s from x = -1; the rest of the field is still.""" + return build_game( + {4: make_robot(4, is_friendly=True, x=-1.0 + 0.5 * t, y=0.0)}, + {0: make_robot(0, is_friendly=False, x=ENEMY[0], y=ENEMY[1])}, + make_ball(*BALL), + my_team_is_right=True, + my_team_is_yellow=True, + referee_command=referee, + ts=t, + ) + + +def test_sample_times_keeps_the_end_even_off_the_grid(): + assert sample_times(0.0, 0.2, 0.1) == [0.0, 0.1, 0.2] + assert sample_times(0.0, 0.25, 0.1) == [0.0, 0.1, 0.2, 0.25] + assert sample_times(3.0, 3.0, 0.1) == [3.0] + with pytest.raises(ValueError): + sample_times(0.0, 1.0, 0.0) + with pytest.raises(ValueError): + sample_times(1.0, 0.0, 0.1) + + +def test_trace_rows_and_png_for_a_fixed_target(tmp_path): + out = tmp_path / "trace.png" + + trace = plan_trace(_game, 4, 0.0, 0.2, out, step=0.1, target=(1.0, 0.0), title="synthetic") + + assert out.read_bytes()[:8] == b"\x89PNG\r\n\x1a\n" + assert trace.path == out + assert [r.t for r in trace.rows] == [0.0, 0.1, 0.2] + assert trace.target_note.startswith("target: (1.00, 0.00) fixed") + for r in trace.rows: + assert r.robot == pytest.approx((-1.0 + 0.5 * r.t, 0.0)) + assert r.target == (1.0, 0.0) + assert r.robot_to_ball == pytest.approx(math.dist(r.robot, BALL)) + assert r.robot_to_carrot == pytest.approx(math.dist(r.robot, r.carrot)) + assert r.robot_to_target == pytest.approx(math.dist(r.robot, r.target)) + assert r.carrot_offset >= 0.0 + assert r.referee == "NORMAL_START" + assert r.ball == BALL + assert trace.rows[0].speed is None + assert trace.rows[1].speed == pytest.approx(0.5) + assert trace.rows[2].speed == pytest.approx(0.5) + + +def test_stand_in_target_is_the_robot_lookahead_seconds_later(tmp_path): + trace = plan_trace(_game, 4, 0.0, 0.2, tmp_path / "standin.png", step=0.1, lookahead=0.3) + + assert trace.target_note == "target: stand-in, robot at t+0.3 s" + for r in trace.rows: + assert r.target == pytest.approx((-1.0 + 0.5 * (r.t + 0.3), 0.0)) + + +def test_referee_changes_show_as_separate_spans(tmp_path): + def game_at(t): + command = RefereeCommand.STOP if t < 0.15 else RefereeCommand.NORMAL_START + return _game(t, command) + + trace = plan_trace(game_at, 4, 0.0, 0.3, tmp_path / "ref.png", step=0.1, target=(1.0, 0.0)) + + assert [r.referee for r in trace.rows] == ["STOP", "STOP", "NORMAL_START", "NORMAL_START"] + spans = _referee_spans(list(trace.rows), 0.3) + assert [(name, round(start, 3), round(end, 3)) for name, start, end in spans] == [ + ("STOP", 0.0, 0.2), + ("NORMAL_START", 0.2, 0.3), + ] + + +def _row(carrot, robot=(0.762, -0.225), ball=(0.0, 0.0)): + return TraceRow( + t=258.0, + robot=robot, + target=(-1.0, -0.3), + carrot=carrot, + robot_to_carrot=math.dist(robot, carrot), + robot_to_target=math.dist(robot, (-1.0, -0.3)), + carrot_offset=0.0, + robot_to_ball=math.dist(robot, ball), + clearance=0.216, + speed=None, + ball=ball, + referee="PREPARE_KICKOFF_YELLOW", + ) + + +def test_a_short_carrot_out_past_the_keep_out_is_a_push_out_not_a_parked_robot(): + """counter_flow_vs_tiki_taka_plus at 258 s: robot 0.795 m from the ball, carrot 0.85 m from it.""" + assert "pushed out of the keep-out" in short_carrot_label(_row(carrot=(0.811, -0.236))) + # The same short carrot left inside the circle is a stall. + assert "parked" in short_carrot_label(_row(carrot=(0.75, -0.22))) + + +# --- findings ------------------------------------------------------------------------------------- + + +def _short_row(t: float, *, robot_to_ball: float, carrot_to_ball: float, robot_to_target: float = 2.0): + """A short-carrot tick: the carrot collapsed onto the robot (0.05 m) while the target is 2 m away. + The ball is at the origin, the robot on the x axis at `robot_to_ball`, the carrot at `carrot_to_ball`.""" + return TraceRow( + t=t, + robot=(robot_to_ball, 0.0), + target=(3.0, 0.0), + carrot=(carrot_to_ball, 0.0), + robot_to_carrot=0.05, + robot_to_target=robot_to_target, + carrot_offset=0.0, + robot_to_ball=robot_to_ball, + clearance=1.0, + speed=0.0, + ball=(0.0, 0.0), + referee="NORMAL_START", + ) + + +def _far_row(t: float): + """A tick whose carrot is well clear of the robot: not a short carrot, so it breaks a run.""" + return TraceRow( + t=t, + robot=(2.0, 0.0), + target=(3.0, 0.0), + carrot=(2.5, 0.0), + robot_to_carrot=0.5, + robot_to_target=1.0, + carrot_offset=0.0, + robot_to_ball=2.0, + clearance=1.0, + speed=0.5, + ball=(0.0, 0.0), + referee="NORMAL_START", + ) + + +def test_pushed_out_is_a_short_carrot_that_starts_inside_the_keep_out_and_ends_outside(): + assert carrot_kind(_short_row(0.0, robot_to_ball=0.79, carrot_to_ball=0.9)) == "pushed_out" + assert carrot_kind(_short_row(0.0, robot_to_ball=0.5, carrot_to_ball=0.9)) == "pushed_out" + + +def test_parked_is_a_short_carrot_that_is_not_pushed_across_the_keep_out_edge(): + # Robot exactly on the 0.8 m edge is not inside it (strict inequality): parked. + assert carrot_kind(_short_row(0.0, robot_to_ball=0.8, carrot_to_ball=0.9)) == "parked" + # Robot already outside, carrot also outside: parked. + assert carrot_kind(_short_row(0.0, robot_to_ball=0.81, carrot_to_ball=0.9)) == "parked" + # Robot inside and carrot inside too: nothing was pushed out, so parked. + assert carrot_kind(_short_row(0.0, robot_to_ball=0.79, carrot_to_ball=0.7)) == "parked" + + +def test_a_run_of_one_second_or_more_is_one_finding_per_span(): + pushed = [_short_row(round(i * 0.1, 6), robot_to_ball=0.7, carrot_to_ball=0.9) for i in range(11)] # 1.0 s + parked = [_short_row(round((11 + i) * 0.1, 6), robot_to_ball=1.5, carrot_to_ball=1.6) for i in range(11)] + findings = plan_trace_findings(pushed + parked, 4, replay="x.npz", side="friendly") + + assert [f.kind for f in findings] == ["pushed_out", "parked"] + assert findings[0].robots == (4,) and findings[0].value == 1.0 and findings[0].threshold == PARKED_MIN_S + assert findings[0].t0 == 0.0 and findings[0].t1 == pytest.approx(1.0) + assert findings[1].t0 == pytest.approx(1.1) + + +def test_a_run_under_a_second_and_a_gap_inside_a_run_give_no_finding(): + short = [_short_row(round(i * 0.1, 6), robot_to_ball=1.5, carrot_to_ball=1.6) for i in range(9)] # 0.8 s + gap = [_far_row(0.9)] + rows = short + gap + [_short_row(round(1.0 + i * 0.1, 6), robot_to_ball=1.5, carrot_to_ball=1.6) for i in range(9)] + assert plan_trace_findings(rows, 4, replay=None, side="friendly") == [] diff --git a/utama_core/tests/analysis/test_ratings.py b/utama_core/tests/analysis/test_ratings.py new file mode 100644 index 00000000..84abf28a --- /dev/null +++ b/utama_core/tests/analysis/test_ratings.py @@ -0,0 +1,71 @@ +import numpy as np +import pytest + +from utama_core.analysis import ratings + + +def test_no_matches_keeps_the_prior(): + r = ratings.fit([], names=["x", "y"]) + assert r.strength("x") == pytest.approx((0.0, ratings.PRIOR_SD)) + assert r.p_better("x", "y") == pytest.approx(0.5) + + +def test_many_matches_recover_true_strengths(): + """With plenty of noisy matches the posterior mean lands on the true margins and its SD + shrinks like 1/sqrt(matches).""" + rng = np.random.default_rng(0) + true = {"x": 1.0, "y": 0.0, "z": -1.0} + pairs = [("x", "y"), ("y", "z"), ("z", "x")] + matches = [(a, b, true[a] - true[b] + rng.normal(0, 1.6)) for _ in range(400) for a, b in pairs] + r = ratings.fit(matches) + for n, s in true.items(): + assert r.strength(n)[0] == pytest.approx(s, abs=0.1) + assert r.noise_sd == pytest.approx(1.6, abs=0.1) + assert r.p_better("x", "z") > 0.999 + + +def test_margin_is_shrunk_toward_zero_by_the_prior(): + """One 3-0 win is not a 3-goal strength gap: the prior pulls it in.""" + r = ratings.fit([("x", "y", 3.0)], noise_sd=1.6) + m, sd = r.diff("x", "y") + assert 0 < m < 3.0 + expected = 2 * ratings.PRIOR_SD**2 / (2 * ratings.PRIOR_SD**2 + 1.6**2) * 3.0 + assert m == pytest.approx(expected) + + +def test_noise_sd_cannot_collapse_on_few_matches(): + """Two matches that fit perfectly still leave noise_sd near its prior.""" + r = ratings.fit([("x", "y", 1.0), ("x", "y", 1.0)]) + assert r.noise_sd > 0.8 * ratings.NOISE_SD + + +def test_common_opponents_compare_two_strategies_that_never_met(): + """x beats o by more than y does, so x is likely stronger than y without x v y matches.""" + matches = [("x", "o", 2.0)] * 20 + [("y", "o", 0.0)] * 20 + r = ratings.fit(matches) + assert r.p_better("x", "y") > 0.99 + + +def test_identical_paired_results_pin_the_difference_near_zero(): + """The case that forced the paired test: a copy of the champion matches its results exactly + against every opponent, and a few lucky head-to-head wins must not make it 'better'.""" + m, sd = ratings.paired_difference([0.0] * 24, [1, 4, 1, 0], noise_sd=1.9) + assert abs(m) < 0.3 + assert ratings.p_positive(m, sd) < 0.9 + + +def test_paired_spread_is_shrunk_toward_no_cancelling(): + """Two equal diffs have zero spread, but the variance stays near 2 * noise_sd^2 * N0 / (N0 + 1).""" + m, sd = ratings.paired_difference([1.0, 1.0], [], noise_sd=1.9) + var_d = ratings.PAIRED_PRIOR_N * 2 * 1.9**2 / (ratings.PAIRED_PRIOR_N + 1) + assert sd == pytest.approx((1 / (2 * ratings.PRIOR_SD**2) + 2 / var_d) ** -0.5) + + +def test_consistent_paired_gains_are_decisive(): + m, sd = ratings.paired_difference([1.0, 2.0, 1.0, 1.5] * 6, [], noise_sd=1.9) + assert ratings.p_positive(m, sd) > 0.99 + + +def test_no_evidence_is_a_coin_flip(): + m, sd = ratings.paired_difference([], [], noise_sd=1.9) + assert ratings.p_positive(m, sd) == pytest.approx(0.5) diff --git a/utama_core/tests/analysis/test_realism.py b/utama_core/tests/analysis/test_realism.py new file mode 100644 index 00000000..b125d016 --- /dev/null +++ b/utama_core/tests/analysis/test_realism.py @@ -0,0 +1,340 @@ +from types import SimpleNamespace as NS + +import numpy as np +import pytest + +from utama_core.analysis.realism import ( + passes, + reaction, + scorecard, + shot_outcomes, + shots, + xg, +) +from utama_core.entities.referee.referee_command import RefereeCommand as C +from utama_core.entities.referee.stage import Stage + +_DT = 1 / 60 +_KEEPER = 0 # both sides' keeper id + + +def _robot(x, y): + return NS(p=NS(x=x, y=y)) + + +def _frame(ts, bx, by, vx=0.0, vy=0.0, friendly=None, enemy=None, cmd=C.NORMAL_START, score=(0, 0)): + """Friendly is yellow and attacks the +x goal.""" + referee = NS( + referee_command=cmd, + stage=Stage.NORMAL_FIRST_HALF, + yellow_team=NS(score=score[0], goalkeeper=_KEEPER), + blue_team=NS(score=score[1], goalkeeper=_KEEPER), + game_events=[], + ) + return NS( + ts=ts, + ball=NS(p=NS(x=bx, y=by), v=NS(x=vx, y=vy)), + friendly_robots=friendly or {}, + enemy_robots=enemy or {}, + my_team_is_right=False, + my_team_is_yellow=True, + referee=referee, + ) + + +def _kick(x0, y0, vx, vy, rest_s=0.3, roll_s=1.0, goal_at_s=None, **kw): + """A ball at rest at (x0, y0), then kicked at (vx, vy) and rolling without slowing; the + friendly score rises `goal_at_s` after the kick if given.""" + frames, t = [], 0.0 + while t < rest_s: + frames.append(_frame(t, x0, y0, **kw)) + t += _DT + t_kick = t + while t < rest_s + roll_s: + dt = t - t_kick + score = (1, 0) if goal_at_s is not None and dt >= goal_at_s else (0, 0) + frames.append(_frame(t, x0 + vx * dt, y0 + vy * dt, vx, vy, score=score, **kw)) + t += _DT + return frames, t_kick + + +def test_a_hard_kick_at_the_goal_mouth_is_one_shot_from_where_the_ball_rested(): + frames, t_kick = _kick(2.0, 0.0, 4.0, 0.0) + found = shots.scan(frames)["shots"] + assert len(found) == 1 + s = found[0] + assert s["side"] == "friendly" and s["context"] == "open_play" + assert s["t"] == pytest.approx(t_kick, abs=1e-3) + assert s["distance_m"] == pytest.approx(2.5) + assert s["peak"] == pytest.approx(4.0) + + +def test_a_kick_just_outside_the_on_target_margin_or_too_slow_is_not_a_shot(): + # from x = 2.0 the line reaches the goal line after 2.5 m: aim at y = 0.66 vs 0.5 + 0.15 + wide, _ = _kick(2.0, 0.0, 4.0, 4.0 * 0.66 / 2.5) + inside, _ = _kick(2.0, 0.0, 4.0, 4.0 * 0.64 / 2.5) + slow, _ = _kick(2.0, 0.0, shots.KICK_MPS - 0.1, 0.0) + assert shots.scan(wide)["shots"] == [] + assert len(shots.scan(inside)["shots"]) == 1 + assert shots.scan(slow)["shots"] == [] + + +def test_a_ball_kicked_on_inside_the_goal_after_crossing_the_line_is_not_a_shot(): + # real logs: after a goal, play runs on until a human confirms it, and the ball bounces in the net + frames, _ = _kick(4.6, 0.1, 4.0, 0.0) # at rest 0.1 m past the +x goal line, then driven deeper + assert shots.scan(frames)["shots"] == [] + + +def test_a_kick_in_a_stoppage_is_not_a_shot(): + frames, _ = _kick(2.0, 0.0, 4.0, 0.0, cmd=C.STOP) + assert shots.scan(frames)["shots"] == [] + + +def test_a_sim_goal_goes_to_the_shot_before_the_ball_entered_the_goal(): + # 2.5 m at 4 m/s: in the goal 0.625 s after the kick; our referee awards 0.3 s later + frames, _ = _kick(2.0, 0.0, 4.0, 0.0, goal_at_s=0.68) + scanned = shots.scan(frames) + links = shots.link_goals(scanned) + assert links["link_source"] == "ball_in_goal" + assert links["unshot_goals"] == {"friendly": 0, "enemy": 0} + assert scanned["shots"][0]["scored"] + shot_outcomes.follow(frames, scanned["shots"]) + assert scanned["shots"][0]["outcome"] == "goal" + + +def _shot(t, x, y, side="friendly"): + return {"side": side, "t": t, "raw_x": x, "raw_y": y, "scored": False} + + +def _event(kind, t, kick, origin=None): + return {"type": kind, "t": t, "goal_sign": 1.0, "kick": kick, "message": None, "origin": origin} + + +def _real(shot_list, entry_t, possible_t, goal_t, kick): + return { + "shots": shot_list, + "in_goal_events": [(entry_t, "friendly")], + "game_events": [_event("POSSIBLE_GOAL", possible_t, kick), _event("GOAL", goal_t, kick)], + "score_events": [(goal_t, "friendly", False)], + } + + +def test_a_late_operator_confirmed_goal_goes_to_the_shot_at_the_autorefs_kick_location(): + near, later = _shot(100.0, 3.0, 0.2), _shot(104.0, 1.0, -1.0) + # the operator accepts 44.5 s after POSSIBLE_GOAL; the later shot elsewhere is not it + links = shots.link_goals(_real([near, later], 105.0, 105.5, 150.0, (3.0, 0.25))) + assert near["scored"] and not later["scored"] and "kick_mismatch" not in near + assert links["link_source"] == "game_events" + assert links["unshot_goals"]["friendly"] == 0 + assert links["confirm_lags"] == [44.5] + + +def test_a_hand_entered_possible_goal_still_finds_the_ball_entry_up_to_the_manual_window(): + shot = _shot(100.0, 3.0, 0.2) + entry = 101.0 + in_window = _real([dict(shot)], entry, entry + shots.MANUAL_GOAL_S, entry + shots.MANUAL_GOAL_S + 5, (3.0, 0.2)) + too_late = _real([dict(shot)], entry, entry + shots.MANUAL_GOAL_S + 1, entry + shots.MANUAL_GOAL_S + 6, (3.0, 0.2)) + shots.link_goals(in_window) + links = shots.link_goals(too_late) + assert in_window["shots"][0]["scored"] + # without the entry the crossing time is the event's, far past the shot: missed + assert not too_late["shots"][0]["scored"] + assert links["missed"][0]["entry_found"] is False + + +def test_with_no_shot_at_the_kick_location_the_sides_latest_shot_scores_flagged_as_a_mismatch(): + first, last = _shot(100.0, 3.0, 0.2), _shot(103.0, 2.0, 1.0) + links = shots.link_goals(_real([first, last], 104.0, 104.5, 110.0, (4.2, -0.3))) + assert last["scored"] and last["kick_mismatch"] and not first["scored"] + assert links["missed"] == [] + + +def test_a_score_rise_with_no_goal_event_on_that_goal_is_unlinked(): + s = _shot(100.0, 3.0, 0.2) + scanned = _real([s], 101.0, 101.5, 120.0, (3.0, 0.2)) + scanned["score_events"] = [(130.0, "friendly", False)] # more than 1 s from the GOAL event + links = shots.link_goals(scanned) + assert not s["scored"] and links["unshot_goals"]["friendly"] == 1 + + +def test_the_first_robot_the_shot_reaches_names_its_outcome(): + keeper = {_KEEPER: _robot(4.0, 0.0)} + frames, _ = _kick(2.0, 0.0, 4.0, 0.0, enemy=keeper) + scanned = shots.scan(frames) + shot_outcomes.follow(frames, scanned["shots"]) + s = scanned["shots"][0] + assert s["outcome"] == "keeper" + assert s["keeper_depth_m"] == pytest.approx(0.5) + + +def test_a_shot_inside_the_margin_but_outside_the_post_ends_wide(): + frames, _ = _kick(2.0, 0.0, 4.0, 4.0 * 0.6 / 2.5) + scanned = shots.scan(frames) + shot_outcomes.follow(frames, scanned["shots"]) + assert scanned["shots"][0]["outcome"] == "wide" + + +def test_attempts_drop_passes_and_kicks_from_beyond_six_metres(): + assert shot_outcomes.is_attempt({"outcome": "keeper", "distance_m": shot_outcomes.MAX_ATTEMPT_M}) + assert not shot_outcomes.is_attempt({"outcome": "keeper", "distance_m": shot_outcomes.MAX_ATTEMPT_M + 0.01}) + assert not shot_outcomes.is_attempt({"outcome": "teammate", "distance_m": 2.0}) + + +def test_the_logistic_fit_recovers_known_probabilities_and_survives_a_round_trip(): + rng = np.random.default_rng(1) + X = rng.normal(size=(20000, len(xg.FEATURES))) + true_w = np.array([-1.0, 0.8, -0.5] + [0.0] * (len(xg.FEATURES) - 2)) + p = 1 / (1 + np.exp(-(np.c_[np.ones(len(X)), X] @ true_w))) + y = (rng.random(len(X)) < p).astype(float) + model = xg.Logit().fit(X, y) + assert np.abs(model.predict(X) - p).max() < 0.05 + again = xg.Logit.from_dict(model.to_dict()) + np.testing.assert_allclose(again.predict(X[:10]), model.predict(X[:10])) + with pytest.raises(ValueError): + xg.Logit.from_dict({**model.to_dict(), "features": ["log_dist"]}) + + +def _attempt(scored, outcome="keeper", distance=3.0): + return { + "distance_m": distance, + "angle_deg": 10.0, + "open_goal": 0.5, + "og_keeper": 0.7, + "keeper_in_place": True, + "peak": 4.0, + "context": "open_play", + "scored": scored, + "outcome": "goal" if scored else outcome, + "speed_1m": None, + } + + +def test_scorecard_rates_pool_matches_and_drop_short_ones(): + model = xg.Logit().fit(np.array([xg.row(_attempt(False)), xg.row(_attempt(True))]), np.array([0.0, 1.0])) + long_match = {"live_s": 120.0, "restarts": 4, "shots": [_attempt(True), _attempt(False)]} + other = {"live_s": 240.0, "restarts": 2, "shots": [_attempt(False, "wide")]} + short = {"live_s": xg.MIN_LIVE_S - 1, "restarts": 9, "shots": [_attempt(True)] * 5} + long_match["shots"].append(_attempt(False, "teammate")) # a pass: not an attempt + rows = scorecard.rows(scorecard.usable([long_match, other, short]), model, n_boot=20) + # 3 attempts over 6 live minutes for 2 teams + assert rows["attempts per team per live minute"]["value"] == pytest.approx(3 / 12) + assert rows["outcome: wide, post or crossed"]["value"] == pytest.approx(1 / 3) + assert rows["outcome: wide"]["value"] == pytest.approx(1 / 3) + assert rows["restarts per match"]["value"] == pytest.approx(3.0) + + +def _outcome(frames): + scanned = shots.scan(frames) + shot_outcomes.follow(frames, scanned["shots"]) + return scanned["shots"][0] + + +def _aimed_at(y): + # from x = 2.0 the line reaches the goal line after 2.5 m; the post is at y = 0.5 + return _kick(2.0, 0.0, 4.0, 4.0 * y / 2.5)[0] + + +def test_where_an_unscored_shot_crosses_the_line_splits_wide_post_and_crossed_no_goal(): + wide = _outcome(_aimed_at(0.6)) + assert wide["outcome"] == "wide" and wide["miss_m"] == pytest.approx(0.1, abs=0.01) + assert _outcome(_aimed_at(0.5 + shot_outcomes.POST_BAND_M - 0.005))["outcome"] == "post" + assert _outcome(_aimed_at(0.5 + shot_outcomes.POST_BAND_M + 0.01))["outcome"] == "wide" + crossed = _outcome(_aimed_at(0.3)) + assert crossed["outcome"] == "crossed_no_goal" and crossed["cross_y"] == pytest.approx(0.3, abs=0.02) + + +def test_a_ball_bouncing_back_off_the_post_is_post(): + frames, _ = _kick(2.0, 0.0, 4.0, 4.0 * 0.5 / 2.5) + frames = [f for f in frames if f.ball.p.x <= 4.47] # up to 3 cm short of the post + last = frames[-1] + x, y = last.ball.p.x, last.ball.p.y + for k in range(1, 30): + frames.append(_frame(last.ts + k * _DT, x - 3.0 * k * _DT, y, -3.0, 0.0)) + assert _outcome(frames)["outcome"] == "post" + + +def _keeper_frames(move_at_s, vy_after, x=4.4, y0=0.4): + """Shot straight down y = 0 from x = 2.0 at 4 m/s; the keeper stands off the line at + (x, y0) and from `move_at_s` after the kick moves across at `vy_after`.""" + frames, t_kick = _kick(2.0, 0.0, 4.0, 0.0) + for f in frames: + dt = max(0.0, f.ts - t_kick - move_at_s) + f.enemy_robots = {_KEEPER: _robot(x, y0 + vy_after * dt)} + return frames + + +def _react(frames): + scanned = shots.scan(frames) + reaction.measure(frames, scanned["shots"]) + return scanned["shots"][0] + + +def test_the_keeper_reaction_is_the_first_velocity_change_toward_the_shot_line(): + s = _react(_keeper_frames(0.2, -1.0)) + assert s["keeper_toward_s"] == pytest.approx(0.2, abs=0.04) + assert s["keeper_react_s"] == pytest.approx(0.2, abs=0.04) + assert s["keeper_window_s"] == pytest.approx(0.6, abs=0.03) # the ball passes x = 4.4 + assert s["keeper_line_m"] == pytest.approx(0.4) + + +def test_a_keeper_moving_away_from_the_line_reacts_but_not_toward_it_and_a_still_one_not_at_all(): + away = _react(_keeper_frames(0.2, 1.0)) + assert away["keeper_react_s"] is not None and away["keeper_toward_s"] is None + assert _react(_keeper_frames(0.0, 0.0))["keeper_react_s"] is None + + +def _passes(frames): + scanned = shots.scan(frames) + assert scanned["shots"] == [] + return passes.follow(frames, []) + + +def _pass_frames(enemy=None, speed=3.0): + """Friendly robot 0 at (0, -0.1) kicks the ball from (0, 0) up the y axis; friendly robot 1 + waits at (0.1, 2.0).""" + friendly = {0: _robot(0.0, -0.1), 1: _robot(0.1, 2.0)} + return _kick(0.0, 0.0, 0.0, speed, friendly=friendly, enemy=enemy, roll_s=2.0)[0] + + +def test_a_kick_toward_a_teammate_is_a_pass_with_its_line_offset_and_completion(): + (p,) = _passes(_pass_frames()) + assert p["side"] == "friendly" and p["completed"] + assert p["offset_m"] == pytest.approx(0.1, abs=1e-3) + + +def test_a_pass_softer_than_a_shot_kick_still_counts_but_one_below_the_pass_threshold_does_not(): + (p,) = _passes(_pass_frames(speed=passes.KICK_MPS + 0.1)) + assert p["completed"] + assert _passes(_pass_frames(speed=passes.KICK_MPS - 0.1)) == [] + + +def test_a_kick_that_is_a_shot_is_not_also_a_pass(): + frames = _pass_frames() + kick_t = frames[passes.kicks(frames)[0][0]].ts + assert passes.follow(frames, [kick_t - 0.05]) == [] + + +def test_a_pass_an_opponent_reaches_first_is_incomplete_and_a_kick_to_nobody_is_no_pass(): + (p,) = _passes(_pass_frames(enemy={5: _robot(0.05, 1.0)})) + assert not p["completed"] + lonely, _ = _kick(0.0, 0.0, 0.0, 3.0, friendly={0: _robot(0.0, -0.1)}) + assert _passes(lonely) == [] + + +def test_reaction_rows_use_only_shots_that_leave_time_and_count_no_reaction_in_time_as_none(): + model = xg.Logit().fit(np.array([xg.row(_attempt(False)), xg.row(_attempt(True))]), np.array([0.0, 1.0])) + react = dict(keeper_window_s=0.6, keeper_line_m=0.3, keeper_react_s=0.2) + attempts = [ + {**_attempt(False), **react, "keeper_toward_s": 0.2}, + {**_attempt(False), **react, "keeper_toward_s": None}, + {**_attempt(False), **react, "keeper_window_s": 0.2, "keeper_toward_s": 0.1}, # no time + ] + match = {"match": "m", "live_s": 120.0, "restarts": 0, "shots": attempts} + rows = scorecard.rows(scorecard.usable([match]), model, n_boot=5) + assert rows["keeper moves toward the shot line within 0.4 s, share"]["value"] == pytest.approx(0.5) + assert rows["keeper moves toward the shot line, median (s)"]["value"] == pytest.approx(0.2) + + +def test_the_broken_tracker_match_is_excluded(): + (stem,) = scorecard.EXCLUDED_MATCHES + assert scorecard.usable([{"match": stem, "live_s": 600.0, "shots": []}]) == [] diff --git a/utama_core/tests/analysis/test_restart_picture.py b/utama_core/tests/analysis/test_restart_picture.py new file mode 100644 index 00000000..d941705a --- /dev/null +++ b/utama_core/tests/analysis/test_restart_picture.py @@ -0,0 +1,201 @@ +"""tools/restart_picture.py's capture of the referee override's commanded targets (no simulator).""" + +import json +import math +from pathlib import Path + +import pytest + +from utama_core.analysis.plan_snapshot import build_game as build_game_with_referee +from utama_core.analysis.restart_picture import ( + ALERT, + plan_findings, + plan_restart, + render_restart, +) +from utama_core.config.referee_constants import BALL_KEEP_OUT_DISTANCE +from utama_core.custom_referee import actions +from utama_core.entities.data.referee import RefereeData +from utama_core.entities.game.team_info import TeamInfo +from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.entities.referee.stage import Stage +from utama_core.tests.fixtures.game_builder import build_game, make_ball, make_robot + + +def _referee(goalkeeper: int, command: RefereeCommand) -> RefereeData: + """Referee data with our (yellow) goalkeeper set, as the replay's own team data carries it.""" + return RefereeData( + source_identifier=None, + time_sent=0.0, + time_received=0.0, + referee_command=command, + referee_command_timestamp=0.0, + stage=Stage.NORMAL_FIRST_HALF, + stage_time_left=0.0, + blue_team=TeamInfo(name="blue", score=0, goalkeeper=0), + yellow_team=TeamInfo(name="yellow", score=0, goalkeeper=goalkeeper), + ) + + +def _robot_plan(plan, robot_id): + return next(r for r in plan.robots if r.robot_id == robot_id) + + +def test_stop_moves_a_robot_inside_keep_out_to_the_edge_and_captures_its_target(): + ball = (0.0, 0.0) + game = build_game( + { + 4: make_robot(4, is_friendly=True, x=0.5, y=0.0), # 0.5 m from the ball: inside the keep-out + 5: make_robot(5, is_friendly=True, x=-3.0, y=2.0), + }, + {0: make_robot(0, is_friendly=False, x=2.0, y=-2.0)}, + make_ball(*ball), + my_team_is_right=True, + my_team_is_yellow=True, + referee_command=RefereeCommand.STOP, + ) + real_move = actions.move + + plan = plan_restart(game, RefereeCommand.STOP) + + target = _robot_plan(plan, 4).target + assert target is not None and _robot_plan(plan, 4).kind == "move" + assert math.hypot(target[0] - ball[0], target[1] - ball[1]) >= BALL_KEEP_OUT_DISTANCE - 1e-6 + assert not any("keep-out" in f for f in _robot_plan(plan, 4).flags) + assert plan.side == "both" + assert actions.move is real_move # the recorders are removed again after the call + + +def test_our_kickoff_gives_the_lowest_id_non_keeper_the_kicker_role_and_keeps_the_keeper_out(): + # Our side is yellow and defends the left goal (my_team_is_right=False): own half is x < 0. + game = build_game_with_referee( + { + 1: make_robot(1, is_friendly=True, x=-4.0, y=0.0), # goalkeeper + 2: make_robot(2, is_friendly=True, x=-1.0, y=1.0), + 3: make_robot(3, is_friendly=True, x=-1.5, y=-1.0), + }, + {0: make_robot(0, is_friendly=False, x=2.0, y=0.0)}, + make_ball(0.0, 0.0), + my_team_is_right=False, + my_team_is_yellow=True, + referee=_referee(goalkeeper=1, command=RefereeCommand.PREPARE_KICKOFF_YELLOW), + ) + + plan = plan_restart(game, RefereeCommand.PREPARE_KICKOFF_YELLOW, sim_time=0.0) + + assert plan.side == "ours" + kicker = _robot_plan(plan, 2) + assert kicker.role == "kicker" and kicker.kind == "move" + assert math.hypot(kicker.target[0] - 0.0, kicker.target[1]) < 0.3 # approaches the ball at centre + assert _robot_plan(plan, 1).role == "keeper" and _robot_plan(plan, 1).kind == "none" + assert _robot_plan(plan, 3).role is None and _robot_plan(plan, 3).kind == "move" + + +def test_plan_renders_a_png(tmp_path): + game = build_game( + {4: make_robot(4, is_friendly=True, x=0.5, y=0.0)}, + {0: make_robot(0, is_friendly=False, x=2.0, y=-2.0)}, + make_ball(0.0, 0.0), + my_team_is_right=True, + my_team_is_yellow=True, + referee_command=RefereeCommand.STOP, + ) + plan = plan_restart(game, RefereeCommand.STOP, sim_time=1.5) + out = render_restart(plan, tmp_path / "stop.png", title="stop") + + assert out.read_bytes()[:8] == b"\x89PNG\r\n\x1a\n" + assert plan.flagged == () and ALERT.startswith("#") + + +def test_a_non_restart_command_is_refused(): + game = build_game( + {4: make_robot(4, is_friendly=True, x=0.5, y=0.0)}, + {}, + make_ball(0.0, 0.0), + referee_command=RefereeCommand.NORMAL_START, + ) + try: + plan_restart(game, RefereeCommand.NORMAL_START) + except ValueError: + return + raise AssertionError("NORMAL_START should be refused") + + +# --- findings ------------------------------------------------------------------------------------- + + +def _stop_game_with_commanded_targets(monkeypatch, targets: dict, *, my_team_is_right: bool = True): + """A STOP game whose override commands each robot in `targets` to the given point (the step's `move` is + what the recorder captures), so a target can sit inside the keep-out on purpose.""" + from utama_core.engine.referee_override import RefereeOverride + from utama_core.entities.data.vector import Vector2D + + def fake_tick(self, game, motion_controller, command): + for rid, (x, y) in targets.items(): + actions.move(game, motion_controller, rid, Vector2D(x, y), 0.0) + return {rid: actions.empty_command(False) for rid in targets} + + monkeypatch.setattr(RefereeOverride, "tick", fake_tick) + return build_game( + {rid: make_robot(rid, is_friendly=True, x=2.0 + rid * 0.1, y=2.0) for rid in targets}, + {}, + make_ball(0.0, 0.0), + my_team_is_right=my_team_is_right, + my_team_is_yellow=True, + referee_command=RefereeCommand.STOP, + ) + + +def test_targets_inside_the_keep_out_are_findings_and_the_one_cm_deadband_is_its_own_kind(monkeypatch): + game = _stop_game_with_commanded_targets( + monkeypatch, + { + 7: (0.5, 0.0), # 0.50 m from the ball: a robot sent into the circle + 8: (0.0, 0.795), # inside the keep-out by less than 1 cm: the stop deadband + 9: (0.785, 0.0), # 1.5 cm inside: not the deadband + }, + ) + plan = plan_restart(game, RefereeCommand.STOP, sim_time=3.0) + findings = plan_findings(plan, Path("match.npz"), "friendly", 3.0) + + by_robot = {f.robots[0]: f for f in findings} + assert by_robot[7].kind == "target_in_keep_out" and by_robot[7].value == pytest.approx(0.5) + assert by_robot[8].kind == "keep_out_short" and by_robot[8].value == pytest.approx(0.795) + assert by_robot[9].kind == "target_in_keep_out" + for f in findings: + assert f.tool == "restart_picture" and f.threshold == BALL_KEEP_OUT_DISTANCE and f.unit == "m" + assert f.side == "friendly" and f.t0 == f.t1 == 3.0 and f.replay == "match.npz" + assert f.message.startswith(f"R{f.robots[0]} is commanded to") + + +def test_a_target_in_the_opponent_box_reports_how_far_it_must_move_out(monkeypatch): + # We defend the left goal, so the opponent's box is at +x (x > 3.5 m on the standard field); a target inside + # it, with its keep distance, is projected out, and the finding's value is that push. + game = _stop_game_with_commanded_targets(monkeypatch, {7: (4.2, 0.0)}, my_team_is_right=False) + (finding,) = plan_findings(plan_restart(game, RefereeCommand.STOP), Path("m.npz"), "friendly", 0.0) + assert finding.kind == "target_in_defense_area" and finding.threshold is None + assert finding.value > 0.0 + + +def test_restart_occurrences_keep_every_repeat_and_restart_commands_keep_the_first(tmp_path): + from utama_core.analysis.restart_picture import ( + restart_commands, + restart_occurrences, + ) + + rows = [ + {"event": "referee", "sim_time": 1.0, "command": "STOP"}, + {"event": "referee", "sim_time": 2.0, "command": "NORMAL_START"}, # not a restart the override handles + {"event": "referee", "sim_time": 5.0, "command": "STOP"}, + {"event": "referee", "sim_time": 9.0, "command": "DIRECT_FREE_BLUE"}, + ] + replay = tmp_path / "match.npz" + replay.write_bytes(b"") + (tmp_path / "match.intentions.jsonl").write_text("\n".join(json.dumps(r) for r in rows) + "\n") + + assert restart_occurrences(replay) == [ + (1.0, RefereeCommand.STOP), + (5.0, RefereeCommand.STOP), + (9.0, RefereeCommand.DIRECT_FREE_BLUE), + ] + assert [t for t, _ in restart_commands(replay)] == [1.0, 9.0] diff --git a/utama_core/tests/analysis/test_signal_report.py b/utama_core/tests/analysis/test_signal_report.py index 3700329c..6807f9b3 100644 --- a/utama_core/tests/analysis/test_signal_report.py +++ b/utama_core/tests/analysis/test_signal_report.py @@ -1,10 +1,4 @@ -from tools.signal_report import _ranked, build - - -def test_rank_colours_point_green_where_the_value_usually_helps(): - assert _ranked([1.0, 2.0, 3.0], 1) == [0.0, 0.5, 1.0] - assert _ranked([1.0, 2.0, 3.0], -1) == [1.0, 0.5, 0.0] # e.g. danger conceded: less is better - assert _ranked([None, 2.0, 2.0], 1) == [0.5, 0.5, 0.5] # missing, and ties, sit in the middle +from tools.signal_report import build, markdown def _result(a: str, b: str, score_a: int, score_b: int) -> dict: @@ -44,10 +38,15 @@ def test_build_writes_every_figure(tmp_path): table = build(summary, losses, tmp_path) assert sorted(p.name for p in tmp_path.iterdir()) == [ - "attack_defense.png", "ball_losses.png", "fouls.png", + "results.png", "shot_quality.png", - "signal_heatmap.png", + "signals.png", ] assert table["build_x_kernel_strategy"]["chances"]["regain_to_shot"] == 1.0 + # Shots faced are the other side's shots: x (config_a, friendly) faced the enemy's 1, y faced 2. + assert table["build_x_kernel_strategy"]["shots_faced"] == 1 + assert table["build_y_kernel_strategy"]["shots_faced"] == 2 + rows = markdown(table).splitlines() + assert rows[2].startswith("| `x` | 3.00 | 1.0 | 0.0 | 2.0 | 1.0 | 50% | 0% |") diff --git a/utama_core/tests/analysis/test_triage.py b/utama_core/tests/analysis/test_triage.py new file mode 100644 index 00000000..073593ff --- /dev/null +++ b/utama_core/tests/analysis/test_triage.py @@ -0,0 +1,145 @@ +"""tools/triage.py without replays: which matches are chosen (each strategy's worst N as config_a, by goal +difference), the picture ranking and cap, and the index's text. Match files are empty stubs and their scores +come from a fake `final_score`, so nothing is simulated or drawn from a real replay.""" + +from pathlib import Path + +import pytest + +from tools import triage +from utama_core.analysis import matches as match_files +from utama_core.analysis.findings import Finding + +A, B, C = "build_alpha_kernel_strategy", "build_beta_kernel_strategy", "build_gamma_kernel_strategy" + + +def _finding(kind, value, *, robots=(1,), t0=0.0, t1=10.0, message=None): + return Finding( + tool="test", + kind=kind, + replay=None, + side="friendly", + t0=t0, + t1=t1, + robots=robots, + value=value, + threshold=None, + unit="s", + message=message or f"{kind} {value}", + ) + + +@pytest.fixture +def run_dir(tmp_path, monkeypatch): + """Stub match files (alpha as config_a in three matches, as config_b in one) and their scores.""" + d = tmp_path / "run" + d.mkdir() + scores = { + "alpha_vs_beta": (0, 3), # alpha GD -3 + "alpha_vs_gamma_s1": (2, 2), # alpha GD 0 + "alpha_vs_gamma_s2": (1, 0), # alpha GD +1 + "beta_vs_alpha": (4, 0), # alpha is config_b here, GD -4 for alpha: must not count as alpha's config_a match + "beta_vs_gamma": (1, 1), + } + for stem in scores: + (d / f"{stem}.npz").write_bytes(b"") + monkeypatch.setattr(match_files, "final_score", lambda npz: scores[npz.name.removesuffix(".npz")]) + return d + + +def test_worst_n_by_goal_difference_as_config_a(run_dir): + chosen = triage.select_matches(run_dir, None, 2) + alpha = [m.npz.name for m in chosen if m.a == "alpha"] + assert alpha == ["alpha_vs_beta.npz", "alpha_vs_gamma_s1.npz"] # GD -3 then 0; the +1 match is the best and cut + + +def test_strategy_filter_takes_full_or_short_names(run_dir): + chosen = triage.select_matches(run_dir, {A}, 5) + assert {m.a for m in chosen} == {"alpha"} + assert len(chosen) == 3 + chosen = triage.select_matches(run_dir, {"beta"}, 5) + assert [m.npz.name for m in chosen] == ["beta_vs_gamma.npz", "beta_vs_alpha.npz"] # GD 0 then +4 + + +def test_every_strategy_gets_its_own_worst_matches(run_dir): + chosen = triage.select_matches(run_dir, None, 1) + assert sorted((m.a, m.npz.name) for m in chosen) == [ + ("alpha", "alpha_vs_beta.npz"), + ("beta", "beta_vs_gamma.npz"), + ] + + +def _timeline(t0: float = 0.0) -> triage.TimelinePicture: + return triage.TimelinePicture(Path("m.npz"), t0, t0 + 40.0) + + +def test_rank_puts_kind_priority_first_then_value(): + items = [ + triage.Item(_finding("tactic_thrash", 50.0), request=_timeline(1.0)), + triage.Item(_finding("stall", 3.0), request=_timeline(2.0)), + triage.Item(_finding("tactic_thrash", 9.0), request=_timeline(3.0)), + triage.Item(_finding("keep_out_short", 1.0), request=triage.RestartPicture(Path("r.npz"), 2.0, None)), + triage.Item(_finding("target_in_keep_out", 0.1), request=triage.RestartPicture(Path("r.npz"), 1.0, None)), + triage.Item(_finding("stall", 9.0)), # no picture source: never ranked + ] + ranked = triage.rank(items) + assert [it.finding.kind for it in ranked] == [ + "stall", + "target_in_keep_out", + "tactic_thrash", + "tactic_thrash", + "keep_out_short", + ] + assert [it.finding.value for it in ranked if it.finding.kind == "tactic_thrash"] == [50.0, 9.0] + + +def test_draw_pictures_caps_at_k_and_shares_one_file(tmp_path, monkeypatch): + drawn = [] + + def fake_build(npz, t0, t1, side, out): + drawn.append((t0, t1)) + Path(out).write_bytes(b"\x89PNG stub") + + monkeypatch.setattr(triage.match_timeline, "build", fake_build) + items = [triage.Item(_finding("stall", 3.0), request=_timeline())] + items += [triage.Item(_finding("tactic_thrash", 2.0 + i), request=_timeline()) for i in range(3)] + items += [triage.Item(_finding("tactic_thrash", 1.0), request=_timeline(100.0))] + pictures = tmp_path / "pictures" + pictures.mkdir() + shown = triage.draw_pictures(items, pictures, max_pictures=2) + assert len(shown) == 2 + pictured = [it for it in items if it.image is not None] + assert [it.finding.kind for it in pictured] == ["stall", "tactic_thrash"] + assert len({it.image for it in pictured}) == 1 and drawn == [(0.0, 40.0)] # one window, drawn once + + +def test_index_has_each_pictured_finding_message_and_path(run_dir, tmp_path): + out = tmp_path / "out" + out.mkdir() + picture = out / "01_stall.png" + picture.write_bytes(b"x") + stall = triage.Item( + _finding("stall", 3.0, message="R1 oscillates with the ball frozen for 3.0 s."), + request=_timeline(773.0), + window=(773.0, 796.0), + image=picture, + ) + quiet = triage.Item( + _finding("tactic_thrash", 4.0, message="R2 thrashes, no picture.") + ) # not pictured: counted only + m = triage.select_matches(run_dir, {A}, 1)[0] + report = triage.MatchReport(m, [stall, quiet], notes=[]) + index = triage.write_index(out, run_dir, [report], max_pictures=8, restarts=False).read_text() + assert "R1 oscillates with the ball frozen for 3.0 s." in index + assert str(picture.resolve()) in index + assert "Picture of 773.0-796.0 s" in index + assert "R2 thrashes, no picture." not in index # only pictured findings are described + assert "| stall | 1 |" in index and "| tactic_thrash | 1 |" in index # every finding is counted + assert "alpha_vs_beta: 0-3 (GD -3)" in index + + +def test_index_says_so_when_a_strategy_has_no_matches(tmp_path): + out = tmp_path / "out" + out.mkdir() + index = triage.write_index(out, tmp_path, [], max_pictures=8, restarts=False).read_text() + assert "No matches with this strategy" in index diff --git a/utama_core/tests/custom_referee/test_custom_referee.py b/utama_core/tests/custom_referee/test_custom_referee.py index 55af9455..e1a6cde6 100644 --- a/utama_core/tests/custom_referee/test_custom_referee.py +++ b/utama_core/tests/custom_referee/test_custom_referee.py @@ -1133,7 +1133,7 @@ def test_simulation_oob_auto_advances_to_ball_placement_then_direct_free(self): assert data.next_command == RefereeCommand.NORMAL_START def test_simulation_stop_advances_after_timeout_even_if_a_robot_never_clears(self): - """Found live in a full_match_tournament.py replay + """Found live in a round-robin replay (counter_press_vs_tiki_taka_RK.pkl, 2026-09-01): an out-of-bounds STOP queued a DIRECT_FREE_BLUE restart, but one robot parked itself oscillating just inside _BALL_CLEAR_DIST of the stationary ball and diff --git a/utama_core/tests/custom_referee/test_free_kick_taken.py b/utama_core/tests/custom_referee/test_free_kick_taken.py new file mode 100644 index 00000000..8367dc89 --- /dev/null +++ b/utama_core/tests/custom_referee/test_free_kick_taken.py @@ -0,0 +1,55 @@ +"""SSL rulebook §5.4 / §5.3.3: a free kick is taken, and the ball in play, once the ball has moved +0.05 m. A kick during the `DIRECT_FREE_*` preparation used to go unnoticed until the referee's own +auto-advance, so a defender the ball rolled onto was still charged with keep_out. +""" + +from utama_core.custom_referee.state_machine import GameStateMachine +from utama_core.entities.data.vector import Vector3D +from utama_core.entities.game.ball import Ball +from utama_core.entities.game.game_frame import GameFrame +from utama_core.entities.referee.referee_command import RefereeCommand + + +def _frame(ts: float, ball_xy: tuple[float, float]) -> GameFrame: + zero = Vector3D(0, 0, 0) + return GameFrame( + ts=ts, + my_team_is_yellow=True, + my_team_is_right=False, + friendly_robots={}, + enemy_robots={}, + ball=Ball(p=Vector3D(ball_xy[0], ball_xy[1], 0), v=zero, a=zero), + referee=None, + ) + + +def _machine_in_free_kick(ball_xy: tuple[float, float], t: float = 0.0) -> GameFrame: + sm = GameStateMachine(half_duration_seconds=300.0, kickoff_team="yellow", n_robots_yellow=3, n_robots_blue=3) + sm.seed_clock(0.0) + sm.set_command(RefereeCommand.DIRECT_FREE_YELLOW, t) + sm.step(t, None, _frame(t, ball_xy)) + return sm + + +def test_ball_moved_4cm_during_free_kick_is_still_the_restart(): + sm = _machine_in_free_kick((1.0, 0.5)) + result = sm.step(0.1, None, _frame(0.1, (1.04, 0.5))) + assert result.referee_command == RefereeCommand.DIRECT_FREE_YELLOW + + +def test_ball_moved_6cm_during_free_kick_is_live_play(): + sm = _machine_in_free_kick((1.0, 0.5)) + result = sm.step(0.1, None, _frame(0.1, (1.06, 0.5))) + assert result.referee_command == RefereeCommand.NORMAL_START + + +def test_free_kick_origin_is_where_the_ball_was_when_this_free_kick_began(): + """A later free kick measures its 0.05 m from the ball's spot at its own start, not the + spot of an earlier one.""" + sm = _machine_in_free_kick((1.0, 0.5)) + sm.set_command(RefereeCommand.STOP, 0.2) + sm.step(0.2, None, _frame(0.2, (1.5, 0.5))) + sm.set_command(RefereeCommand.DIRECT_FREE_BLUE, 0.3) + sm.step(0.3, None, _frame(0.3, (1.5, 0.5))) + result = sm.step(0.4, None, _frame(0.4, (1.54, 0.5))) + assert result.referee_command == RefereeCommand.DIRECT_FREE_BLUE diff --git a/utama_core/tests/dashboard/test_tournament_view.py b/utama_core/tests/dashboard/test_tournament_view.py index b7b9e15f..0eaec05d 100644 --- a/utama_core/tests/dashboard/test_tournament_view.py +++ b/utama_core/tests/dashboard/test_tournament_view.py @@ -32,7 +32,7 @@ def test_each_match_carries_the_path_of_its_replay_when_the_file_exists(tmp_path assert [r["replay"] for r in loaded["results"]] == ["tournament_1/low_block_vs_three_slot.npz", None] -def test_full_match_tournament_replays_are_found_by_their_side_and_kickoff_tag(tmp_path, monkeypatch): +def test_replays_with_side_and_kickoff_tags_are_found(tmp_path, monkeypatch): run = _write_run( tmp_path, [ diff --git a/utama_core/tests/engine/test_all_tactics.py b/utama_core/tests/engine/test_all_tactics.py index c764e70d..8f72cd86 100644 --- a/utama_core/tests/engine/test_all_tactics.py +++ b/utama_core/tests/engine/test_all_tactics.py @@ -353,7 +353,7 @@ def spy_block_attacker(*args, **kwargs): def test_press_and_contain_picks_the_closest_robot_as_presser(runner_factory): - """Found live (competitive `full_match_tournament.py` stuck-window sweep, + """Found live (competitive round-robin stuck-window sweep, 2026-09-01): the scheduler hands `tick()` a numerically-sorted `robot_ids` tuple (`Strategy._run_step`'s `tuple(sorted(robot_ids))`), but `presser_id = robot_ids[0]` treated that as "closest to the ball" — diff --git a/utama_core/tests/engine/test_check_strategy_branch.py b/utama_core/tests/engine/test_check_strategy_branch.py index c13a2743..b334ba0e 100644 --- a/utama_core/tests/engine/test_check_strategy_branch.py +++ b/utama_core/tests/engine/test_check_strategy_branch.py @@ -43,7 +43,7 @@ def test_strategy_modules_tests_and_catalog_are_allowed(): "path", [ "utama_core/engine/strategy.py", - "utama_core/scenario_bench/scenario_scorer.py", + "utama_core/analysis/goal_ledger.py", "tools/evaluation/match.py", "tools/check_strategy_branch.py", ".github/workflows/ci.yml", diff --git a/utama_core/tests/engine/test_evaluate.py b/utama_core/tests/engine/test_evaluate.py new file mode 100644 index 00000000..60dbd2f4 --- /dev/null +++ b/utama_core/tests/engine/test_evaluate.py @@ -0,0 +1,80 @@ +from tools.evaluation import evaluate +from utama_core.replay import eval_ledger + + +def test_round_fixtures_pair_candidate_and_champion_on_the_same_opponents_and_seed(): + fixtures = evaluate.round_fixtures(["c"], "ch", ["o1", "o2"], 1) + assert set(fixtures) == {("c", "o1", 1), ("c", "o2", 1), ("c", "ch", 1), ("ch", "o1", 1), ("ch", "o2", 1)} + + +def test_round_fixtures_swap_sides_in_even_rounds(): + assert ("o1", "c", 2) in evaluate.round_fixtures(["c"], "ch", ["o1"], 2) + assert ("ch", "c", 2) in evaluate.round_fixtures(["c"], "ch", ["o1"], 2) + + +def test_two_candidates_share_the_champions_fixtures(): + fixtures = evaluate.round_fixtures(["c1", "c2"], "ch", ["o1"], 1) + assert fixtures.count(("ch", "o1", 1)) == 1 + assert ("c1", "c2", 1) not in fixtures + + +def test_verdict_thresholds_are_inclusive_and_cap_rounds(): + assert evaluate.verdict(evaluate.PROMOTE, 1, 6) == "better" + assert evaluate.verdict(evaluate.DROP, 1, 6) == "worse" + assert evaluate.verdict(0.5, 5, 6) is None + assert evaluate.verdict(0.5, 6, 6) == "undecided" + + +def test_ledger_round_trip_keeps_the_last_entry_per_fixture_and_skips_torn_lines(tmp_path): + path = tmp_path / "ledger.jsonl" + base = {"env": "e", "a": "x", "b": "y", "a_fp": "1", "b_fp": "2", "seed": 1} + eval_ledger.append([{**base, "fixture": "f", "score_a": 1}, {**base, "env": "other", "fixture": "g"}], path) + eval_ledger.append([{**base, "fixture": "f", "score_a": 2}], path) + with open(path, "a") as f: + f.write('{"env": "e", "fixt') + loaded = eval_ledger.load("e", path) + assert list(loaded) == ["f"] and loaded["f"]["score_a"] == 2 + + +def test_fixture_id_depends_on_seed_and_code(): + f = eval_ledger.fixture_id + assert f("e", "x", "1", "y", "2", 1) != f("e", "x", "1", "y", "2", 2) + assert f("e", "x", "1", "y", "2", 1) != f("e", "x", "9", "y", "2", 1) + + +def test_per_opponent_compares_only_matches_with_the_same_seed_and_colour(): + """The champion's seed-1 match as config_a pairs with the candidate's seed-1 match as + config_a; the candidate's seed-1 match as config_b has no partner and is left out.""" + + def e(a, b, sa, sb, seed): + return {"a": a, "b": b, "a_fp": "f", "b_fp": "f", "score_a": sa, "score_b": sb, "seed": seed} + + ledger = { + "1": e("c", "o", 2, 0, 1), + "2": e("o", "c", 5, 0, 1), + "3": e("ch", "o", 1, 0, 1), + "4": e("c", "ch", 0, 3, 1), + } + rows = evaluate._per_opponent(ledger, {"c": "f", "o": "f", "ch": "f"}, "c", "ch", ["o"]) + assert rows["o"] == {"candidate": 2, "champion": 1, "matches": 1} + assert rows["head-to-head"]["candidate"] == -3 + + +def test_paired_differences_use_same_seed_and_colour_and_orient_head_to_head(): + def e(a, b, sa, sb, seed): + return {"a": a, "b": b, "a_fp": "f", "b_fp": "f", "score_a": sa, "score_b": sb, "seed": seed} + + ledger = { + "1": e("c", "o", 2, 0, 1), # pairs with 3 + "2": e("o", "c", 5, 0, 1), # candidate as config_b: no champion partner + "3": e("ch", "o", 1, 0, 1), + "4": e("ch", "c", 3, 0, 2), # head-to-head, candidate as config_b + } + diffs, h2h = evaluate.paired(ledger, {"c": "f", "o": "f", "ch": "f"}, "c", "ch", ["o"]) + assert diffs == [2 - 1] + assert h2h == [-3] + + +def test_a_verdict_after_round_k_ignores_later_seeds_already_in_the_ledger(): + ledger = {"1": {"seed": 1}, "2": {"seed": 2}, "3": {"seed": None}} + assert list(evaluate._up_to_round(ledger, 1)) == ["1"] diff --git a/utama_core/tests/engine/test_explain.py b/utama_core/tests/engine/test_explain.py new file mode 100644 index 00000000..78ff6ab7 --- /dev/null +++ b/utama_core/tests/engine/test_explain.py @@ -0,0 +1,149 @@ +"""`tools/evaluation/explain.py` on a synthetic run dir of real columnar replays: the side assignment +(a candidate's numbers come from its own matches, against any opponent), the goals from the final +referee state, the paired goal difference, the four files, and `evaluate.py --explain` off by default.""" + +import json + +import pytest + +from tools import pitch_maps as pm +from tools.evaluation import evaluate, explain +from utama_core.entities.data.referee import RefereeData +from utama_core.entities.data.vector import Vector2D, Vector3D +from utama_core.entities.game import Ball, GameFrame, Robot +from utama_core.entities.game.team_info import TeamInfo +from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.entities.referee.stage import Stage +from utama_core.replay.columnar_writer import ( + ColumnarReplayWriter, + ColumnarReplayWriterConfig, +) + +CAND, CHAMP, OPP1, OPP2 = ( + "build_cand_kernel_strategy", + "build_champ_kernel_strategy", + "build_opp1_kernel_strategy", + "build_opp2_kernel_strategy", +) + + +def _robot(rid, x, y, friendly): + return Robot( + id=rid, + is_friendly=friendly, + has_ball=False, + p=Vector2D(x, y), + v=Vector2D(0.0, 0.0), + a=Vector2D(0.0, 0.0), + orientation=0.0, + ) + + +def _referee(score_yellow, score_blue): + return RefereeData( + source_identifier=None, + time_sent=0.0, + time_received=0.0, + referee_command=RefereeCommand.NORMAL_START, + referee_command_timestamp=0.0, + stage=Stage.NORMAL_FIRST_HALF, + stage_time_left=100.0, + blue_team=TeamInfo("blue", score=score_blue), + yellow_team=TeamInfo("yellow", score=score_yellow), + ) + + +def _write_match(run_dir, a, b, score_a, score_b, seed=None): + """A three-tick match, config_a (yellow, friendly) scoring `score_a`; the team with the ball is + always the friendly one. Written the way `evaluate.py`/`round_robin.py` name their files.""" + stem = f"{pm.sr.short(a)}_vs_{pm.sr.short(b)}" + ("" if seed is None else f"_s{seed}") + path = run_dir / f"{stem}.npz" + cfg = ColumnarReplayWriterConfig(replay_name=stem, overwrite_existing=True, checkpoint_every_s=0) + writer = ColumnarReplayWriter(cfg, my_team_is_yellow=True, exp_friendly=3, exp_enemy=3, path=path) + for i in range(3): + writer.write_frame( + GameFrame( + ts=i * 0.1, + my_team_is_yellow=True, + my_team_is_right=True, + friendly_robots={ + 0: _robot(0, 4.3, 0.0, True), + 1: _robot(1, 1.0, 0.0, True), + 2: _robot(2, -1.0, 2.0, True), + }, + enemy_robots={ + 10: _robot(10, -1.5, 0.0, False), + 11: _robot(11, 2.0, -2.0, False), + 12: _robot(12, 0.0, 3.0, False), + }, + ball=Ball(p=Vector3D(1.0, 0.0, 0.0), v=Vector3D(0.0, 0.0, 0.0), a=Vector3D(0.0, 0.0, 0.0)), + referee=_referee(score_a, score_b), + ) + ) + writer.close() + (run_dir / f"{stem}.stats.json").write_text( + json.dumps({"fouls": [{"rule": "crashing", "sim_time": 0.0, "side": "friendly", "ball_xy": [1.0, 0.0]}]}) + ) + return path + + +@pytest.fixture +def run_dir(tmp_path): + d = tmp_path / "run" + d.mkdir() + _write_match(d, CAND, OPP1, 2, 0, seed=1) # candidate friendly: 2-0 + _write_match(d, OPP1, CAND, 1, 3, seed=1) # candidate enemy: 3-1 + _write_match(d, CHAMP, OPP1, 0, 1, seed=1) # champion friendly: 0-1 + _write_match(d, OPP1, CHAMP, 1, 1, seed=1) # champion enemy: 1-1 + _write_match(d, CAND, CHAMP, 1, 1, seed=3) # head to head: 1-1 + _write_match(d, OPP1, OPP2, 5, 0, seed=1) # neither side: must not count for either + return d + + +def test_explain_writes_the_four_files(run_dir, tmp_path): + out = tmp_path / "out" + explain.explain(run_dir, CAND, CHAMP, out, workers=1) + for name in ("positions.png", "shots.png", "fouls.png"): + data = (out / name).read_bytes() + assert data[:8] == b"\x89PNG\r\n\x1a\n" and len(data) < 250_000 + assert json.loads((out / "summary.json").read_text())["candidate"] == "cand" + assert (out / "triage" / "index.md").exists() # the candidate's worst matches, as config_a + + +def test_each_side_counts_its_own_matches_and_goals(run_dir, tmp_path): + s = explain.explain(run_dir, CAND, CHAMP, tmp_path / "out", workers=1) + cand, champ = s["sides"]["cand"], s["sides"]["champ"] + assert cand["matches"] == 3 and champ["matches"] == 3 # the OPP1-OPP2 match is in neither + # candidate: 2-0, 3-1, 1-1 -> for (2+3+1)/3, against (0+1+1)/3 + assert cand["goals_for_per_match"] == pytest.approx(2.0) + assert cand["goals_against_per_match"] == pytest.approx(2 / 3) + # champion: 0-1, 1-1, 1-1 -> for (0+1+1)/3, against (1+1+1)/3 + assert champ["goals_for_per_match"] == pytest.approx(2 / 3) + assert champ["goals_against_per_match"] == pytest.approx(1.0) + + +def test_paired_difference_uses_same_opponent_seed_and_colour(run_dir, tmp_path): + s = explain.explain(run_dir, CAND, CHAMP, tmp_path / "out", workers=1) + paired = s["paired"] + assert set(paired) == {"opp1", "head-to-head"} # opp2 never met either side + # both pairs (seed 1, each colour): candidate +2 and +2, champion -1 and 0 + assert paired["opp1"] == {"candidate": 4, "champion": -1, "matches": 2} + assert paired["head-to-head"] == {"candidate": 0, "champion": 0, "matches": 1} + + +def test_explain_refuses_when_a_side_has_no_replay(run_dir, tmp_path): + with pytest.raises(FileNotFoundError): + explain.explain(run_dir, "build_nobody_kernel_strategy", CHAMP, tmp_path / "out", workers=1) + + +def test_evaluate_explain_is_off_by_default(): + assert evaluate.build_parser().parse_args(["build_x_kernel_strategy"]).explain is False + assert evaluate.build_parser().parse_args(["build_x_kernel_strategy", "--explain"]).explain is True + + +def test_an_explain_failure_never_escapes(monkeypatch, tmp_path): + def boom(*args, **kwargs): + raise RuntimeError("no replays") + + monkeypatch.setattr(explain, "explain", boom) + evaluate._explain(tmp_path, "eval_test", [CAND], CHAMP) # must print and return, not raise diff --git a/utama_core/tests/engine/test_ladder.py b/utama_core/tests/engine/test_ladder.py deleted file mode 100644 index ab096f6a..00000000 --- a/utama_core/tests/engine/test_ladder.py +++ /dev/null @@ -1,139 +0,0 @@ -"""`tools/evaluation/ladder.py`: the stopping rule, the reference pool and the fixtures. Pure: -no match is played.""" - -from __future__ import annotations - -import json - -import pytest - -from tools.evaluation import ladder - -MAX = 2 * len(ladder.SETTINGS) - - -def _r(a, b, sa, sb): - return {"config_a": a, "config_b": b, "score_a": sa, "score_b": sb} - - -# --- stopping rule --------------------------------------------------------------------------- - - -@pytest.mark.parametrize("wins, losses", [(3, 0), (0, 3), (4, 0)]) -def test_no_stop_before_min_matches_even_with_a_lead(wins, losses): - assert not ladder.decided(wins, losses, ladder.MIN_MATCHES - 1, MAX) - - -def test_stops_at_min_matches_on_a_lead_of_exactly_lead(): - assert ladder.decided(ladder.LEAD, 0, ladder.MIN_MATCHES, MAX) - assert ladder.decided(0, ladder.LEAD, ladder.MIN_MATCHES, MAX) - assert ladder.decided(ladder.LEAD, 0, 5, MAX) # 3W 2D - - -def test_does_not_stop_on_a_lead_one_short(): - assert not ladder.decided(ladder.LEAD - 1, 0, ladder.MIN_MATCHES, MAX) - assert not ladder.decided(3, 1, 4, MAX) # margin 2 with 4 left - - -def test_stops_once_the_sign_cannot_change(): - # margin 2 with 1 match left: the most the last match moves it is 1 - assert ladder.decided(4, 2, MAX - 1, MAX) - # margin 2 with exactly 2 left could still end level: keep going - assert not ladder.decided(3, 1, MAX - 2, MAX) - - -def test_level_pairing_plays_every_match(): - for played in range(ladder.MIN_MATCHES, MAX): - assert not ladder.decided(1, 1, played, MAX) - assert ladder.decided(1, 1, MAX, MAX) - - -# --- reference pool -------------------------------------------------------------------------- - -_RESULTS = [ - _r("a", "b", 2, 0), # a beats b - _r("a", "c", 1, 1), - _r("a", "d", 3, 0), - _r("b", "c", 0, 1), - _r("b", "d", 1, 0), - _r("c", "d", 0, 0), -] - - -def test_standings_points_and_goal_difference(): - table = ladder.standings(_RESULTS) - assert table["a"] == {"points": 7, "goal_difference": 5, "matches": 3} - assert table["c"] == {"points": 5, "goal_difference": 1, "matches": 3} - assert table["b"] == {"points": 3, "goal_difference": -2, "matches": 3} - assert table["d"] == {"points": 1, "goal_difference": -4, "matches": 3} - - -def test_pool_is_the_top_by_points_and_leaves_out_the_candidate(): - assert ladder.reference_pool(_RESULTS, "z", size=2) == ["a", "c"] - assert ladder.reference_pool(_RESULTS, "a", size=2) == ["c", "b"] - - -def test_pool_leaves_out_retired_strategies(monkeypatch): - monkeypatch.setattr(ladder, "RETIRED", {"a"}) - assert ladder.reference_pool(_RESULTS, "z", size=2) == ["c", "b"] - - -def test_pool_breaks_a_points_tie_on_goal_difference_then_name(): - results = [_r("x", "y", 3, 0), _r("y", "z", 1, 0), _r("z", "x", 1, 0)] # 3 points each - # x GD +2, z GD 0, y GD -2 - assert ladder.reference_pool(results, "none", size=3) == ["x", "z", "y"] - level = [_r("p", "q", 1, 0), _r("q", "p", 1, 0)] # same points, same GD - assert ladder.reference_pool(level, "none", size=2) == ["p", "q"] - - -def _write_run(root, name, config_names, n_results, fuzz_seed=None): - run = root / name - run.mkdir() - summary = { - "config_names": config_names, - "fuzz_seed": fuzz_seed, - "results": [_r("a", "b", 0, 0)] * n_results, - } - (run / "summary.json").write_text(json.dumps(summary)) - return run / "summary.json" - - -def test_latest_round_robin_skips_pair_runs_fuzzed_runs_and_partial_runs(tmp_path): - full = _write_run(tmp_path, "tournament_20261001_000000", ["a", "b", "c", "d"], 6) - _write_run(tmp_path, "tournament_20261002_000000", ["a", "b"], 1) # --pair - _write_run(tmp_path, "tournament_20261003_000000", ["a", "b", "c", "d"], 6, fuzz_seed=1) - _write_run(tmp_path, "tournament_20261004_000000", ["a", "b", "c", "d"], 5) # one short - assert ladder.latest_round_robin(tmp_path) == full - - -def test_latest_round_robin_none_when_there_is_none(tmp_path): - assert ladder.latest_round_robin(tmp_path) is None - - -# --- fixtures -------------------------------------------------------------------------------- - - -def test_a_mirrored_pair_swaps_the_teams_and_keeps_the_setting(): - assert ladder.fixtures("cand", "opp", (True, False)) == [ - ("cand", "opp", True, False), - ("opp", "cand", True, False), - ] - - -def test_the_round_robin_setting_comes_first_and_has_the_round_robin_file_tag(): - # The first mirrored pair is the round-robin's own match (and its --both-sides twin), - # so a stored round-robin record is reused and its replay name matches. - assert ladder.SETTINGS[0] == (True, True) - assert ladder._suffix(True, True) == "" - assert {ladder._suffix(*s) for s in ladder.SETTINGS[1:]} == {"_Rk", "_LK", "_Lk"} - - -def test_summarise_counts_from_the_candidates_side(): - matches = [ - {"result": _r("cand", "opp", 2, 1)}, - {"result": _r("opp", "cand", 1, 1)}, - {"result": _r("opp", "cand", 3, 0)}, - ] - s = ladder.summarise(matches, "cand") - assert (s["wins"], s["draws"], s["losses"], s["points"], s["goal_difference"]) == (1, 1, 1, 4, -2) - assert s["points_per_match"] == pytest.approx(1.33) diff --git a/utama_core/tests/engine/test_tournament_cli_args.py b/utama_core/tests/engine/test_tournament_cli_args.py index f70eda86..1c1d469b 100644 --- a/utama_core/tests/engine/test_tournament_cli_args.py +++ b/utama_core/tests/engine/test_tournament_cli_args.py @@ -96,6 +96,10 @@ def _fake_run_match( summary = json.loads((run_dirs[0] / "summary.json").read_text()) assert summary["fuzz_seed"] is None assert summary["fuzz_interval_s"] is None + # The run's matches go to the evaluation ledger under REPLAY_BASE_PATH, so a test run never + # writes into the real one. + (line,) = (tmp_path / "eval" / "ledger.jsonl").read_text().splitlines() + assert json.loads(line)["source"] == run_dirs[0].name def test_pair_plays_exactly_that_one_pairing_in_the_given_order(tmp_path, monkeypatch): @@ -150,3 +154,75 @@ def _fake_run_match(config_a_name, config_b_name, *_args, **_kwargs): monkeypatch.setattr("sys.argv", ["round_robin.py", "--sequential", "--no-save", "a", "c"]) tournament.main() assert calls == [("build_a_kernel_strategy", "build_c_kernel_strategy")] + + +def test_sim_noise_parsed_and_passed_to_run_match_and_summary(tmp_path, monkeypatch): + from utama_core.rsoccer_simulator.src.Utils.sim_noise import SimNoise + + calls = [] + + def _fake_run_match(config_a_name, config_b_name, **kwargs): + calls.append(kwargs.get("sim_noise")) + return _stub_result(config_a_name, config_b_name) + + monkeypatch.setattr(tournament, "run_match", _fake_run_match) + monkeypatch.setattr(tournament, "REPLAY_BASE_PATH", tmp_path) + monkeypatch.setattr( + "sys.argv", + [ + "tournament.py", + "--sequential", + "--sim-noise", + '{"seed": 3, "latency_s": [0.05, 0.1], "kick_speed_sd": 0.2}', + "build_low_block_kernel_strategy", + "build_tiki_taka_kernel_strategy", + ], + ) + + tournament.main() + + assert calls == [SimNoise(seed=3, latency_s=(0.05, 0.1), kick_speed_sd=0.2)] + (summary_path,) = tmp_path.glob("tournament_*/summary.json") + assert json.loads(summary_path.read_text())["sim_noise"]["seed"] == 3 + + +def test_match_seconds_keys_the_match_cache_and_is_recorded_in_the_summary(tmp_path, monkeypatch): + # A shortened run's matches must not be cached under the full-length key, or a later + # full-length `--reuse` run would serve them as full matches. + durations = [] + + def _fake_match_key(_graph, _a, _b, *, duration_seconds, **_kwargs): + durations.append(duration_seconds) + return f"key-{duration_seconds}" + + class _EmptyCache: + def get(self, _key): + return None + + def put(self, _key, _record): + pass + + monkeypatch.setattr(tournament, "run_match", _stub_result) + monkeypatch.setattr(tournament, "match_key", _fake_match_key) + monkeypatch.setattr(tournament, "CodeGraph", lambda: None) + monkeypatch.setattr(tournament, "_append_to_ledger", lambda *a: None) # needs real fingerprints + monkeypatch.setattr(tournament.match_cache, "MatchCache", _EmptyCache) + monkeypatch.setattr(tournament, "REPLAY_BASE_PATH", tmp_path) + monkeypatch.setattr( + "sys.argv", + [ + "tournament.py", + "--sequential", + "--reuse", + "--match-seconds", + "300", + "build_low_block_kernel_strategy", + "build_tiki_taka_kernel_strategy", + ], + ) + + tournament.main() + + assert durations and set(durations) == {300.0} + (summary_path,) = tmp_path.glob("tournament_*/summary.json") + assert json.loads(summary_path.read_text())["max_match_seconds"] == 300.0 diff --git a/utama_core/tests/engine/test_tournament_reuse.py b/utama_core/tests/engine/test_tournament_reuse.py index 8d78d24c..02a8b90e 100644 --- a/utama_core/tests/engine/test_tournament_reuse.py +++ b/utama_core/tests/engine/test_tournament_reuse.py @@ -48,6 +48,7 @@ def fake_losses(npz_path): monkeypatch.setattr(tournament, "run_match", fake_run_match) monkeypatch.setattr(tournament, "REPLAY_BASE_PATH", tmp_path) monkeypatch.setattr(tournament, "CodeGraph", lambda: None) + monkeypatch.setattr(tournament, "_append_to_ledger", lambda *a: None) # needs real fingerprints monkeypatch.setattr(tournament, "match_key", lambda _g, a, b, **_kw: f"{a}{rig.version[a]}{b}{rig.version[b]}") monkeypatch.setattr(tournament.match_cache, "MatchCache", lambda: rig.cache) monkeypatch.setattr(tournament.restart_outcomes, "analyse_match", lambda path: []) diff --git a/utama_core/tests/engine/test_tournament_summary.py b/utama_core/tests/engine/test_tournament_summary.py index fe8c2873..8e9f52fe 100644 --- a/utama_core/tests/engine/test_tournament_summary.py +++ b/utama_core/tests/engine/test_tournament_summary.py @@ -1,4 +1,5 @@ -from tools.evaluation.round_robin import foul_table, stall_incidents, strategy_table +from tools.evaluation.round_robin import foul_table, stall_incidents +from utama_core.analysis.strategy_table import strategy_table def _result(a: str, b: str, score_a: int, score_b: int) -> dict: @@ -144,3 +145,12 @@ def test_one_deterministic_freeze_against_two_opponents_is_one_incident(): ["overload_flow_vs_low_block"], ] assert incidents[0]["kind"] == "COMMITTED_FROZEN" + + +def test_ratings_table_ranks_by_goal_margin_with_an_sd(): + from tools.evaluation.round_robin import _ratings + + table = _ratings([_result("x", "y", 3, 0), _result("y", "z", 2, 0), _result("x", "z", 4, 1)]) + x, z = table["build_x_kernel_strategy"], table["build_z_kernel_strategy"] + assert x["strength"] > table["build_y_kernel_strategy"]["strength"] > z["strength"] + assert x["sd"] > 0 diff --git a/utama_core/tests/entities/test_vector.py b/utama_core/tests/entities/test_vector.py index 81ed006b..e43af0a0 100644 --- a/utama_core/tests/entities/test_vector.py +++ b/utama_core/tests/entities/test_vector.py @@ -28,6 +28,7 @@ def test_vector2d_operations(): # Test dot product dot_product = np.dot(v1, v2) assert dot_product == 11 + assert v1.dot(v2) == 11 # Test distance distance = v1.distance_to(v2) diff --git a/utama_core/scenario_bench/__init__.py b/utama_core/tests/moments/__init__.py similarity index 100% rename from utama_core/scenario_bench/__init__.py rename to utama_core/tests/moments/__init__.py diff --git a/utama_core/tests/moments/free_kick_defender_dodges_into_ball.json b/utama_core/tests/moments/free_kick_defender_dodges_into_ball.json new file mode 100644 index 00000000..e62c3030 --- /dev/null +++ b/utama_core/tests/moments/free_kick_defender_dodges_into_ball.json @@ -0,0 +1,158 @@ +{ + "name": "free_kick_defender_dodges_into_ball", + "note": "Their free kick on the touch line; robots 3 and 5 both start inside the keep-out circle and leave toward the same side. The planner sidesteps robot 5 round robot 3 straight at the ball (0.52 m to 0.27 m): a keep-out foul. Round-robin tournament_20261017_002337, clear_danger vs split_shape, ~50 such fouls across the run.", + "source": { + "replay": "replays/tournament_20261017_002337/clear_danger_vs_split_shape.npz", + "t": 254.65 + }, + "strategy": "all", + "opponent": "build_split_shape_kernel_strategy", + "seconds": 2.0, + "checks": [ + "keeps_clear" + ], + "requires": [ + "restart_lasts", + "robot_near_ball" + ], + "known_failure": null, + "designated_position": null, + "sim_noise": { + "seed": 2, + "latency_s": [ + 0.03, + 0.15 + ], + "kick_speed_offset_sd": 0.2, + "kick_speed_sd": 0.45, + "vision": { + "x_stddev": 0.0007, + "y_stddev": 0.0007, + "th_stddev_deg": 0.3 + }, + "vanishing": 0.005, + "ball_roll_decel": [ + 0.27, + 0.33 + ], + "ball_slide_decel": [ + 2.5, + 4.0 + ], + "kick_angle_offset_sd_deg": 1.5, + "kick_angle_sd_deg": 3.0, + "max_accel": [ + 1.5, + 3.5 + ] + }, + "scenario": { + "referee_command": "DIRECT_FREE_BLUE", + "ball": [ + -0.4807656705379486, + 2.7513861656188965, + 0.009728293865919113, + 0.02854176051914692 + ], + "friendly": [ + { + "id": 0, + "x": 4.408860206604004, + "y": 0.14157667756080627, + "orientation": 3.0654706954956055, + "vx": 0.005269075743854046, + "vy": 0.008301910944283009 + }, + { + "id": 1, + "x": -3.9969911575317383, + "y": 2.4013280868530273, + "orientation": 0.13057857751846313, + "vx": 0.045420803129673004, + "vy": 0.02216733619570732 + }, + { + "id": 2, + "x": -1.270822525024414, + "y": 2.693711280822754, + "orientation": -3.064497232437134, + "vx": 0.006828407756984234, + "vy": 0.023743558675050735 + }, + { + "id": 3, + "x": -0.7388142943382263, + "y": 2.4017837047576904, + "orientation": -2.0348594188690186, + "vx": -0.5011354088783264, + "vy": -0.3382619619369507 + }, + { + "id": 4, + "x": 2.6023404598236084, + "y": 1.6028587818145752, + "orientation": 2.7492294311523438, + "vx": 0.04201503098011017, + "vy": 0.006691030692309141 + }, + { + "id": 5, + "x": -0.6322249174118042, + "y": 2.256791114807129, + "orientation": -1.8467990159988403, + "vx": -0.03902489319443703, + "vy": -0.009351136162877083 + } + ], + "enemy": [ + { + "id": 0, + "x": -4.410989284515381, + "y": 0.04050786420702934, + "orientation": 0.44752776622772217, + "vx": -0.025609247386455536, + "vy": 0.021452851593494415 + }, + { + "id": 1, + "x": 1.5008584260940552, + "y": 2.7009005546569824, + "orientation": 1.7254570722579956, + "vx": -0.03238960728049278, + "vy": 0.0071782260201871395 + }, + { + "id": 2, + "x": -2.0162811279296875, + "y": 2.445668935775757, + "orientation": -1.8984222412109375, + "vx": 0.012988249771296978, + "vy": -0.1313505917787552 + }, + { + "id": 3, + "x": -1.2379214763641357, + "y": 0.8434605598449707, + "orientation": 1.3214842081069946, + "vx": 0.004649681970477104, + "vy": -0.021365584805607796 + }, + { + "id": 4, + "x": 0.7154814600944519, + "y": 0.2881236970424652, + "orientation": 1.982088327407837, + "vx": 0.007788113784044981, + "vy": -0.014537670649588108 + }, + { + "id": 5, + "x": -3.179486036300659, + "y": 1.0890549421310425, + "orientation": 0.6215268969535828, + "vx": -0.017317960038781166, + "vy": -0.0193502027541399 + } + ] + } +} diff --git a/utama_core/tests/moments/free_kick_robot_drives_in_at_start.json b/utama_core/tests/moments/free_kick_robot_drives_in_at_start.json new file mode 100644 index 00000000..fb7d4496 --- /dev/null +++ b/utama_core/tests/moments/free_kick_robot_drives_in_at_start.json @@ -0,0 +1,159 @@ +{ + "name": "free_kick_robot_drives_in_at_start", + "note": "Round-robin tournament_20261017_002337, decoy_and_overload vs overload_press, from t=155.3 s, the start of the DIRECT_FREE_BLUE that their kicker took at about 156.85 s (the source's keep_out foul by robot 3 came at 157.72 s). The moment starts before the kick: at 156.9 s the kick had already happened, so the restart was over before the moment began. Their kicker pushes the ball off during the free kick, and it rolls onto robot 3, which stands 0.79 m away. Robot 3 must not drive in (keeps_clear), and the referee must not charge it with keep_out once the ball has moved 0.05 m: the kick was taken and the ball is in play.", + "source": { + "replay": "replays/tournament_20261017_002337/decoy_and_overload_vs_overload_press.npz", + "t": 155.3 + }, + "strategy": "all", + "opponent": "build_overload_press_kernel_strategy", + "seconds": 3.0, + "checks": [ + "keep_out", + "keeps_clear" + ], + "known_failure": null, + "designated_position": null, + "sim_noise": { + "seed": 2, + "latency_s": [ + 0.03, + 0.15 + ], + "kick_speed_offset_sd": 0.2, + "kick_speed_sd": 0.45, + "vision": { + "x_stddev": 0.0007, + "y_stddev": 0.0007, + "th_stddev_deg": 0.3 + }, + "vanishing": 0.005, + "ball_roll_decel": [ + 0.27, + 0.33 + ], + "ball_slide_decel": [ + 2.5, + 4.0 + ], + "kick_angle_offset_sd_deg": 1.5, + "kick_angle_sd_deg": 3.0, + "max_accel": [ + 1.5, + 3.5 + ] + }, + "scenario": { + "referee_command": "DIRECT_FREE_BLUE", + "ball": [ + 2.536470890045166, + 2.750206232070923, + -0.017454709857702255, + -0.024647576734423637 + ], + "friendly": [ + { + "id": 0, + "x": 4.395570278167725, + "y": 0.2623056173324585, + "orientation": 2.205690860748291, + "vx": -0.04277755692601204, + "vy": 0.046198222786188126 + }, + { + "id": 1, + "x": -2.934321403503418, + "y": -1.1155835390090942, + "orientation": 0.759769082069397, + "vx": -0.02258961834013462, + "vy": -0.0004753725079353899 + }, + { + "id": 2, + "x": -1.647862195968628, + "y": 0.7498007416725159, + "orientation": 0.4906955063343048, + "vx": 0.0026092727202922106, + "vy": -0.020033113658428192 + }, + { + "id": 3, + "x": 3.317260980606079, + "y": 2.6062958240509033, + "orientation": -0.1795390099287033, + "vx": 0.010497135110199451, + "vy": 0.014908869750797749 + }, + { + "id": 4, + "x": 2.836455821990967, + "y": 0.9419028162956238, + "orientation": 1.6644355058670044, + "vx": 0.04859288036823273, + "vy": 0.009664556942880154 + }, + { + "id": 5, + "x": 3.4410338401794434, + "y": 1.0231825113296509, + "orientation": 1.997361660003662, + "vx": 0.007438821252435446, + "vy": 0.010257917456328869 + } + ], + "enemy": [ + { + "id": 0, + "x": -4.406434059143066, + "y": -0.23964542150497437, + "orientation": 0.4114653170108795, + "vx": -0.02397257462143898, + "vy": 0.0030586537905037403 + }, + { + "id": 1, + "x": 2.012925624847412, + "y": 0.7591932415962219, + "orientation": 1.3095483779907227, + "vx": 0.03543104976415634, + "vy": -0.0006866941694170237 + }, + { + "id": 2, + "x": 2.217620611190796, + "y": 0.01867786794900894, + "orientation": 1.4449868202209473, + "vx": 0.03139911964535713, + "vy": 0.012284408323466778 + }, + { + "id": 3, + "x": 1.7827485799789429, + "y": -0.7556700110435486, + "orientation": 1.3505078554153442, + "vx": -0.016163356602191925, + "vy": 0.00692970072850585 + }, + { + "id": 4, + "x": 1.7530889511108398, + "y": 0.7376781105995178, + "orientation": 1.2018553018569946, + "vx": 0.0074382200837135315, + "vy": -0.024446330964565277 + }, + { + "id": 5, + "x": 1.743301510810852, + "y": 1.749427318572998, + "orientation": 1.1451590061187744, + "vx": 0.06030035391449928, + "vy": 0.09924978017807007 + } + ] + }, + "requires": [ + "restart_lasts", + "robot_near_ball" + ] +} diff --git a/utama_core/tests/moments/placement_corner_robot_drives_onto_ball.json b/utama_core/tests/moments/placement_corner_robot_drives_onto_ball.json new file mode 100644 index 00000000..2e464005 --- /dev/null +++ b/utama_core/tests/moments/placement_corner_robot_drives_onto_ball.json @@ -0,0 +1,162 @@ +{ + "name": "placement_corner_robot_drives_onto_ball", + "note": "Their placement at (-4.0, -2.5): robot 1 starts inside the keep-out circle, boxed in the corner by two opponents. It must leave without closing on the ball. Before 935da778 the planner's detour took it back toward the ball (fails at seeds 0, 1, 3, 4; seed 1 stored). The placement moves the ball onto robot 1, which keeps_clear does not count as driving in.", + "source": { + "replay": "replays/tournament_20261017_002337/overload_press_vs_press_trigger_flow.npz", + "t": 333.5 + }, + "strategy": "all", + "opponent": "build_press_trigger_flow_kernel_strategy", + "seconds": 3.0, + "checks": [ + "keeps_clear", + "ball_placement_interference" + ], + "known_failure": null, + "designated_position": [ + -4.0, + -2.5 + ], + "sim_noise": { + "seed": 1, + "latency_s": [ + 0.03, + 0.15 + ], + "kick_speed_offset_sd": 0.2, + "kick_speed_sd": 0.45, + "vision": { + "x_stddev": 0.0007, + "y_stddev": 0.0007, + "th_stddev_deg": 0.3 + }, + "vanishing": 0.005, + "ball_roll_decel": [ + 0.27, + 0.33 + ], + "ball_slide_decel": [ + 2.5, + 4.0 + ], + "kick_angle_offset_sd_deg": 1.5, + "kick_angle_sd_deg": 3.0, + "max_accel": [ + 1.5, + 3.5 + ] + }, + "scenario": { + "referee_command": "BALL_PLACEMENT_BLUE", + "ball": [ + -3.8450865745544434, + -2.4952964782714844, + 0.26960477232933044, + -0.004714237991720438 + ], + "friendly": [ + { + "id": 0, + "x": 4.408496856689453, + "y": 0.05949976667761803, + "orientation": -3.0830912590026855, + "vx": -0.02837618812918663, + "vy": 0.018985001370310783 + }, + { + "id": 1, + "x": -3.961132764816284, + "y": -2.4723739624023438, + "orientation": -2.9403579235076904, + "vx": 0.41556069254875183, + "vy": 0.14093388617038727 + }, + { + "id": 2, + "x": -1.7985084056854248, + "y": 0.7482134699821472, + "orientation": -2.174622058868408, + "vx": -0.007584169041365385, + "vy": 0.025894450023770332 + }, + { + "id": 3, + "x": -1.801575779914856, + "y": -0.7477805018424988, + "orientation": -2.4529616832733154, + "vx": -0.003465779824182391, + "vy": 0.006816626526415348 + }, + { + "id": 4, + "x": -3.48284649848938, + "y": -2.4960625171661377, + "orientation": -0.008658156730234623, + "vx": 0.6966798305511475, + "vy": 0.147159144282341 + }, + { + "id": 5, + "x": -3.473487138748169, + "y": -2.0466408729553223, + "orientation": 0.762504518032074, + "vx": 0.29568204283714294, + "vy": 0.3123944401741028 + } + ], + "enemy": [ + { + "id": 0, + "x": -4.5, + "y": -0.4088737368583679, + "orientation": -0.005148385651409626, + "vx": 0.05368196219205856, + "vy": 0.003654107451438904 + }, + { + "id": 1, + "x": -2.546550750732422, + "y": -0.7113376259803772, + "orientation": -2.2168846130371094, + "vx": -0.010257352143526077, + "vy": -0.010980518534779549 + }, + { + "id": 2, + "x": -3.9515433311462402, + "y": -1.7606247663497925, + "orientation": 1.5919874906539917, + "vx": -0.0330691933631897, + "vy": 0.16876448690891266 + }, + { + "id": 3, + "x": -2.365267515182495, + "y": 0.6217703223228455, + "orientation": -2.1252009868621826, + "vx": 0.034154750406742096, + "vy": -0.0006636145408265293 + }, + { + "id": 4, + "x": -3.997985363006592, + "y": -2.295707941055298, + "orientation": -1.145664930343628, + "vx": 0.2552659809589386, + "vy": 0.10615040361881256 + }, + { + "id": 5, + "x": -4.202090740203857, + "y": -2.402651786804199, + "orientation": 2.385448455810547, + "vx": 0.24332153797149658, + "vy": 0.07585639506578445 + } + ] + }, + "requires": [ + "restart_lasts", + "robot_near_ball" + ] +} diff --git a/utama_core/tests/moments/placement_in_corner_traps_defender.json b/utama_core/tests/moments/placement_in_corner_traps_defender.json new file mode 100644 index 00000000..aba4a11d --- /dev/null +++ b/utama_core/tests/moments/placement_in_corner_traps_defender.json @@ -0,0 +1,162 @@ +{ + "name": "placement_in_corner_traps_defender", + "note": "Their ball placement to (4.0, 2.5), near our corner. Robot 2, 0.3 m from the ball, is pushed out to 0.8 m along its own bearing, off the pitch; clamped back onto it the target is still inside the circle, so the robot never leaves: a placement interference foul. Round-robin tournament_20261017_002337, counter_flow vs give_and_go_solo.", + "source": { + "replay": "replays/tournament_20261017_002337/counter_flow_vs_give_and_go_solo.npz", + "t": 163.0 + }, + "strategy": "all", + "opponent": "build_give_and_go_solo_kernel_strategy", + "seconds": 3.0, + "checks": [ + "keeps_clear", + "ball_placement_interference" + ], + "requires": [ + "restart_lasts", + "robot_near_ball" + ], + "known_failure": null, + "designated_position": [ + 4.0, + 2.5 + ], + "sim_noise": { + "seed": 2, + "latency_s": [ + 0.03, + 0.15 + ], + "kick_speed_offset_sd": 0.2, + "kick_speed_sd": 0.45, + "vision": { + "x_stddev": 0.0007, + "y_stddev": 0.0007, + "th_stddev_deg": 0.3 + }, + "vanishing": 0.005, + "ball_roll_decel": [ + 0.27, + 0.33 + ], + "ball_slide_decel": [ + 2.5, + 4.0 + ], + "kick_angle_offset_sd_deg": 1.5, + "kick_angle_sd_deg": 3.0, + "max_accel": [ + 1.5, + 3.5 + ] + }, + "scenario": { + "referee_command": "BALL_PLACEMENT_BLUE", + "ball": [ + 4.000718593597412, + 2.5001039505004883, + 0.03022906184196472, + 0.01820620521903038 + ], + "friendly": [ + { + "id": 0, + "x": 4.407966136932373, + "y": 0.4080584943294525, + "orientation": 1.6470003128051758, + "vx": -0.0005575984832830727, + "vy": 0.03238838165998459 + }, + { + "id": 1, + "x": 3.4034035205841064, + "y": 0.31569981575012207, + "orientation": 1.4781031608581543, + "vx": 0.01965811289846897, + "vy": 0.01500271912664175 + }, + { + "id": 2, + "x": 4.276703834533691, + "y": 2.3508384227752686, + "orientation": -1.1787805557250977, + "vx": 0.0009964045602828264, + "vy": -0.007879270240664482 + }, + { + "id": 3, + "x": -2.7047958374023438, + "y": -0.032752856612205505, + "orientation": 0.34564128518104553, + "vx": 0.001046720426529646, + "vy": -0.025415241718292236 + }, + { + "id": 4, + "x": 2.2714576721191406, + "y": 0.9499884247779846, + "orientation": 0.6757456064224243, + "vx": 0.02668331004679203, + "vy": -0.017388781532645226 + }, + { + "id": 5, + "x": 2.8221805095672607, + "y": 1.6641405820846558, + "orientation": 0.481290727853775, + "vx": 0.002441104268655181, + "vy": -0.02858341671526432 + } + ], + "enemy": [ + { + "id": 0, + "x": -4.5, + "y": -0.401977002620697, + "orientation": 0.00540520902723074, + "vx": 0.017526358366012573, + "vy": -0.008871330879628658 + }, + { + "id": 1, + "x": 2.8238449096679688, + "y": 1.1251498460769653, + "orientation": 0.5976036190986633, + "vx": -0.02055889181792736, + "vy": -0.008285828866064548 + }, + { + "id": 2, + "x": 2.925846815109253, + "y": 0.150612011551857, + "orientation": 0.9782609939575195, + "vx": 0.006188818719238043, + "vy": 0.04150957241654396 + }, + { + "id": 3, + "x": 4.0975236892700195, + "y": 2.3687822818756104, + "orientation": -2.889813184738159, + "vx": -0.012717907316982746, + "vy": -0.005403792019933462 + }, + { + "id": 4, + "x": 2.931936740875244, + "y": -1.0765888690948486, + "orientation": 1.1885813474655151, + "vx": -0.01568521000444889, + "vy": -0.03724595531821251 + }, + { + "id": 5, + "x": 3.718639612197876, + "y": 1.927229881286621, + "orientation": -1.0715361833572388, + "vx": -0.12264327704906464, + "vy": -0.07666496932506561 + } + ] + } +} diff --git a/utama_core/tests/moments/placement_robot_drives_in_mid_pitch.json b/utama_core/tests/moments/placement_robot_drives_in_mid_pitch.json new file mode 100644 index 00000000..3e695571 --- /dev/null +++ b/utama_core/tests/moments/placement_robot_drives_in_mid_pitch.json @@ -0,0 +1,161 @@ +{ + "name": "placement_robot_drives_in_mid_pitch", + "note": "Round-robin tournament_20261017_002337, counter_flow vs score_aware_counter_flow, t=122.5 s: their ball placement to (2.5, 0.74) (the source's keep_out foul by robot 5 at 123.35 s came in their free kick that followed). Robot 4 of ours drives in from 0.65 m to 0.49 m of the ball during their placement, inside the keep-out circle. Our side must keep out of their placement and must not drive in. Passes now; FAILED at 14038121, the code before the planner fix c64fc6f9, so the planner fix covers it. Protected regression.", + "source": { + "replay": "replays/tournament_20261017_002337/counter_flow_vs_score_aware_counter_flow.npz", + "t": 122.5 + }, + "strategy": "all", + "opponent": "build_score_aware_counter_flow_kernel_strategy", + "seconds": 3.0, + "checks": [ + "keeps_clear" + ], + "requires": [ + "restart_lasts", + "robot_near_ball" + ], + "known_failure": null, + "designated_position": [ + 2.5, + 0.73833464762983 + ], + "sim_noise": { + "seed": 2, + "latency_s": [ + 0.03, + 0.15 + ], + "kick_speed_offset_sd": 0.2, + "kick_speed_sd": 0.45, + "vision": { + "x_stddev": 0.0007, + "y_stddev": 0.0007, + "th_stddev_deg": 0.3 + }, + "vanishing": 0.005, + "ball_roll_decel": [ + 0.27, + 0.33 + ], + "ball_slide_decel": [ + 2.5, + 4.0 + ], + "kick_angle_offset_sd_deg": 1.5, + "kick_angle_sd_deg": 3.0, + "max_accel": [ + 1.5, + 3.5 + ] + }, + "scenario": { + "referee_command": "BALL_PLACEMENT_BLUE", + "ball": [ + 2.499613046646118, + 0.739011287689209, + -0.017688587307929993, + 0.003960727714002132 + ], + "friendly": [ + { + "id": 0, + "x": 4.399986743927002, + "y": 0.38434505462646484, + "orientation": 3.068960666656494, + "vx": 0.007426020223647356, + "vy": 0.01688084565103054 + }, + { + "id": 1, + "x": 2.9468634128570557, + "y": -0.029310377314686775, + "orientation": -1.007601022720337, + "vx": -0.015627240762114525, + "vy": -0.01603234000504017 + }, + { + "id": 2, + "x": 2.8821847438812256, + "y": 1.8997732400894165, + "orientation": -3.010634660720825, + "vx": -0.011780944652855396, + "vy": -0.015498174354434013 + }, + { + "id": 3, + "x": 3.0620038509368896, + "y": 0.2510182559490204, + "orientation": -0.720249354839325, + "vx": 0.08946499228477478, + "vy": -0.0671628788113594 + }, + { + "id": 4, + "x": 2.5736711025238037, + "y": 0.009045029059052467, + "orientation": -1.5071358680725098, + "vx": 0.025471419095993042, + "vy": -0.2905663549900055 + }, + { + "id": 5, + "x": 2.416513442993164, + "y": 0.10382003337144852, + "orientation": -1.7725669145584106, + "vx": 0.24410876631736755, + "vy": 0.306346595287323 + } + ], + "enemy": [ + { + "id": 0, + "x": -4.408724784851074, + "y": 0.40765130519866943, + "orientation": 0.7368849515914917, + "vx": 0.01618080772459507, + "vy": -0.02199448272585869 + }, + { + "id": 1, + "x": 0.9694547653198242, + "y": 2.583130121231079, + "orientation": -2.83927583694458, + "vx": 0.017511464655399323, + "vy": 0.01774093508720398 + }, + { + "id": 2, + "x": 1.0189850330352783, + "y": -0.05494910478591919, + "orientation": 2.9494831562042236, + "vx": 0.016077173873782158, + "vy": -0.01805715635418892 + }, + { + "id": 3, + "x": 0.9458892345428467, + "y": 0.9254980683326721, + "orientation": -2.9719526767730713, + "vx": 0.011257879436016083, + "vy": 0.00039845824358053505 + }, + { + "id": 4, + "x": -2.313833713531494, + "y": 0.6179143190383911, + "orientation": 2.8177902698516846, + "vx": 0.020992867648601532, + "vy": -0.00785255990922451 + }, + { + "id": 5, + "x": 0.6030757427215576, + "y": 1.1093614101409912, + "orientation": 3.1113860607147217, + "vx": -0.023117583245038986, + "vy": 0.015441475436091423 + } + ] + } +} diff --git a/utama_core/tests/moments/test_moment_authoring.py b/utama_core/tests/moments/test_moment_authoring.py new file mode 100644 index 00000000..57e09ec5 --- /dev/null +++ b/utama_core/tests/moments/test_moment_authoring.py @@ -0,0 +1,278 @@ +"""Pure checks (no rsim) of hand-made moments in `tools/moments.py`: the template and the +validation errors.""" + +import dataclasses + +import pytest + +from tools import moments + + +def _template(): + return moments.template("probe", "a note", "NORMAL_START", ["keeps_clear"]) + + +def _with_friendly(m, robots): + return dataclasses.replace(m, scenario=dataclasses.replace(m.scenario, friendly_robots=tuple(robots))) + + +def _robot(robots, rid): + return next(r for r in robots if r.id == rid) + + +def _replace_robot(robots, rid, **changes): + return tuple(dataclasses.replace(r, **changes) if r.id == rid else r for r in robots) + + +def test_template_round_trips_through_load(tmp_path): + m = _template() + path = moments.save(m, tmp_path / "probe.json") + again = moments.load(path) + assert moments.to_dict(again) == moments.to_dict(m) + assert again.sim_noise["seed"] == 1 + assert sorted(r.id for r in again.scenario.friendly_robots) == list(range(6)) + assert sorted(r.id for r in again.scenario.enemy_robots) == list(range(6)) + + +def test_template_is_our_side_on_the_right_facing_left(): + m = _template() + ours = {r.id: r for r in m.scenario.friendly_robots} + theirs = {r.id: r for r in m.scenario.enemy_robots} + assert ours[0].x > 0 and theirs[0].x < 0 + assert all(ours[i].x > 0 and theirs[i].x < 0 for i in range(6)) + + +def test_existing_moments_still_load(): + for path in sorted(moments.MOMENTS_DIR.glob("*.json")): + moments.load(path) + + +def test_referee_rule_names_come_from_the_code(): + names = moments.referee_rule_names() + assert {"keep_out", "double_touch", "excessive_dribbling", "goal"} <= names + + +# --- validation --------------------------------------------------------------------------------- + + +def test_each_side_needs_ids_0_to_5_exactly_once(): + m = _template() + friendly = _replace_robot(m.scenario.friendly_robots, 5, id=3) + with pytest.raises(moments.MomentError, match=r"friendly robots must have ids 0-5 exactly once"): + moments.validate(_with_friendly(m, friendly)) + + +def test_robots_must_be_on_the_pitch(): + m = _with_friendly(_template(), _replace_robot(_template().scenario.friendly_robots, 2, x=5.0)) + with pytest.raises(moments.MomentError, match=r"friendly robot 2 at \(5.00, -1.20\) is off the pitch"): + moments.validate(m) + + +def test_the_ball_must_be_on_the_pitch(): + m = dataclasses.replace(_template(), scenario=dataclasses.replace(_template().scenario, ball_y=3.5)) + with pytest.raises(moments.MomentError, match=r"ball at \(0.00, 3.50\) is off the pitch"): + moments.validate(m) + + +def test_an_unknown_check_is_named(): + m = dataclasses.replace(_template(), checks=["keeps_clera"]) + with pytest.raises(moments.MomentError, match=r"unknown check 'keeps_clera'"): + moments.validate(m) + + +def test_checks_must_not_be_empty(): + with pytest.raises(moments.MomentError, match=r"checks is empty"): + moments.validate(dataclasses.replace(_template(), checks=[])) + + +def test_a_real_rule_name_and_star_are_valid_checks(): + moments.validate(dataclasses.replace(_template(), checks=["double_touch", "*"])) + + +def test_unknown_strategy_and_opponent_are_named(): + with pytest.raises(moments.MomentError, match=r"unknown strategy 'nope'"): + moments.validate(dataclasses.replace(_template(), strategy="build_nope_kernel_strategy")) + with pytest.raises(moments.MomentError, match=r"unknown opponent 'nope'"): + moments.validate(dataclasses.replace(_template(), opponent="build_nope_kernel_strategy")) + + +def test_seconds_must_be_positive(): + with pytest.raises(moments.MomentError, match=r"seconds must be positive, got 0"): + moments.validate(dataclasses.replace(_template(), seconds=0)) + + +def test_every_problem_is_reported_together(): + m = dataclasses.replace(_template(), checks=["nope"], seconds=-1) + with pytest.raises(moments.MomentError) as err: + moments.validate(m) + assert "unknown check 'nope'" in str(err.value) and "seconds must be positive" in str(err.value) + + +# --- `play --seed`, and the foul listing's restart side --------------------------------------- + + +def test_with_seed_replaces_only_the_seed_and_does_not_mutate_the_moment(): + m = _template() + seeded = moments.with_seed(m, 7) + assert seeded.sim_noise["seed"] == 7 + assert {k: v for k, v in seeded.sim_noise.items() if k != "seed"} == { + k: v for k, v in m.sim_noise.items() if k != "seed" + } + assert m.sim_noise["seed"] == 1 + + +def test_with_seed_needs_noise_to_seed(): + with pytest.raises(moments.MomentError, match=r"no sim_noise to seed"): + moments.with_seed(dataclasses.replace(_template(), sim_noise=None), 3) + + +@pytest.mark.parametrize( + "command, we_are_yellow, side", + [ + ("DIRECT_FREE_YELLOW", True, "ours"), + ("BALL_PLACEMENT_YELLOW", True, "ours"), + ("DIRECT_FREE_BLUE", True, "theirs"), + ("DIRECT_FREE_BLUE", False, "ours"), + ("NORMAL_START", True, None), + ("STOP", True, None), + ("TIMEOUT_YELLOW", True, None), + ], +) +def test_restart_side_is_ours_theirs_or_neither(command, we_are_yellow, side): + from utama_core.entities.referee.referee_command import RefereeCommand + + assert moments.restart_side(RefereeCommand[command], we_are_yellow) == side + assert moments.restart_side(None, we_are_yellow) is None + + +def test_our_fouls_names_the_command_active_and_the_restart_side(tmp_path): + import json + + def foul(t, side="friendly", rule="keep_out"): + return {"rule": rule, "sim_time": t, "side": side, "robot_id": 1, "tactic": "X", "inferred": False} + + results = [ + { + "config_a": "build_a_kernel_strategy", + "config_b": "build_b_kernel_strategy", + "stats": {"fouls": [foul(1.0), foul(5.0), foul(12.0), foul(20.0), foul(6.0, side="enemy")]}, + } + ] + (tmp_path / "summary.json").write_text(json.dumps({"results": results})) + rows = [ + {"event": "referee", "sim_time": 0.0, "command": "NORMAL_START"}, + {"event": "referee", "sim_time": 4.0, "command": "DIRECT_FREE_BLUE"}, + {"event": "referee", "sim_time": 10.0, "command": "DIRECT_FREE_YELLOW"}, + ] + (tmp_path / "a_vs_b.intentions.jsonl").write_text("\n".join(json.dumps(r) for r in rows) + "\n") + + fouls = moments.our_fouls(tmp_path) + assert [(f["sim_time"], f["command"], f["restart"]) for f in fouls] == [ + (1.0, "NORMAL_START", None), + (5.0, "DIRECT_FREE_BLUE", "theirs"), + (12.0, "DIRECT_FREE_YELLOW", "ours"), + (20.0, "DIRECT_FREE_YELLOW", "ours"), + ] + kept = moments.without_own_restarts(fouls) + assert [f["sim_time"] for f in kept] == [1.0, 5.0] + + +def test_our_fouls_names_the_match_replay_relative_to_the_repo(tmp_path): + import json + + row = {"rule": "keep_out", "sim_time": 3.0, "side": "friendly", "robot_id": 1, "tactic": "X"} + results = [ + {"config_a": "build_a_kernel_strategy", "config_b": "build_b_kernel_strategy", "stats": {"fouls": [row]}} + ] + (tmp_path / "summary.json").write_text(json.dumps({"results": results})) + (fouls,) = moments.our_fouls(tmp_path) + assert fouls["replay"] is None # no a_vs_b replay in the run dir + + (tmp_path / "a_vs_b.npz").write_bytes(b"") + (fouls,) = moments.our_fouls(tmp_path) + assert fouls["replay"] == str(tmp_path / "a_vs_b.npz") # outside the repo: as given + + (tmp_path / "a_vs_b.npz").unlink() + (tmp_path / "a_vs_b.pkl").write_bytes(b"") + (fouls,) = moments.our_fouls(tmp_path) + assert fouls["replay"] == str(tmp_path / "a_vs_b.pkl") + + +def test_repo_relative_paths_drop_the_repo_root(): + assert moments._repo_relative(moments.REPO_ROOT / "replays" / "t" / "a_vs_b.npz") == "replays/t/a_vs_b.npz" + + +def test_our_fouls_without_a_match_log_has_no_command(tmp_path): + import json + + row = {"rule": "keep_out", "sim_time": 3.0, "side": "friendly", "robot_id": 1, "tactic": "X"} + results = [ + {"config_a": "build_a_kernel_strategy", "config_b": "build_b_kernel_strategy", "stats": {"fouls": [row]}} + ] + (tmp_path / "summary.json").write_text(json.dumps({"results": results})) + (fouls,) = moments.our_fouls(tmp_path) + assert fouls["command"] is None and fouls["restart"] is None + assert moments.without_own_restarts([fouls]) == [fouls] + + +def _foul_row(config_a, rule, restart=None, tactic="X"): + return { + "config_a": f"build_{config_a}_kernel_strategy", + "config_b": "build_b_kernel_strategy", + "rule": rule, + "tactic": tactic, + "restart": restart, + } + + +def test_count_fouls_groups_by_the_given_columns_most_common_first(): + fouls = [ + _foul_row("a", "keep_out"), + _foul_row("b", "crashing"), + _foul_row("a", "keep_out"), + _foul_row("a", "crashing"), + _foul_row("b", "crashing"), + _foul_row("b", "crashing"), + ] + assert moments.count_fouls(fouls, ["strategy", "rule"]) == [ + (("b", "crashing"), 3), + (("a", "keep_out"), 2), + (("a", "crashing"), 1), + ] + + +def test_count_fouls_names_a_missing_restart_live_and_a_missing_tactic_dash(): + fouls = [_foul_row("a", "keep_out", restart=None, tactic=None), _foul_row("a", "keep_out", restart="theirs")] + assert moments.count_fouls(fouls, ["restart", "tactic"]) == [(("live", "-"), 1), (("theirs", "X"), 1)] + + +def test_count_fouls_ties_are_in_name_order(): + fouls = [_foul_row("b", "keep_out"), _foul_row("a", "keep_out")] + assert [key for key, _ in moments.count_fouls(fouls, ["strategy"])] == [("a",), ("b",)] + + +def test_without_stoppages_drops_out_of_bounds_and_keeps_rules_fouls(): + fouls = [_foul_row("a", "out_of_bounds"), _foul_row("a", "defense_area_stoppage"), _foul_row("a", "keep_out")] + assert [f["rule"] for f in moments.without_stoppages(fouls)] == ["defense_area_stoppage", "keep_out"] + + +def test_print_fouls_by_prints_header_counts_and_total(capsys): + moments.print_fouls_by([_foul_row("a", "keep_out"), _foul_row("a", "keep_out")], ["strategy", "rule"]) + lines = capsys.readouterr().out.splitlines() + assert lines[0].split() == ["count", "strategy", "rule"] + assert lines[1].split() == ["2", "a", "keep_out"] + assert lines[2].split() == ["2", "total"] + + +def test_fouls_json_is_the_foul_list_or_the_by_counts_as_rows(): + import json + + fouls = [_foul_row("a", "keep_out", restart=None), _foul_row("a", "keep_out"), _foul_row("b", "crashing")] + as_list = json.loads(moments.fouls_json(fouls)) + assert as_list == fouls + + rows = json.loads(moments.fouls_json(fouls, ["strategy", "rule"])) + assert rows == [ + {"strategy": "a", "rule": "keep_out", "count": 2}, + {"strategy": "b", "rule": "crashing", "count": 1}, + ] diff --git a/utama_core/tests/moments/test_moments.py b/utama_core/tests/moments/test_moments.py new file mode 100644 index 00000000..d31cc4ac --- /dev/null +++ b/utama_core/tests/moments/test_moments.py @@ -0,0 +1,56 @@ +"""Every regression moment (`tools/moments.py`, the JSON files beside this one) against every +strategy it concerns. A moment with a `known_failure` is a strict xfail, so its fix shows as +XPASS: then delete the `known_failure`.""" + +import pytest + +from tools import moments + + +def _cases(): + for path in sorted(moments.MOMENTS_DIR.glob("*.json")): + m = moments.load(path) + marks = [pytest.mark.xfail(strict=True, reason=m.known_failure)] if m.known_failure else [] + for strategy in moments.strategies_for(m): + yield pytest.param(path, strategy, id=f"{m.name}-{moments.short_name(strategy)}", marks=marks) + + +@pytest.mark.parametrize("path, strategy", list(_cases())) +def test_moment(path, strategy): + m = moments.load(path) + failures = moments.judge(m, moments.play(m, strategy)) + assert not failures, f"{m.name}: {m.note}\n" + "\n".join(failures) + + +def _tick(t, command, ball, ours): + return moments.Tick(t=t, command=command, ball=ball, ours=ours, our_speeds={}, our_foul=None) + + +def test_keeps_clear_lets_a_robot_inside_leave_and_flags_one_driving_in(): + from utama_core.entities.referee.referee_command import RefereeCommand + + stop, dt = RefereeCommand.STOP, 1 / moments.TICKS_PER_SECOND + leaving = [_tick(i * dt, stop, (0.0, 0.0), {1: (0.2 + 0.01 * i, 0.0)}) for i in range(60)] + assert moments.keeps_clear(leaving) == [] + driving_in = [_tick(i * dt, stop, (0.0, 0.0), {1: (0.45 - 0.004 * i, 0.0)}) for i in range(60)] + assert "drove in" in moments.keeps_clear(driving_in)[0] + + +def test_keeps_clear_does_not_blame_a_robot_standing_still_for_the_ball_coming_to_it(): + """placement_corner_robot_drives_onto_ball: the placement moved the ball 0.2 m onto a robot.""" + from utama_core.entities.referee.referee_command import RefereeCommand + + stop, dt = RefereeCommand.STOP, 1 / moments.TICKS_PER_SECOND + ball_comes = [_tick(i * dt, stop, (0.004 * i, 0.0), {1: (0.45, 0.0)}) for i in range(60)] + assert moments.keeps_clear(ball_comes) == [] + + +def test_keeps_clear_flags_a_robot_still_inside_after_the_grace_and_ignores_our_own_restart(): + from utama_core.entities.referee.referee_command import RefereeCommand + + dt = 1 / moments.TICKS_PER_SECOND + n = int((moments.LEAVE_WITHIN_S + 0.1) / dt) + stuck = [_tick(i * dt, RefereeCommand.DIRECT_FREE_BLUE, (0.0, 0.0), {2: (0.3, 0.0)}) for i in range(n)] + assert "inside the keep-out circle" in moments.keeps_clear(stuck)[0] + ours = [_tick(i * dt, RefereeCommand.DIRECT_FREE_YELLOW, (0.0, 0.0), {2: (0.3, 0.0)}) for i in range(n)] + assert moments.keeps_clear(ours) == [] diff --git a/utama_core/tests/moments/test_play_options.py b/utama_core/tests/moments/test_play_options.py new file mode 100644 index 00000000..c6d1768e --- /dev/null +++ b/utama_core/tests/moments/test_play_options.py @@ -0,0 +1,115 @@ +"""Pure checks (no rsim) of `play`'s options in `tools/moments.py`: the seed parser, the events +list and the strategy resolver.""" + +import pytest + +from tools import moments +from utama_core.entities.referee.referee_command import RefereeCommand + +DT = 1 / moments.TICKS_PER_SECOND +STOP = RefereeCommand.STOP +THEIRS = RefereeCommand.DIRECT_FREE_BLUE # the other team's restart, for us yellow +OURS = RefereeCommand.DIRECT_FREE_YELLOW + + +def _tick(i, command=None, ball=(0.0, 0.0), ours=None, ball_speed=None, foul=None): + return moments.Tick( + t=round(i * DT, 4), + command=command, + ball=ball, + ours=ours or {}, + our_speeds={}, + our_foul=foul, + ball_speed=ball_speed, + ) + + +def test_parse_seeds_takes_ranges_lists_and_both(): + assert moments.parse_seeds("0-4") == [0, 1, 2, 3, 4] + assert moments.parse_seeds("0,2,3") == [0, 2, 3] + assert moments.parse_seeds("0-1,3") == [0, 1, 3] + assert moments.parse_seeds("2,2,1") == [2, 1] + + +@pytest.mark.parametrize("spec", ["4-1", "x", "1-", "", "0,,2", "-3"]) +def test_parse_seeds_rejects_what_it_cannot_read(spec): + with pytest.raises(ValueError): + moments.parse_seeds(spec) + + +def test_events_lists_referee_changes_fouls_and_one_line_per_kick(): + ticks = [ + _tick(0, STOP, ours={1: (1.0, 0.0)}), + _tick(1, STOP, ours={1: (1.0, 0.0)}), + _tick(2, THEIRS, ours={1: (1.0, 0.0)}, foul=("keep_out", (3,))), + _tick(3, THEIRS, ours={1: (1.0, 0.0)}, foul=("keep_out", (3,)), ball_speed=2.5), + _tick(4, THEIRS, ours={1: (1.0, 0.0)}, foul=("keep_out", (3,)), ball_speed=2.4), + ] + lines = moments.events(ticks) + assert any("referee: STOP -> DIRECT_FREE_BLUE" in line for line in lines) + assert sum("foul: keep_out by robot(s) [3]" in line for line in lines) == 1 + kicks = [line for line in lines if "ball kicked" in line] + assert len(kicks) == 1 and "2.50 m/s" in kicks[0] + + +def test_events_marks_entering_and_leaving_the_keep_out_circle_only_during_restarts_against_us(): + during_stop = [_tick(0, STOP, ours={2: (0.3, 0.0)}), _tick(1, STOP, ours={2: (0.3, 0.0)})] + assert [line for line in moments.events(during_stop) if "keep-out" in line] == [ + "t=0.00s STOP: robot 2 enters the keep-out circle at 0.30 m" + ] + leaving = [_tick(0, STOP, ours={2: (0.3, 0.0)}), _tick(1, STOP, ours={2: (0.8, 0.0)})] + assert any("robot 2 leaves the keep-out circle at 0.80 m" in line for line in moments.events(leaving)) + ours = [_tick(0, OURS, ours={2: (0.3, 0.0)}), _tick(1, OURS, ours={2: (0.3, 0.0)})] + assert not any("keep-out" in line and "enters" in line for line in moments.events(ours)) + + +def test_events_gives_each_robots_closest_approach_and_skips_ticks_without_a_ball(): + ticks = [ + _tick(0, ours={1: (1.0, 0.0)}), + _tick(1, ours={1: (0.4, 0.0)}), + _tick(2, ours={1: (0.7, 0.0)}), + _tick(3, ball=None, ours={1: (0.0, 0.0)}), + ] + assert moments.events(ticks)[-1] == "robot 1 closest to the ball: 0.40 m at t=0.02s" + + +def test_events_counts_what_it_leaves_out_past_the_line_limit(): + ticks = [_tick(i, foul=("keep_out", (i % 2,))) for i in range(40)] + lines = moments.events(ticks) + assert len(lines) == moments.MAX_EVENT_LINES + assert lines[-1] == "... 11 more" + + +def test_events_of_nothing_is_nothing(): + assert moments.events([]) == [] + + +def test_factory_for_resolves_a_catalog_name_to_its_factory(): + from utama_core.strategy import kernel_strategy + + factory = moments.factory_for("build_split_shape_kernel_strategy") + assert factory is kernel_strategy.build_split_shape_kernel_strategy + + +def test_factory_for_imports_a_factory_from_a_scratch_file(tmp_path): + scratch = tmp_path / "mine.py" + scratch.write_text( + "from utama_core.strategy import kernel_strategy\n\n" + "def build_mine_kernel_strategy(ids):\n" + " return kernel_strategy.build_split_shape_kernel_strategy(ids)\n" + ) + name = f"{scratch}:build_mine_kernel_strategy" + assert callable(moments.factory_for(name)(moments.OUTFIELD_ROBOT_IDS)) + assert moments._full_name(name) == name + assert moments.label(name) == "mine:build_mine_kernel_strategy" + + +def test_factory_for_names_a_missing_file_a_missing_factory_and_an_unknown_strategy(tmp_path): + with pytest.raises(moments.MomentError, match="no such file"): + moments.factory_for(f"{tmp_path}/nope.py:build_x") + scratch = tmp_path / "other.py" + scratch.write_text("def something_else(ids):\n return None\n") + with pytest.raises(moments.MomentError, match="has no factory 'build_x'"): + moments.factory_for(f"{scratch}:build_x") + with pytest.raises(moments.MomentError, match="unknown strategy 'nope'"): + moments.factory_for("build_nope_kernel_strategy") diff --git a/utama_core/tests/moments/test_requires_and_outcomes.py b/utama_core/tests/moments/test_requires_and_outcomes.py new file mode 100644 index 00000000..f0350237 --- /dev/null +++ b/utama_core/tests/moments/test_requires_and_outcomes.py @@ -0,0 +1,86 @@ +"""Pure checks (no rsim) of the `requires` conditions in `tools/moments.py`: each fires on the case it guards and not on the one it must allow, over +synthetic ticks.""" + +import dataclasses + +import pytest + +from tools import moments +from utama_core.entities.referee.referee_command import RefereeCommand + +DT = 1 / moments.TICKS_PER_SECOND +FREE_KICK = RefereeCommand.DIRECT_FREE_BLUE + + +def _tick(t, command, ball, ours): + return moments.Tick( + t=t, + command=command, + ball=ball, + ours=ours, + our_speeds={}, + our_foul=None, + ) + + +def _probe(**changes): + return dataclasses.replace(moments.template("probe", "a note", "NORMAL_START", ["*"]), **changes) + + +# --- restart_lasts ------------------------------------------------------------------------------- + + +def test_restart_lasts_holds_when_the_restart_outlasts_its_second(): + ticks = [_tick(i * DT, FREE_KICK, (0.0, 0.0), {1: (3.0, 0.0)}) for i in range(int(2.0 / DT))] + assert moments.REQUIRES["restart_lasts"](ticks) is None + + +def test_restart_lasts_fires_when_the_restart_ends_early(): + ticks = [_tick(i * DT, FREE_KICK, (0.0, 0.0), {1: (3.0, 0.0)}) for i in range(int(0.4 / DT))] + ticks += [_tick(0.4 + i * DT, RefereeCommand.NORMAL_START, (0.0, 0.0), {1: (3.0, 0.0)}) for i in range(60)] + why = moments.REQUIRES["restart_lasts"](ticks) + assert why is not None and "DIRECT_FREE_BLUE ended at 0.40 s" in why + + +def test_restart_lasts_fires_when_the_moment_does_not_start_from_a_restart(): + ticks = [_tick(i * DT, RefereeCommand.NORMAL_START, (0.0, 0.0), {1: (3.0, 0.0)}) for i in range(120)] + assert "does not start from a restart" in moments.REQUIRES["restart_lasts"](ticks) + + +def test_restart_lasts_counts_a_restart_still_on_at_the_end_only_once_the_play_was_long_enough(): + short = [_tick(i * DT, FREE_KICK, (0.0, 0.0), {1: (3.0, 0.0)}) for i in range(int(0.5 / DT))] + assert "still on at the end" in moments.REQUIRES["restart_lasts"](short) + + +# --- robot_near_ball ----------------------------------------------------------------------------- + + +def test_robot_near_ball_holds_when_a_robot_of_ours_comes_within_a_metre(): + ticks = [_tick(0.0, FREE_KICK, (0.0, 0.0), {1: (3.0, 0.0)}), _tick(DT, FREE_KICK, (0.0, 0.0), {1: (0.9, 0.0)})] + assert moments.REQUIRES["robot_near_ball"](ticks) is None + + +def test_robot_near_ball_fires_when_none_of_ours_gets_near(): + ticks = [_tick(i * DT, FREE_KICK, (0.0, 0.0), {1: (1.5, 0.0)}) for i in range(10)] + assert "closest robot of ours to the ball was 1.50 m" in moments.REQUIRES["robot_near_ball"](ticks) + + +def test_a_failed_requirement_is_reported_as_did_not_arise(): + m = _probe(requires=["robot_near_ball"], checks=[]) + far = [_tick(0.0, None, (0.0, 0.0), {1: (3.0, 0.0)})] + assert moments.judge(m, far) == [ + "did not arise: robot_near_ball (the closest robot of ours to the ball was 3.00 m, need 1.0 m)" + ] + near = [_tick(0.0, None, (0.0, 0.0), {1: (0.5, 0.0)})] + assert moments.judge(m, near) == [] + + +def test_requires_round_trip_through_json_and_unknown_ones_are_named(): + m = _probe(requires=["restart_lasts", "robot_near_ball"], checks=["*"]) + assert moments.from_dict(moments.to_dict(m)).requires == ["restart_lasts", "robot_near_ball"] + with pytest.raises(moments.MomentError, match=r"unknown requires 'ball_kickd'"): + moments.validate(_probe(requires=["ball_kickd"])) + + +def test_requires_and_checks_are_registered_by_name(): + assert {"restart_lasts", "robot_near_ball"} == set(moments.REQUIRES) diff --git a/utama_core/tests/motion_planning/fastpathplanner_corner_test.py b/utama_core/tests/motion_planning/fastpathplanner_corner_test.py index b76a500f..31e5f1fb 100644 --- a/utama_core/tests/motion_planning/fastpathplanner_corner_test.py +++ b/utama_core/tests/motion_planning/fastpathplanner_corner_test.py @@ -38,7 +38,12 @@ # Real enemy defense area with my_team_is_right=False (enemy goal at +x). _BOX = (3.5, 4.5, -1.0, 1.0) _SEEDS = range(40) -_KNOWN_STUCK = {0, 19, 26, 30} +# 19 and 30 arrive since a robot pressed against a blocker may drive straight away from it +# (planner.py step 3b). +_KNOWN_STUCK = {0, 26} +# On the edge of the same flip: arrives on some CPUs (here at tick 64, after 14 carrot reversals) and +# sticks on CI's runners, whose float results differ slightly. Neither outcome fails the run. +_HOST_DEPENDENT = {28} def _in_box(x: float, y: float) -> bool: @@ -107,7 +112,11 @@ def test_corner_route_is_safe(seed): ( pytest.param(s, marks=pytest.mark.xfail(strict=True, reason="FPP detour-side flip (roadmap item 5)")) if s in _KNOWN_STUCK - else s + else ( + pytest.param(s, marks=pytest.mark.xfail(strict=False, reason="on the edge of the flip; differs by CPU")) + if s in _HOST_DEPENDENT + else s + ) ) for s in _SEEDS ], diff --git a/utama_core/tests/motion_planning/fastpathplanner_subgoal_test.py b/utama_core/tests/motion_planning/fastpathplanner_subgoal_test.py index c114782e..95cd43b2 100644 --- a/utama_core/tests/motion_planning/fastpathplanner_subgoal_test.py +++ b/utama_core/tests/motion_planning/fastpathplanner_subgoal_test.py @@ -2,7 +2,7 @@ detour-recursion failsafe -- no rsim/StrategyRunner needed, since the bug and fix live entirely in this pure-geometry recursion. -Found live in a full_match_tournament.py replay +Found live in a round-robin replay (counter_press_vs_tiki_taka_RK.pkl, 2026-09-01): a robot standing right at the field's boundary wall, routing toward a target on the other side of the field, got a carrot that flip-flopped between the real route and a bogus diff --git a/utama_core/tests/motion_planning/motion_planning_benchmark_figures_test.py b/utama_core/tests/motion_planning/motion_planning_benchmark_figures_test.py new file mode 100644 index 00000000..8e79d14c --- /dev/null +++ b/utama_core/tests/motion_planning/motion_planning_benchmark_figures_test.py @@ -0,0 +1,113 @@ +"""Pure tests for the benchmark's `--figures` output: no rsim run, a synthetic 20-tick record.""" + +import math +from types import SimpleNamespace + +from tools.motion_planning_benchmark import ( + SCHEMES, + BenchmarkManager, + Scenario, + write_figures, +) + +PNG_MAGIC = b"\x89PNG\r\n\x1a\n" +TICKS = 20 + + +def _trace(friendly_speed: float, opponent_dy: float) -> dict: + """Friendly robot 0 runs along +x; an opponent stands still at (0, 0.3) and another runs + across at `opponent_dy` per tick, so one scheme's trace has clearance and the other's has none.""" + t = [k / 60.0 for k in range(TICKS)] + friendly = [(-2.0 + friendly_speed * tk, 0.0) for tk in t] + opponent = [(0.0, 0.3 + opponent_dy * k) for k in range(TICKS)] + static = [(1.0, -0.5)] * TICKS + return { + "t": t, + "robots": { + "friendly:0": {"xy": friendly, "speed": [friendly_speed] * TICKS}, + "enemy:0": {"xy": opponent, "speed": [abs(opponent_dy) * 60.0] * TICKS}, + "enemy:1": {"xy": static, "speed": [0.0] * TICKS}, + }, + "clearance": [math.dist(friendly[k], opponent[k]) - 0.18 for k in range(TICKS)], + } + + +def _aggregate(scenario: str, scheme: str) -> dict: + return { + "scenario": scenario, + "scheme": scheme, + "passed": scheme == "fpp", + "sim_time_mean_s": 3.0, + "path_length_ratio_mean": 1.02, + "min_clearance_worst_m": 0.1, + "collision_events_total": 0, + "final_error_max_worst_m": 0.05, + "stopped_while_unreached_ratio_mean": 0.01, + "controller_mean_ms": 0.2, + "controller_p95_ms_mean": 0.4, + "max_speed_worst_mps": 1.5, + } + + +def _scenario(name: str, friendly_targets, enemy_starts) -> Scenario: + return Scenario( + name=name, + description="Synthetic scenario for the figure test.", + friendly_starts=((-2.0, 0.0),), + friendly_targets=friendly_targets, + enemy_starts=enemy_starts, + ) + + +def test_figures_are_written_as_png(tmp_path): + schemes = [SCHEMES[0], SCHEMES[1]] + scenario = _scenario("synthetic_crossing", ((2.0, 0.0),), ((0.0, 0.3), (1.0, -0.5))) + payload = { + "schemes": schemes, + "scenarios": [scenario.name], + "aggregates": [_aggregate(scenario.name, s) for s in schemes], + } + traces = { + (scenario.name, schemes[0]): _trace(friendly_speed=0.2, opponent_dy=0.01), + (scenario.name, schemes[1]): _trace(friendly_speed=0.1, opponent_dy=0.0), + } + + written = write_figures(payload, traces, tmp_path, {scenario.name: scenario}) + + assert [p.name for p in written] == ["trajectories_synthetic_crossing.png", "summary.png"] + for path in written: + data = path.read_bytes() + assert data[:8] == PNG_MAGIC, path + assert len(data) > 1000, path + + +def test_single_robot_scenario_without_obstacles_still_writes_figures(tmp_path): + scheme = SCHEMES[0] + scenario = _scenario("synthetic_direct", ((2.0, 0.0),), ()) + trace = _trace(friendly_speed=0.2, opponent_dy=0.0) + trace["robots"] = {"friendly:0": trace["robots"]["friendly:0"]} + trace["clearance"] = [None] * TICKS + payload = {"schemes": [scheme], "scenarios": [scenario.name], "aggregates": [_aggregate(scenario.name, scheme)]} + + written = write_figures(payload, {(scenario.name, scheme): trace}, tmp_path, {scenario.name: scenario}) + + assert (tmp_path / "trajectories_synthetic_direct.png").read_bytes()[:8] == PNG_MAGIC + assert written[-1] == tmp_path / "summary.png" + + +def test_default_manager_records_nothing_and_recording_captures_each_tick(): + scenario = _scenario("synthetic_default", ((2.0, 0.0),), ((0.0, 0.5),)) + assert BenchmarkManager(scenario).trace is None + + manager = BenchmarkManager(scenario, record_trace=True) + robots = { + ("friendly", 0): SimpleNamespace(p=SimpleNamespace(x=0.0, y=0.0), v=SimpleNamespace(x=0.3, y=0.4)), + ("enemy", 0): SimpleNamespace(p=SimpleNamespace(x=0.0, y=0.5), v=SimpleNamespace(x=0.0, y=0.0)), + } + manager._record_tick(0.0, robots, min_distance=0.5) + + assert manager.trace["t"] == [0.0] + assert manager.trace["robots"]["friendly:0"] == {"xy": [(0.0, 0.0)], "speed": [0.5]} + assert manager.trace["robots"]["enemy:0"]["speed"] == [0.0] + # Clearance is the nearest centre distance minus both radii, the same as `min_clearance_m`. + assert math.isclose(manager.trace["clearance"][0], 0.5 - 0.18) diff --git a/utama_core/tests/motion_planning/test_fpp_inside_ball_keep_out.py b/utama_core/tests/motion_planning/test_fpp_inside_ball_keep_out.py new file mode 100644 index 00000000..50649da8 --- /dev/null +++ b/utama_core/tests/motion_planning/test_fpp_inside_ball_keep_out.py @@ -0,0 +1,83 @@ +"""FastPathPlanner never takes a robot already inside the ball's keep-out circle closer to +the ball during a restart that keeps it out. + +Moment placement_corner_robot_drives_onto_ball (tournament_20261017_002337): during the +opponent's ball placement, robot 1 sat 0.16 m from the ball in a corner, boxed in by enemies 4 +and 5. The carrot the planner gave it swung round enemy 4 and ran within 0.13 m of the ball +(the robot's own run toward that carrot closes on the ball). Step 8 guarded only robots +outside the circle. + +The state is one the moment reaches about half a second in, not its start: from rest the +carrot at the start brings the robot only 0.003 m closer, inside the tolerance. +""" + +import numpy as np + +from utama_core.config.field_params import STANDARD_FIELD_DIMS +from utama_core.config.referee_constants import BALL_KEEP_OUT_DISTANCE +from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.global_utils.math_utils import distance_point_to_segment +from utama_core.motion_planning.src.fastpathplanning.planner import FastPathPlanner +from utama_core.tests.fixtures.game_builder import build_game, make_ball, make_robot + +_BALL = (-3.747805, -2.497416) +_US = (-3.909004, -2.467464) +_TARGET = (-4.4, -1.891450) # the referee's push-out point +# All twelve robots of the captured state (id: x, y, orientation). The carrot depends on the +# far ones too: with only the robots within 1.5 m the planner gives a straight run that +# never nears the ball. +_FRIENDLY = { + 0: (4.40873, 0.05898, -3.092253), + 1: (-3.909004, -2.467464, 2.206669), + 2: (-1.797528, 0.747956, -2.172401), + 3: (-1.801786, -0.748043, -2.449482), + 4: (-3.358823, -2.494843, -3.6e-05), + 5: (-3.419104, -1.959768, 0.973707), +} +_ENEMY = { + 0: (-4.445358, -0.409642, -0.002635), + 1: (-2.546535, -0.71166, -2.220263), + 2: (-3.958383, -1.727368, 1.734304), + 3: (-2.36478, 0.621993, -2.121389), + 4: (-3.953127, -2.293594, -2.541111), + 5: (-4.216878, -2.250004, 1.885296), +} + + +def test_carrot_does_not_bring_a_robot_inside_the_ball_keep_out_closer_to_the_ball(): + game = build_game( + {rid: make_robot(rid, is_friendly=True, x=x, y=y, orientation=o) for rid, (x, y, o) in _FRIENDLY.items()}, + {rid: make_robot(rid, is_friendly=False, x=x, y=y, orientation=o) for rid, (x, y, o) in _ENEMY.items()}, + make_ball(*_BALL), + referee_command=RefereeCommand.BALL_PLACEMENT_BLUE, + ) + carrot = FastPathPlanner(env=None)._path_to(game, 1, _TARGET, STANDARD_FIELD_DIMS.full_field_bounds) + ball, us = np.array(_BALL), np.array(_US) + start_gap = np.linalg.norm(us - ball) + # Smoothing may graze the start gap by a centimetre; without the fix the carrot runs + # about 0.02 m closer than that, and the robot reaches 0.11 m from the ball. + assert distance_point_to_segment(ball, us, carrot) >= start_gap - 0.01 + + +def test_a_robot_just_inside_the_ball_keep_out_is_sent_out_of_it_not_onto_its_edge(): + """counter_flow_vs_tiki_taka_plus (tournament_20261017_151052), 252-267 s: in the other + team's kick-off a robot 0.795 m from the ball, just inside the 0.8 m circle, was sent by + the kick-off step round the circle toward its spot in its own half. That run is a chord + that closes on the ball, so the planner replaced it with the push-out point on the circle, + 0.005 m away: the robot parked there, still inside, and the kick-off waited the referee's + full 15 s cap. The push-out must take it clear of the circle.""" + us = (0.762, -0.225) + round_the_circle = (0.8 * np.cos(np.radians(-76)), 0.8 * np.sin(np.radians(-76))) + game = build_game( + {4: make_robot(4, is_friendly=True, x=us[0], y=us[1])}, + {0: make_robot(0, is_friendly=False, x=0.12, y=0.0)}, + make_ball(0.0, 0.0), + my_team_is_right=False, + my_team_is_yellow=False, + referee_command=RefereeCommand.PREPARE_KICKOFF_YELLOW, + ) + carrot = np.asarray( + FastPathPlanner(env=None)._path_to(game, 4, round_the_circle, STANDARD_FIELD_DIMS.full_field_bounds) + ) + assert np.linalg.norm(carrot) > BALL_KEEP_OUT_DISTANCE + 0.02 + assert distance_point_to_segment(np.zeros(2), np.array(us), carrot) >= np.linalg.norm(us) - 1e-6 diff --git a/utama_core/tests/motion_planning/test_fpp_pressed_against_robot.py b/utama_core/tests/motion_planning/test_fpp_pressed_against_robot.py new file mode 100644 index 00000000..cc7885db --- /dev/null +++ b/utama_core/tests/motion_planning/test_fpp_pressed_against_robot.py @@ -0,0 +1,52 @@ +"""FastPathPlanner lets a robot pressed against another robot (inside its clearance) drive +straight away from it. + +Moment placement_in_corner_traps_defender (tournament_20261017_002337): a defender 0.18 m +from the opponent's placer, inside the 0.216 m clearance, could plan no segment out, the +straight one away included; the detour ran back through the placer and it never left. +""" + +import numpy as np +import pytest + +from utama_core.config.field_params import STANDARD_FIELD_DIMS +from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.global_utils.math_utils import distance_point_to_segment +from utama_core.motion_planning.src.fastpathplanning.planner import ( + FastPathPlanner, + _leaving, +) +from utama_core.tests.fixtures.game_builder import build_game, make_ball, make_robot + +_US, _PLACER, _TARGET = (4.28, 2.35), (4.1, 2.37), (4.36, 1.78) + + +def test_planner_drives_a_pressed_robot_away_from_what_it_is_pressed_against(): + game = build_game( + {2: make_robot(2, is_friendly=True, x=_US[0], y=_US[1])}, + {3: make_robot(3, is_friendly=False, x=_PLACER[0], y=_PLACER[1])}, + make_ball(4.0, 2.5), + referee_command=RefereeCommand.BALL_PLACEMENT_BLUE, + ) + carrot = FastPathPlanner(env=None)._path_to(game, 2, _TARGET, STANDARD_FIELD_DIMS.full_field_bounds) + start_gap = np.hypot(_US[0] - _PLACER[0], _US[1] - _PLACER[1]) + # The carrot is smoothed, not the straight run itself: allow it to graze 1 cm closer. + # Without the fix it runs back past the placer, about 0.05 m from it. + assert distance_point_to_segment(np.array(_PLACER), np.array(_US), carrot) >= start_gap - 0.01 + + +_POINT = (np.array([0.0, 0.0]), np.array([0.0, 0.0])) # a robot obstacle at the origin + + +@pytest.mark.parametrize( + "start, target, leaving", + [ + ((0.1, 0.0), (1.0, 0.0), True), # inside, straight away + ((0.1, 0.0), (0.1, 1.0), True), # inside, square to it: never closer + ((0.1, 0.0), (-1.0, 0.0), False), # inside, through it + ((0.1, 0.0), (0.0, 1.0), False), # inside, closer on the way + ((0.3, 0.0), (1.0, 0.0), False), # outside the clearance: an ordinary obstacle + ], +) +def test_leaving_is_inside_the_clearance_and_never_closer(start, target, leaving): + assert _leaving(np.array(start), np.array(target), _POINT, clearance=0.216) is leaving diff --git a/utama_core/tests/referee/demo_referee_gui_rsim.py b/utama_core/tests/referee/demo_referee_gui_rsim.py deleted file mode 100644 index caaf51cf..00000000 --- a/utama_core/tests/referee/demo_referee_gui_rsim.py +++ /dev/null @@ -1,73 +0,0 @@ -"""demo_referee_gui_rsim.py — CustomReferee + web GUI + StrategyRunner (RSim). - -Run: - pixi run python utama_core/tests/referee/demo_referee_gui_rsim.py - # RSim window opens; open http://localhost:8080 in a browser - -What it does: - - Creates a CustomReferee (human profile) and attaches it to the browser - dashboard's referee tab. - - Passes the referee to StrategyRunner via referee=. StrategyRunner - calls referee.step() on every tick and handles ball teleports on STOP - automatically — no patching required. - - WanderingTactic is used as the base strategy so robots visibly move and - you can watch RefereeOverride interrupt them when you issue - commands from the dashboard (Halt, Kickoff Yellow, etc.). - -Operator workflow: - 1. Open http://localhost:8080 in a browser. - 2. Robots start moving under WanderingTactic. - 3. Click any command button (Halt, Stop, Kickoff Yellow…) — robots reposition. - 4. Click Normal Start to resume free play. - 5. With the human profile, the referee stays in STOP after a goal until the operator advances play. -""" - -from utama_core.custom_referee import CustomReferee -from utama_core.custom_referee.profiles.profile_loader import load_profile -from utama_core.dashboard import attach_dashboard -from utama_core.dashboard.views import referee as referee_view -from utama_core.run import StrategyRunner -from utama_core.tests.referee.wandering_strategy import wandering_strategy - -# --------------------------------------------------------------------------- -# Configuration -# --------------------------------------------------------------------------- - -PROFILE = "human" # "human" or "simulation" -DASHBOARD_PORT = 8080 -N_ROBOTS = 3 # robots per side -MY_TEAM_IS_YELLOW = True -MY_TEAM_IS_RIGHT = True - -# --------------------------------------------------------------------------- -# Entry point -# --------------------------------------------------------------------------- - - -def main() -> None: - profile = load_profile(PROFILE) - - referee = CustomReferee(profile, n_robots_yellow=N_ROBOTS, n_robots_blue=N_ROBOTS) - - # attach_dashboard() starts the HTTP server in a background daemon - # thread; referee.step() is called by StrategyRunner on every tick, and - # the referee tab receives state automatically after each call. - server = attach_dashboard(port=DASHBOARD_PORT) - referee_view.attach(server, referee, profile) - - runner = StrategyRunner( - strategy=wandering_strategy(tuple(range(N_ROBOTS))), - my_team_is_yellow=MY_TEAM_IS_YELLOW, - my_team_is_right=MY_TEAM_IS_RIGHT, - mode="rsim", - exp_friendly=N_ROBOTS, - exp_enemy=N_ROBOTS, - referee=referee, # StrategyRunner drives referee.step() each tick - show_live_status=True, - ) - - runner.run() - - -if __name__ == "__main__": - main() diff --git a/utama_core/tests/referee/test_referee_unit.py b/utama_core/tests/referee/test_referee_unit.py index 6b683dd5..506f4f95 100644 --- a/utama_core/tests/referee/test_referee_unit.py +++ b/utama_core/tests/referee/test_referee_unit.py @@ -451,6 +451,11 @@ def fake_move(game, motion_controller, robot_id, target_coords, target_oren, dri ball_pos.y - offset * math.sin(oren), ) expected = referee_actions._clamp_to_field_or_ball(approach, game, ball_pos) + # The placer starts on the designated side, so it routes round the ball to the + # (clamped) approach point rather than straight at it. + from utama_core.global_utils.math_utils import detour_around_circle + + expected = detour_around_circle(Vector2D(4.0, 0.0), expected, ball_pos, ROBOT_RADIUS + BALL_RADIUS) assert captured[0][1].x == pytest.approx(expected.x) assert captured[0][1].y == pytest.approx(expected.y) assert captured[0][2] is True @@ -840,6 +845,78 @@ def fake_move(game, motion_controller, robot_id, target_coords, target_oren, dri assert support_move[2] is False +class TestBallPlacementOursApproachAroundBall: + """Our placer approaches the ball's behind-point round the ball, not through it. + + A hand-made moment (since dropped): the placer starts on the designated side, and its straight run + to the approach point passed 0.03 m from the ball, so it drove into the ball and pushed it + off the spot. + """ + + CLEARANCE = ROBOT_RADIUS + BALL_RADIUS + BALL = (-2.0, -3.0) + DESIGNATED = (2.0, -3.0) + APPROACH = (-2.0 - 0.10, -3.0) # 0.10 m behind the ball, on the carry axis + + def _placer_target(self, monkeypatch, robot_xy): + from utama_core.custom_referee import actions as referee_actions + + captured = {} + + def fake_move(game, motion_controller, robot_id, target_coords, target_oren, dribbling=False): + captured[robot_id] = target_coords + return ("move", robot_id) + + monkeypatch.setattr(referee_actions, "move", fake_move) + referee = _make_referee_data(command=RefereeCommand.BALL_PLACEMENT_YELLOW) + referee.designated_position = self.DESIGNATED + game = _make_game( + friendly_robots={4: _robot(4, *robot_xy)}, + referee=referee, + my_team_is_yellow=True, + my_team_is_right=True, + ball=_ball(*self.BALL), + ) + node = referee_actions.BallPlacementOursStep() + node.blackboard = _make_blackboard(game, _make_cmd_map(game)) + node.update() + return captured[4] + + @staticmethod + def _min_distance_to_ball(start, end, ball): + seg = Vector2D(end.x - start.x, end.y - start.y) + rel = Vector2D(ball[0] - start.x, ball[1] - start.y) + t = max(0.0, min(1.0, (rel.x * seg.x + rel.y * seg.y) / (seg.x**2 + seg.y**2))) + closest = Vector2D(start.x + seg.x * t, start.y + seg.y * t) + return math.hypot(closest.x - ball[0], closest.y - ball[1]) + + def test_placer_on_designated_side_routes_round_the_ball(self, monkeypatch): + """Straight run from (1,-2) to the approach point passes 0.03 m from the ball: the + target handed to the planner must be a waypoint whose leg keeps a robot radius plus a + ball radius from the ball, on the placer's own side.""" + start = Vector2D(1.0, -2.0) + target = self._placer_target(monkeypatch, (start.x, start.y)) + + assert (target.x, target.y) != pytest.approx(self.APPROACH) + assert self._min_distance_to_ball(start, target, self.BALL) >= self.CLEARANCE - 1e-9 + assert target.y > self.BALL[1] # round the side the placer is on + + def test_placer_level_with_the_ball_goes_straight_to_the_approach_point(self, monkeypatch): + """Boundary of 'behind': a placer level with the ball (zero component along the carry + direction) is not on the designated side, so its run to the approach point is not + detoured.""" + target = self._placer_target(monkeypatch, (-2.0, -1.0)) + + assert (target.x, target.y) == pytest.approx(self.APPROACH) + + def test_placer_already_behind_the_ball_heads_straight_to_the_approach_point(self, monkeypatch): + """Behind the ball (on the far side from the designated position), the run to the approach + point is the final approach and goes straight there, not round the ball.""" + target = self._placer_target(monkeypatch, (-2.5, -3.0)) + + assert (target.x, target.y) == pytest.approx(self.APPROACH) + + # --------------------------------------------------------------------------- # Keep-out retreat and penalty positioning # --------------------------------------------------------------------------- @@ -1901,6 +1978,29 @@ def test_robot_between_ball_and_opponent_box_clears_round_the_ball(self, monkeyp assert (target - Vector2D(-2.5, 0.0)).mag() == pytest.approx(0.8) assert target.x >= -3.5 + 0.25 + 0.28 # outside the planner's margin round the box + def test_robot_by_a_ball_in_the_corner_clears_round_the_ball_onto_the_pitch(self, monkeypatch): + """Ball at (4.0, 2.5): straight out from it the robot's spot is off the pitch, and + clamped back on (x <= 4.4) it was 0.48 m from the ball, so the robot never left + (moment placement_in_corner_traps_defender).""" + from utama_core.custom_referee import actions as referee_actions + + captured = [] + monkeypatch.setattr( + referee_actions, "move", lambda game, mc, rid, target, oren, dribbling=False: captured.append(target) + ) + + robots = {0: _robot(0, 4.28, 2.36)} + referee = _make_referee_data(command=RefereeCommand.DIRECT_FREE_BLUE) + game = _make_game(friendly_robots=robots, referee=referee, my_team_is_right=True, ball=_ball(4.0, 2.5)) + node = referee_actions.DirectFreeTheirsStep() + node.blackboard = _make_blackboard(game, _make_cmd_map(game)) + + node.update() + + (target,) = captured + assert (target - Vector2D(4.0, 2.5)).mag() == pytest.approx(0.8) + assert referee_actions._clamp_to_field(target, game) == target + def test_all_robots_get_commands(self, monkeypatch): from utama_core.custom_referee import actions as referee_actions diff --git a/utama_core/tests/replay/test_columnar_replay.py b/utama_core/tests/replay/test_columnar_replay.py index f83ed427..821b588c 100644 --- a/utama_core/tests/replay/test_columnar_replay.py +++ b/utama_core/tests/replay/test_columnar_replay.py @@ -416,6 +416,25 @@ def test_a_replay_from_before_the_side_was_stored_per_tick_keeps_one_side(tmp_pa assert replay.frame_at(1).my_team_is_right is False +def test_a_scenario_time_counts_from_the_first_frame_not_from_zero(tmp_path): + # The sidecar's and `fouls`' times count from the first frame (1/60 s here), so t=0.1 is the + # frame at 1/60 + 0.1 = 7/60 s. Counting from zero would pick the frame at 6/60 s instead. + from utama_core.replay.scenario import scenario_from_replay + + frames = [ + dataclasses.replace( + _side_frame(k / 60, True), + ball=Ball(p=Vector3D(float(k), 0.0, 0.0), v=Vector3D(0.0, 0.0, 0.0), a=Vector3D(0.0, 0.0, 0.0)), + ) + for k in range(1, 9) + ] + _write(tmp_path, frames) + + scenario = scenario_from_replay(tmp_path / "test_replay.npz", 0.1) + + assert scenario.ball_x == pytest.approx(7.0) + + def test_a_scenario_from_the_second_half_is_turned_to_config_a_s_right_side(tmp_path): from utama_core.replay.scenario import scenario_from_replay diff --git a/utama_core/tests/replay/test_convert_corpus.py b/utama_core/tests/replay/test_convert_corpus.py new file mode 100644 index 00000000..d8bc8407 --- /dev/null +++ b/utama_core/tests/replay/test_convert_corpus.py @@ -0,0 +1,146 @@ +"""`tools/convert_ssl_log.py`'s per-log corpus job: what it downloads, converts and keeps.""" + +from __future__ import annotations + +import json + +from tools import convert_ssl_log + + +def _job(tmp_path, keep_raw): + return { + "name": "2024-07-01_10-00_A-vs-B.log.gz", + "url": "https://example.invalid/log", + "bytes": 3, # the fake download, b"log" + "year": 2024, + "out": str(tmp_path / "out"), + "raw_dir": str(tmp_path / "raw"), + "raw_vision": False, + "backfill_extras": True, + "keep_raw": keep_raw, + } + + +def _fakes(monkeypatch): + calls = {"download": 0, "convert": 0} + + def _download(_url, path, _expected_bytes=0): + calls["download"] += 1 + path.write_bytes(b"log") # 3 bytes + + def _convert(_raw, out_dir, stem, raw_vision, write_replay): + calls["convert"] += 1 + out_dir.mkdir(parents=True, exist_ok=True) + (out_dir / f"{stem}.npz").write_bytes(b"") + (out_dir / f"{stem}.source.json").write_text(json.dumps({"extras": {"raw_vision": raw_vision}})) + return {} + + monkeypatch.setattr(convert_ssl_log, "_download", _download) + monkeypatch.setattr(convert_ssl_log, "convert_log", _convert) + monkeypatch.setattr(convert_ssl_log, "_summary_line", lambda stem, _summary: stem) + return calls + + +def test_without_keep_raw_the_download_is_deleted_after_converting(tmp_path, monkeypatch): + calls = _fakes(monkeypatch) + + assert convert_ssl_log._corpus_job(_job(tmp_path, keep_raw=False))["status"] == "converted" + + assert calls == {"download": 1, "convert": 1} + assert not list((tmp_path / "raw").rglob("*.log.gz")) + + +def test_keep_raw_keeps_the_download_per_year_and_reuses_it(tmp_path, monkeypatch): + calls = _fakes(monkeypatch) + job = _job(tmp_path, keep_raw=True) + + assert convert_ssl_log._corpus_job(job)["status"] == "converted" + assert (tmp_path / "raw" / "2024" / "2024-07-01_10-00_A-vs-B.log.gz").exists() + assert convert_ssl_log._corpus_job(job)["status"] == "exists" + + assert calls == {"download": 1, "convert": 1} + + +def test_keep_raw_downloads_only_the_raw_log_of_an_otherwise_complete_match(tmp_path, monkeypatch): + calls = _fakes(monkeypatch) + convert_ssl_log._corpus_job(_job(tmp_path, keep_raw=False)) + + assert convert_ssl_log._corpus_job(_job(tmp_path, keep_raw=True))["status"] == "raw kept" + + assert calls == {"download": 2, "convert": 1} + assert (tmp_path / "raw" / "2024" / "2024-07-01_10-00_A-vs-B.log.gz").exists() + + +def test_keep_raw_reuses_a_download_left_by_an_interrupted_run(tmp_path, monkeypatch): + calls = _fakes(monkeypatch) + raw = tmp_path / "raw" / "2024" / "2024-07-01_10-00_A-vs-B.log.gz" + raw.parent.mkdir(parents=True) + raw.write_bytes(b"log") + + assert convert_ssl_log._corpus_job(_job(tmp_path, keep_raw=True))["status"] == "converted" + + assert calls == {"download": 0, "convert": 1} + + +def test_limit_memory_turns_an_oversized_allocation_into_memory_error(): + # in a child process: the limit cannot be raised again once lowered + import subprocess + import sys + + code = ( + "from tools.convert_ssl_log import limit_memory\n" + "limit_memory(1.5)\n" + "try:\n" + " bytearray(2 * 2**30)\n" + "except MemoryError:\n" + " print('MemoryError')\n" + ) + result = subprocess.run([sys.executable, "-c", code], capture_output=True, text=True, check=True) + assert result.stdout.strip() == "MemoryError" + + +class _Response: + def __init__(self, body, status): + self._body, self.status = body, status + + def read(self, n=-1): + chunk, self._body = (self._body, b"") if n < 0 else (self._body[:n], self._body[n:]) + return chunk + + def __enter__(self): + return self + + def __exit__(self, *_): + return False + + +def test_a_download_cut_short_resumes_from_where_it_stopped(tmp_path, monkeypatch): + body = b"0123456789" + requests = [] + + def _urlopen(request, timeout): + start = int(request.get_header("Range", "bytes=0-")[6:-1]) + requests.append(start) + if start == 0: + return _Response(body[:6], 200) # the share closes the connection early, no error + return _Response(body[start:], 206) + + monkeypatch.setattr(convert_ssl_log.urllib.request, "urlopen", _urlopen) + monkeypatch.setattr(convert_ssl_log.time, "sleep", lambda _s: None) + path = tmp_path / "log.gz" + + convert_ssl_log._download("https://example.invalid/log", path, expected_bytes=len(body)) + + assert path.read_bytes() == body + assert requests == [0, 6] + + +def test_a_kept_download_of_the_wrong_size_is_fetched_again(tmp_path, monkeypatch): + calls = _fakes(monkeypatch) + job = {**_job(tmp_path, keep_raw=True), "bytes": 3} + raw = tmp_path / "raw" / "2024" / "2024-07-01_10-00_A-vs-B.log.gz" + raw.parent.mkdir(parents=True) + raw.write_bytes(b"lo") # cut short by an earlier run + + assert convert_ssl_log._corpus_job(job)["status"] == "converted" + assert calls["download"] == 1 diff --git a/utama_core/tests/replay/test_fingerprint.py b/utama_core/tests/replay/test_fingerprint.py index 58a5d9b4..cf2964df 100644 --- a/utama_core/tests/replay/test_fingerprint.py +++ b/utama_core/tests/replay/test_fingerprint.py @@ -14,7 +14,6 @@ from utama_core.replay import fingerprint as fp from utama_core.replay.fingerprint import ( - BENCH_ENTRIES, KERNEL_STRATEGY, REPO_ROOT, ROUND_ROBIN_ENTRY, @@ -166,7 +165,7 @@ def test_editing_the_shared_pickers_changes_the_configs_that_import_them(repo): def test_the_registry_is_not_part_of_any_strategy_fingerprint(repo): - assert _changed_after(repo, KS, '"""Factories."""', '"""Factories, edited."""') == set() + assert _changed_after(repo, KS, '"""Factories."""', '"""Factories."""\n_EDITED = 1') == set() def test_pointing_a_factory_at_another_module_changes_that_config(repo): @@ -230,12 +229,12 @@ def test_shared_code_importing_kernel_strategy_puts_every_strategy_module_in_the path = repo / rel path.write_text("from utama_core.strategy.kernel_strategy import _shared_helper\n\n\n" + path.read_text()) assert _changed_after(repo, B_PY, "_ONLY_B = 3", "_ONLY_B = 4") == {"base", "build_b_kernel_strategy"} - assert _changed_after(repo, PICKERS_PY, "# used by the shared helper", "# edited") == { + assert _changed_after(repo, PICKERS_PY, "_MARGIN = 0.05", "_MARGIN = 0.06") == { "base", "build_a_kernel_strategy", "build_b_kernel_strategy", } - assert _changed_after(repo, KS, '"""Factories."""', '"""Factories, edited."""') == {"base"} + assert _changed_after(repo, KS, '"""Factories."""', '"""Factories."""\n_EDITED = 1') == {"base"} def test_the_rsim_subprocess_script_and_data_files_are_in_the_base(repo): @@ -280,11 +279,15 @@ def test_match_key_covers_every_setting(repo): match_key(g, a, b, duration_seconds=65.0, control_scheme="dwa"), match_key(g, a, b, duration_seconds=65.0, fuzz_seed=1), match_key(g, a, b, duration_seconds=65.0, recorded=False), + match_key(g, a, b, duration_seconds=65.0, sim_noise={"seed": 1}), + match_key(g, a, b, duration_seconds=65.0, sim_noise={"seed": 2}), ] assert len({ref, *variants}) == 1 + len(variants) # the fuzz interval only matters when fuzzing is on assert match_key(g, a, b, duration_seconds=65.0, fuzz_interval_s=(1.0, 2.0)) == ref assert match_key(g, a, b, duration_seconds=65.0, fuzz_seed=1, fuzz_interval_s=(1.0, 2.0)) != variants[5] + # matches without sim noise keep the keys they had before the setting existed + assert match_key(g, a, b, duration_seconds=65.0, sim_noise=None) == ref def test_numerics_env_vars_change_the_base(repo, monkeypatch): @@ -361,7 +364,7 @@ def test_every_tactic_a_factory_builds_is_in_its_closure(graph): def test_the_tactics_kernel_strategy_imports_are_outside_the_base(graph): """Otherwise a tactic edit would rerun every match: the per-config split is the point.""" - base = graph.base_modules([ROUND_ROBIN_ENTRY]) | graph.base_modules(BENCH_ENTRIES) + base = graph.base_modules([ROUND_ROBIN_ENTRY]) for name in ("give_and_go", "press_and_contain", "shadow_and_mark", "switch_of_play", "defense"): assert f"utama_core.tactics.{name}" not in base @@ -377,13 +380,12 @@ def test_the_base_holds_the_sim_script_and_referee_profiles(graph): # it doesn't need to), then add it. # environment: settings.py reads UTAMA_EXACT_MATH -> ENV_PREFIXES; robosim_wrapper copies # the environment into the sim subprocess -> ENV_PREFIXES covers the numerics ones. -# opens files: profile_loader reads profiles/*.yaml -> data_files. start reads a -# bank, whose start is itself in the bench key. The rest write a match's outputs (match -# log, stats, replays) or read old ones for harvesting, never during play. +# opens files: profile_loader reads profiles/*.yaml -> data_files. The rest write a +# match's outputs (match log, stats, replays) or read old ones for analysis, never during play. # subprocess: robosim_wrapper starts robosim_subprocess.py in the robosim pixi env -> # SUBPROCESS_SCRIPTS, and environment_pin's robosim env listing and binary hashes. -# getattr on a module by computed name: tools.evaluation.match and scenario_scorer look a factory -# up by name -> the config names are the fingerprint's own inputs. +# getattr on a module by computed name: tools.evaluation.match looks a factory up by name -> +# the config names are the fingerprint's own inputs. # module-level state: code in the closure, so already hashed; its values must not carry # from one match to the next in a worker, which the cache's replay spot-check verifies. # possession/shield state: reset in StrategyRunner.__init__; the planner's _PERP_ROTATIONS: a memo of @@ -393,10 +395,7 @@ def test_the_base_holds_the_sim_script_and_referee_profiles(graph): "utama_core.config.settings", "utama_core.rsoccer_simulator.src.Simulators.robosim.robosim_wrapper", ], - "getattr on a module by computed name": [ - "tools.evaluation.match", - "utama_core.scenario_bench.scenario_scorer", - ], + "getattr on a module by computed name": ["tools.evaluation.match"], "module-level state": [ "utama_core.motion_planning.src.fastpathplanning.planner", "utama_core.rsoccer_simulator.src.Simulators.robosim.robosim_wrapper", @@ -404,23 +403,42 @@ def test_the_base_holds_the_sim_script_and_referee_profiles(graph): "utama_core.skills.src.shielding", ], "opens files": [ - "utama_core.analysis.turnover_breakdown", "utama_core.custom_referee.profiles.profile_loader", "utama_core.engine.match_log", "utama_core.engine.match_stats", "utama_core.replay.columnar_reader", "utama_core.replay.columnar_writer", - "utama_core.replay.replay_player", "utama_core.replay.replay_writer", - "utama_core.replay.scenario", - "utama_core.scenario_bench.start", ], "subprocess": ["utama_core.rsoccer_simulator.src.Simulators.robosim.robosim_wrapper"], } def test_every_reach_outside_the_import_graph_is_accounted_for(graph): - everything = ( - graph.base_modules([ROUND_ROBIN_ENTRY]) | graph.base_modules(BENCH_ENTRIES) | graph.closure([KERNEL_STRATEGY]) - ) + everything = graph.base_modules([ROUND_ROBIN_ENTRY]) | graph.closure([KERNEL_STRATEGY]) assert graph.audit(everything) == AUDIT + + +_TACTIC = "utama_core/tactics/defend.py" +_TACTIC_NAME = "utama_core.tactics.defend" +_TACTIC_SRC = '"""Old module doc."""\n\nX = 1\n\n\ndef f(a, b):\n """Old doc."""\n return a + b\n' + + +def _file_digest(root: Path, text: str) -> str: + (root / _TACTIC).write_text(text) + (row,) = CodeGraph(root).file_digests([_TACTIC_NAME]) + return row + + +def test_comments_docstrings_and_formatting_do_not_change_a_file_fingerprint(repo): + before = _file_digest(repo, _TACTIC_SRC) + edited = ( + '"""New module doc, longer."""\n# a new comment\nX = 1 # trailing comment\n\n\n' + 'def f(a,\n b):\n """New doc\n spanning lines."""\n return (a + b)\n' + ) + assert _file_digest(repo, edited) == before + + +def test_changing_a_constant_changes_the_file_fingerprint(repo): + before = _file_digest(repo, _TACTIC_SRC) + assert _file_digest(repo, _TACTIC_SRC.replace("X = 1", "X = 2")) != before diff --git a/utama_core/tests/replay/test_replay_trace_tool.py b/utama_core/tests/replay/test_replay_trace_tool.py index 4a35d044..21be1401 100644 --- a/utama_core/tests/replay/test_replay_trace_tool.py +++ b/utama_core/tests/replay/test_replay_trace_tool.py @@ -4,6 +4,7 @@ from types import SimpleNamespace import numpy as np +import pytest from utama_core.entities.referee.referee_command import RefereeCommand as C @@ -53,3 +54,98 @@ def test_trace_prints_each_referee_change_with_its_reason_and_the_nearest_robots def test_trace_keeps_to_the_window(tmp_path): lines = replay_trace.trace(_replay(tmp_path), 0.6, 0.8, every=6) assert [line.split()[0] for line in lines] == ["0.60", "0.70", "0.80"] + + +def _frames(n=120): + """Synthetic arrays in the npz's layout: 60 Hz, ball at the origin and at rest, friendly 4 at + 0.3 m, friendly 5 at 3 m, enemy 2 at 3 m; no one has the ball, NORMAL_START throughout.""" + return { + "ts": 1.0 + np.arange(n) / 60.0, + "has_referee": np.ones(n, bool), + "referee_command": np.full(n, C.NORMAL_START.value, dtype=np.int8), + "ball_p": np.zeros((n, 3)), + "ball_v": np.zeros((n, 3)), + "friendly_p": np.tile([[0.3, 0.0], [-3.0, 0.0]], (n, 1, 1)), + "friendly_ids": np.array([4, 5]), + "friendly_has_ball": np.zeros((n, 2), bool), + "enemy_p": np.tile([[0.0, 3.0]], (n, 1, 1)), + "enemy_ids": np.array([2]), + "enemy_has_ball": np.zeros((n, 1), bool), + } + + +def _no_status(n): + return [None] * n + + +def test_a_kick_is_a_sharp_speed_rise_named_by_the_nearest_robot(): + z = _frames() + z["ball_v"][60:, 0] = 2.0 # at rest, then 2 m/s from tick 60 (t = 1.0 s from the first frame) + evs = replay_trace.events(z, _no_status(120), [], 0.0, 3.0) + + kicks = [e for e in evs if e["kind"] == "KICK"] + assert len(kicks) == 1 + assert kicks[0]["t"] == 1.0 + assert (kicks[0]["side"], kicks[0]["robot_id"]) == ("ours", 4) + assert abs(kicks[0]["dist"] - 0.3) < 1e-9 + + +def test_a_kick_is_counted_once_within_the_cooldown(): + z = _frames() + z["ball_v"][60, 0] = 0.6 # rise of 0.6 m/s in one tick + z["ball_v"][61:, 0] = 1.5 # rises again one tick later: the same kick + kicks = [e for e in replay_trace.events(z, _no_status(120), [], 0.0, 3.0) if e["kind"] == "KICK"] + assert len(kicks) == 1 + + +def test_a_slow_roll_is_not_a_kick(): + z = _frames() + z["ball_v"][:, 0] = np.linspace(0.0, 0.4, 120) # 0.4 m/s over 2 s: no tick rises 0.5 m/s + assert not [e for e in replay_trace.events(z, _no_status(120), [], 0.0, 3.0) if e["kind"] == "KICK"] + + +def test_possession_and_referee_changes_are_events_with_the_status_reason(): + z = _frames() + z["friendly_has_ball"][30:, 0] = True # F from t = 1.5 + z["referee_command"][40:] = C.STOP.value # STOP from t = 1.67 + statuses = [None] * 40 + ["Ball out of bounds"] * 80 + evs = replay_trace.events(z, statuses, [], 0.0, 3.0) + + hold = [e for e in evs if e["kind"] == "HOLD"] + assert [(e["from"], e["to"]) for e in hold] == [("-", "F")] + ref = [e for e in evs if e["kind"] == "REF"] + assert [(e["command"], e["status"]) for e in ref] == [("STOP", "Ball out of bounds")] + assert ref[0]["text"].endswith("| Ball out of bounds") + + +def test_fouls_are_events_by_side_and_only_inside_the_window(): + z = _frames() + fouls = [ + {"rule": "keeps_clear", "sim_time": 1.5, "side": "friendly", "robot_id": 4, "tactic": "T"}, + {"rule": "out_of_bounds", "sim_time": 2.5, "side": "enemy", "robot_id": 2, "tactic": None}, + {"rule": "keeps_clear", "sim_time": 9.0, "side": "friendly", "robot_id": 5, "tactic": None}, + ] + foul_evs = [e for e in replay_trace.events(z, _no_status(120), fouls, 1.0, 3.0) if e["kind"] == "FOUL"] + assert [(e["side"], e["rule"], e["robot_id"]) for e in foul_evs] == [ + ("ours", "keeps_clear", 4), + ("theirs", "out_of_bounds", 2), + ] + + +def test_event_lines_are_capped_with_a_count_of_the_rest(tmp_path): + import json + + path = _replay(tmp_path) + fouls = [{"rule": "keeps_clear", "sim_time": i / 100, "side": "friendly", "robot_id": 4} for i in range(45)] + path.with_name("a_vs_b.stats.json").write_text(json.dumps({"fouls": fouls})) + lines = replay_trace.event_lines(path, 0.0, 1.0) + assert len(lines) == replay_trace.MAX_EVENT_LINES + 1 + assert lines[-1] == "... 6 more" # 45 fouls and the 0.5 s referee change, 40 shown + + +def test_rows_carry_the_nearest_robot_ids_and_distances_as_data(tmp_path): + z, statuses, _ = replay_trace._load(_replay(tmp_path)) + first = replay_trace.rows(z, statuses, 0.0, 0.0)[0] + assert first["referee"] == "NORMAL_START" + assert first["near_F"] == {"id": 4, "dist": pytest.approx(0.3, abs=1e-6)} + assert first["near_E"] == {"id": 2, "dist": pytest.approx(2.24, abs=0.01)} diff --git a/utama_core/tests/replay/test_repro_summary.py b/utama_core/tests/replay/test_repro_summary.py new file mode 100644 index 00000000..08d4d69e --- /dev/null +++ b/utama_core/tests/replay/test_repro_summary.py @@ -0,0 +1,118 @@ +"""`tools/repro_from_replay.py --summary`: per-robot rows from match_log events, and their text.""" + +import importlib.util +from pathlib import Path +from types import SimpleNamespace + +import pytest + +_TOOL = Path(__file__).resolve().parents[3] / "tools" / "repro_from_replay.py" +_spec = importlib.util.spec_from_file_location("repro_from_replay", _TOOL) +repro = importlib.util.module_from_spec(_spec) +_spec.loader.exec_module(repro) + + +def _sample(t, robots, ball=(0.0, 0.0), ref="NORMAL_START"): + return {"t": t, "referee": ref, "ball": ball, "robots": robots} + + +def _intention(t, slot, robots, note=None): + return SimpleNamespace(sim_time=t, tactic_id=slot, robot_ids=tuple(robots), note=note) + + +def _trace(t, key, value): + return SimpleNamespace(sim_time=t, key=key, value=value) + + +def test_rows_are_every_quarter_second_with_tactic_target_and_distance(): + samples = [ + _sample(0.0, {1: (3.0, 4.0), 3: (0.3, 0.0)}), + _sample(0.1, {1: (3.0, 4.0), 3: (0.3, 0.0)}), # skipped: under 0.25 s after the last row + _sample(0.25, {1: (3.0, 4.0), 3: (0.3, 0.0)}, ref="STOP"), + _sample(0.5, {1: (3.0, 4.0), 3: (0.3, 0.0)}), + ] + events = [ + _intention(0.2, "overload", [1], note="DecoyOverloadTactic"), + _intention(0.4, "overload", [], note="released (was committed=False, held 0 committed ticks)"), + _trace(0.1, "give_and_go.pass_target", {"receiver_id": 3, "x": 1.0, "y": 2.0}), + _trace(0.1, "decoy_and_overload.lure_target", {"x": -4.0, "y": 2.5}), # no robot id: not attributed + ] + rows = repro.summary_rows(samples, [1, 3], events) + + assert [(r["t"], r["robot"]) for r in rows] == [(0.0, 1), (0.0, 3), (0.25, 1), (0.25, 3), (0.5, 1), (0.5, 3)] + by = {(r["t"], r["robot"]): r for r in rows} + assert by[(0.0, 1)]["tactic"] == "-" + assert by[(0.25, 1)]["tactic"] == "DecoyOverloadTactic" + assert by[(0.5, 1)]["tactic"] == "-" # released at 0.4 + assert by[(0.25, 3)]["target"] == "give_and_go.pass_target (1.00,2.00)" + assert by[(0.25, 1)]["target"] == "-" + assert by[(0.25, 1)]["dist_ball"] == pytest.approx(5.0) + assert by[(0.25, 1)]["ref"] == "STOP" and by[(0.5, 1)]["ref"] == "NORMAL_START" + + +def test_a_slot_reassigned_to_fewer_robots_drops_the_ones_it_lost(): + samples = [_sample(0.0, {3: (0.0, 0.0), 4: (1.0, 0.0)}), _sample(0.5, {3: (0.0, 0.0), 4: (1.0, 0.0)})] + events = [ + _intention(0.0, "attack", [3, 4], note="GiveAndGoTactic"), + _intention(0.3, "attack", [4]), # robot 3 moved to another slot in the same tick, not logged here + ] + rows = repro.summary_rows(samples, [3, 4], events) + late = {r["robot"]: r["tactic"] for r in rows if r["t"] == 0.5} + assert late == {3: "-", 4: "GiveAndGoTactic"} + + +def test_ball_missing_gives_no_distance(): + rows = repro.summary_rows([_sample(0.0, {1: (1.0, 1.0)}, ball=None)], [1], []) + assert rows[0]["dist_ball"] is None + assert "ball - m" in repro.format_summary(rows)[0] + + +def test_format_caps_the_lines_and_counts_the_rest(): + rows = [ + {"t": i / 4, "robot": 1, "tactic": "-", "pos": (0.0, 0.0), "dist_ball": 1.0, "ref": "STOP", "target": "-"} + for i in range(45) + ] + lines = repro.format_summary(rows) + assert len(lines) == repro.SUMMARY_MAX_LINES + 1 + assert lines[-1] == "... 5 more" + assert "robot 1" in lines[0] and "ref STOP" in lines[0] + + +def test_resolve_sim_noise_uses_the_replays_own_settings_by_default(): + from utama_core.rsoccer_simulator.src.Utils.sim_noise import SimNoise + + settings = SimNoise(seed=2, latency_s=(0.03, 0.15), kick_speed_sd=0.45).to_dict() + noise, line = repro.resolve_sim_noise(None, {"settings": settings, "draw": {}}) + assert noise == SimNoise.from_dict(settings) and noise.seed == 2 + assert line == "sim noise: the replay's own (seed 2)" + + +def test_resolve_sim_noise_without_one_in_the_replay_is_noiseless(): + assert repro.resolve_sim_noise(None, None) == (None, "sim noise: none (the replay stores none)") + + +def test_resolve_sim_noise_none_overrides_the_replays_noise(): + replay = {"settings": {"seed": 2, "latency_s": [0.03, 0.15]}, "draw": {}} + assert repro.resolve_sim_noise("none", replay) == (None, "sim noise: none (--sim-noise none)") + + +def test_resolve_sim_noise_reads_a_settings_file_over_the_replays(tmp_path): + path = tmp_path / "other.json" + path.write_text('{"seed": 7, "kick_speed_sd": 0.3}') + replay = {"settings": {"seed": 2}, "draw": {}} + noise, line = repro.resolve_sim_noise(str(path), replay) + assert noise.seed == 7 and noise.kick_speed_sd == 0.3 + assert line == f"sim noise: {path} (--sim-noise, seed 7)" + + +def test_the_replays_stored_settings_redraw_the_same_robots(): + """The repro's draw is `SimNoise.draw` on the stored settings, so with the same robot counts and + time step it is the match's own draw: latency ticks and kick offsets per robot, exactly.""" + from utama_core.config.settings import TIMESTEP + from utama_core.rsoccer_simulator.src.Utils.sim_noise import SimNoise + + noise = SimNoise(seed=2, latency_s=(0.03, 0.15), kick_speed_offset_sd=0.2, max_accel=(1.5, 3.5)) + counts = {"team_blue": 6, "team_yellow": 6} + stored = noise.draw(counts, TIMESTEP).to_dict() # what StrategyRunner stores with the replay + redrawn = repro.resolve_sim_noise(None, {"settings": noise.to_dict(), "draw": stored})[0].draw(counts, TIMESTEP) + assert redrawn.to_dict() == stored diff --git a/utama_core/tests/replay/test_scenario.py b/utama_core/tests/replay/test_scenario.py index 5b905ad3..e74a194c 100644 --- a/utama_core/tests/replay/test_scenario.py +++ b/utama_core/tests/replay/test_scenario.py @@ -142,8 +142,8 @@ def test_a_start_begins_at_its_own_speeds_not_the_jump_from_the_runner_s_formati # Velocity is a finite difference over the runner's frame history. Left holding the # formation the runner was built in, it read the teleport as a jump from there (the ball # at 30 m/s here, robots far faster): the filtered ball then overshot into goals and out - # of play, and the referee charged ball-speed and crashing fouls no robot made. A bench - # runner applies its start right after construction, as here (`scenario_scorer`). + # of play, and the referee charged ball-speed and crashing fouls no robot made. A runner + # built for a replay applies its start right after construction, as here. runner = _build_runner_and_scenario(_SCENARIO) try: apply_scenario(runner, _SCENARIO, verify=True) @@ -318,7 +318,7 @@ def _assert_close_to_scenario(positions: dict, scenario, *, tol: float, context: # --- the referee state a start is played in ---------------------------------------------- # -# `scenario_scorer._build_runner` seeds every bench runner with PREPARE_KICKOFF_YELLOW. A start +# `_build_runner` seeds every runner with PREPARE_KICKOFF_YELLOW. A start # must be played in its own restart (or live play), from the first tick, not in that kickoff. @@ -366,8 +366,8 @@ def robots(states, friendly): ) -def _bench_runner(frame): - """What `apply_scenario` touches of a bench runner, with the referee `_build_runner` gives it.""" +def _replay_runner(frame): + """What `apply_scenario` touches of a runner, with the referee `_build_runner` gives it.""" from types import SimpleNamespace from utama_core.custom_referee import CustomReferee @@ -413,11 +413,11 @@ def _penalty_start(command: RefereeCommand) -> Scenario: ], ) def test_a_start_is_played_in_its_own_referee_command_from_the_first_tick(command): - """A restart start kept the bench runner's PREPARE_KICKOFF_YELLOW (NORMAL_START was skipped) + """A restart start kept the runner's PREPARE_KICKOFF_YELLOW (NORMAL_START was skipped) or got a STOP inserted before it, which held it while the kicker stood at the ball.""" scenario = _penalty_start(command) frame = _referee_frame(scenario) - runner = _bench_runner(frame) + runner = _replay_runner(frame) apply_scenario(runner, scenario, verify=False) @@ -429,7 +429,7 @@ def test_a_penalty_start_goes_live_as_a_penalty_after_the_referee_s_own_preparat referee's prepare time plus its readiness delay, both counted from the start.""" scenario = _penalty_start(RefereeCommand.PREPARE_PENALTY_BLUE) frame = _referee_frame(scenario) - runner = _bench_runner(frame) + runner = _replay_runner(frame) apply_scenario(runner, scenario, verify=False) seen = [] diff --git a/utama_core/tests/replay/test_ssl_log_extras.py b/utama_core/tests/replay/test_ssl_log_extras.py new file mode 100644 index 00000000..284313ba --- /dev/null +++ b/utama_core/tests/replay/test_ssl_log_extras.py @@ -0,0 +1,290 @@ +"""Tests for the official-log converter's sidecars (`replay/ssl_log.py`): the tracker's kicks, +visibility and angular velocity, raw camera detections, and adding them to a replay converted +before they existed (`tools/convert_ssl_log.py --backfill-extras`).""" + +from __future__ import annotations + +import gzip +import json +import struct + +import numpy as np +import pytest + +from tools import convert_ssl_log +from utama_core.entities.referee.referee_command import RefereeCommand +from utama_core.entities.referee.stage import Stage +from utama_core.replay.ssl_log import ( + LOG_HEADER, + REFEREE, + TRACKER, + VISION_2014, + _add_kick, + _kick_rows, + _VisionRows, + convert_log, +) +from utama_core.team_controller.src.generated_code.ssl_gc_referee_message_pb2 import ( + Referee, +) +from utama_core.team_controller.src.generated_code.ssl_vision_detection_tracked_pb2 import ( + KickedBall, +) +from utama_core.team_controller.src.generated_code.ssl_vision_wrapper_pb2 import ( + SSL_WrapperPacket, +) +from utama_core.team_controller.src.generated_code.ssl_vision_wrapper_tracked_pb2 import ( + TrackerWrapperPacket, +) + +S = 1_000_000_000 # ns +T0 = 1_700_000_000 * S +TRACKER_LAG_S = 0.02 # tracked frames arrive this long after their tracker timestamp + + +def _write_log(path, records) -> None: + body = b"".join(struct.pack(">qii", t, kind, len(payload)) + payload for t, kind, payload in records) + path.write_bytes(gzip.compress(LOG_HEADER + struct.pack(">i", 1) + body)) + + +def _referee(ts_ns, stage, command, counter) -> tuple: + msg = Referee( + packet_timestamp=ts_ns // 1000, + stage=stage.value, + command=command.value, + command_counter=counter, + command_timestamp=ts_ns // 1000, + blue_team_on_positive_half=True, + ) + for team, name in ((msg.yellow, "Yellows"), (msg.blue, "Blues")): + team.name, team.score, team.goalkeeper = name, 0, 0 + team.red_cards = team.yellow_cards = team.timeouts = team.timeout_time = 0 + return ts_ns, REFEREE, msg.SerializeToString() + + +def _tracked(ts_ns, visibility, kick=None, source="TIGERs") -> tuple: + """Yellow 7 and blue 12 on the field; `kick`: (start s after T0, team, id, speed); + `visibility` None sends none.""" + packet = TrackerWrapperPacket(uuid=source, source_name=source) + frame = packet.tracked_frame + frame.frame_number, frame.timestamp = 0, ts_ns / S - TRACKER_LAG_S + shown = {} if visibility is None else {"visibility": visibility} + frame.balls.add(pos=dict(x=0.0, y=0.0, z=0.0), **shown) + frame.robots.add(robot_id=dict(id=7, team=1), pos=dict(x=1.0, y=0.5), orientation=0.0, vel_angular=2.0) + shown = {} if visibility is None else {"visibility": visibility / 2} + frame.robots.add(robot_id=dict(id=12, team=2), pos=dict(x=-1.0, y=0.5), orientation=0.0, **shown) + if kick is not None: + start_s, team, rid, speed = kick + frame.kicked_ball.pos.x, frame.kicked_ball.pos.y = 1.0, 0.5 + frame.kicked_ball.vel.x, frame.kicked_ball.vel.y, frame.kicked_ball.vel.z = speed, 0.0, 0.0 + frame.kicked_ball.start_timestamp = T0 / S + start_s + frame.kicked_ball.robot_id.id, frame.kicked_ball.robot_id.team = rid, team + return ts_ns, TRACKER, packet.SerializeToString() + + +def _camera(ts_ns, camera, ball_mm, robot_mm) -> tuple: + packet = SSL_WrapperPacket() + d = packet.detection + d.frame_number, d.t_capture, d.t_sent, d.camera_id = 5, ts_ns / S - 0.01, ts_ns / S, camera + d.balls.add(confidence=0.9, x=ball_mm[0], y=ball_mm[1], z=0.0, pixel_x=10.0, pixel_y=20.0, area=12) + d.robots_yellow.add(confidence=0.8, robot_id=7, x=robot_mm[0], y=robot_mm[1], orientation=0.3, pixel_x=1, pixel_y=2) + return ts_ns, VISION_2014, packet.SerializeToString() + + +def _kick_of(t_s) -> tuple | None: + """The kick a tracked frame at `t_s` reports: yellow 7 kicks at 0.5 s; the tracker gives it to + blue 12 from 0.6 s and refines the speed until 0.9 s; yellow 7 kicks again at 1.2 s.""" + if 0.5 <= t_s < 1.0: + return (0.5 + (0.01 if t_s >= 0.6 else 0.0), 1 if t_s < 0.6 else 2, 7 if t_s < 0.6 else 12, min(t_s, 0.9) * 5) + if 1.2 <= t_s < 1.4: + return (1.2, 1, 7, 3.0) + return None + + +@pytest.fixture +def log(tmp_path): + """2 s of first half, then the post-game stage; tracked frames at 100 Hz (faster than the + replay's 60 Hz ticks), ball visibility rising from 0 to 1, and a camera frame every 0.5 s from + before the first referee message to after the last.""" + half = Stage.NORMAL_FIRST_HALF + records = [ + _referee(T0, half, RefereeCommand.NORMAL_START, 1), + _referee(T0 + 2 * S, Stage.POST_GAME, RefereeCommand.HALT, 2), + ] + for k in range(0, 201): + t_s = k / 100 + records.append(_tracked(T0 + k * S // 100, visibility=t_s / 2, kick=_kick_of(t_s))) + for k in range(-1, 6): + records.append(_camera(T0 + k * S // 2, camera=k % 2, ball_mm=(1000.0 * k, -500.0), robot_mm=(250.0, 750.0))) + records.sort(key=lambda r: r[0]) + path = tmp_path / "2026-07-01_Yellows-vs-Blues.log.gz" + _write_log(path, records) + return path + + +def test_a_kick_the_tracker_repeats_and_reassigns_is_one_row_with_its_last_kicker_and_estimate(log, tmp_path): + summary = convert_log(log, tmp_path / "out") + kicks = json.loads((tmp_path / "out" / f"{log.name[:-7]}.kicks.json").read_text()) + + assert summary["extras"] == { + "kicks": 2, + "kicks_source": "TIGERs", + "visibility_source": "TIGERs", + "tracker": True, + "raw_vision": False, + } + first, second = kicks + # start on the replay clock: the first report's start time plus its receive-minus-tracker lag + assert first["ts"] == pytest.approx(0.5 + TRACKER_LAG_S, abs=1e-3) + assert (first["team"], first["robot_id"], first["kickers"]) == ("blue", 12, 2) + assert first["vel_first"][0] == pytest.approx(2.5) and first["speed"] == pytest.approx(4.5) + # one report per tick (60 Hz) whose newest tracked frame (100 Hz) is new: ticks 30-59 + assert first["reports"] == 30 + assert (second["team"], second["robot_id"], second["kickers"], second["reports"]) == ("yellow", 7, 1, 12) + + +def test_kicks_and_visibility_come_from_another_source_when_the_replays_tracker_sends_none(tmp_path): + # as 2026: the busiest tracker (ER-FORCE, the replay's) sends neither; TIGERs sends both + half = Stage.NORMAL_FIRST_HALF + records = [ + _referee(T0, half, RefereeCommand.NORMAL_START, 1), + _referee(T0 + 2 * S, Stage.POST_GAME, RefereeCommand.HALT, 2), + ] + for k in range(0, 201): + records.append(_tracked(T0 + k * S // 100, visibility=None, source="ER-FORCE")) + if k % 2 == 0: + records.append(_tracked(T0 + k * S // 100, visibility=0.75, kick=_kick_of(k / 100))) + records.sort(key=lambda r: r[0]) + path = tmp_path / "two-trackers.log.gz" + _write_log(path, records) + summary = convert_log(path, tmp_path / "out") + extra = np.load(tmp_path / "out" / "two-trackers.tracker.zip") + + assert summary["world_source"] == "ER-FORCE" + assert (summary["extras"]["kicks_source"], summary["extras"]["visibility_source"]) == ("TIGERs", "TIGERs") + assert summary["extras"]["kicks"] == 2 + assert extra["ball_visibility"][30] == pytest.approx(0.75) + assert extra["robot_vel_angular"][30, 0, 7] == pytest.approx(2.0) # from ER-FORCE's own frames + + +def test_kick_starts_more_than_the_same_kick_window_apart_are_two_kicks(): + kicks: list[dict] = [] + for start in (10.0, 10.04, 10.1): + kick = KickedBall(start_timestamp=start) + kick.pos.x = kick.pos.y = 0.0 + kick.vel.x = kick.vel.y = kick.vel.z = 0.0 + kick.robot_id.id, kick.robot_id.team = 3, 1 + _add_kick(kicks, kick, recv_ns=int((start + 0.5) * S), frame_timestamp=start + 0.5) + assert [k["reports"] for k in kicks] == [2, 1] + assert kicks[0]["start_ns"] == int(10.0 * S) + + +def test_a_kick_after_a_colour_swap_is_given_in_kick_off_colours_and_one_outside_the_window_dropped(): + tick_ns = np.arange(4) * S + swapped = np.array([False, False, True, True]) + kicks = [ + { + "start_ns": t * S, + "kicker": (0, 1), + "kickers": {(0, 1)}, + "pos": [0, 0], + "vel_first": [1, 0, 0], + "vel": [1, 0, 0], + "reports": 1, + } + for t in (1, 2, 5) + ] + rows = _kick_rows(kicks, start_ns=0, end_ns=4 * S, tick_ns=tick_ns, swapped=swapped) + assert [(r["ts"], r["team"]) for r in rows] == [(1.0, "yellow"), (2.0, "blue")] + + +def test_visibility_and_angular_velocity_land_on_the_replays_ticks(log, tmp_path): + convert_log(log, tmp_path / "out") + extra = np.load(tmp_path / "out" / f"{log.name[:-7]}.tracker.zip") + + ticks = len(extra["ts"]) + assert ticks == 120 and extra["robot_visibility"].shape == (ticks, 2, 16) + # tick 30 (0.5 s) takes the newest tracked frame at or before it: the one at 0.5 s + assert extra["ball_visibility"][30] == pytest.approx(0.25) + assert extra["robot_visibility"][30, 1, 12] == pytest.approx(0.125) + assert np.isnan(extra["robot_visibility"][30, 0, 7]) # yellow 7's visibility is not given + assert extra["robot_vel_angular"][30, 0, 7] == pytest.approx(2.0) + assert np.isnan(extra["robot_visibility"][30, 0, 3]) # no such robot + + +def test_raw_camera_detections_inside_the_window_round_trip_in_metres(log, tmp_path): + summary = convert_log(log, tmp_path / "out", raw_vision=True) + vision = np.load(tmp_path / "out" / f"{log.name[:-7]}.vision.zip") + + assert summary["extras"]["raw_vision"] + # camera frames at -0.5 s and 2.0 s and after are outside the window [0, 2) s + np.testing.assert_allclose(vision["frame_ts"], [0.0, 0.5, 1.0, 1.5]) + np.testing.assert_array_equal(vision["frame_camera"], [0, 1, 0, 1]) + np.testing.assert_allclose(vision["frame_t_sent"] - vision["frame_t_capture"], 0.01, atol=1e-6) + np.testing.assert_array_equal(vision["ball_frame"], [0, 1, 2, 3]) + np.testing.assert_allclose(vision["ball_x"], [0.0, 1.0, 2.0, 3.0]) + np.testing.assert_allclose(vision["ball_y"], -0.5) + np.testing.assert_array_equal(vision["ball_area"], 12) + np.testing.assert_allclose(vision["robot_x"], 0.25) + np.testing.assert_array_equal(vision["robot_id"], 7) + np.testing.assert_array_equal(vision["robot_team"], 0) + assert np.isnan(vision["robot_height"]).all() # not sent + + +def test_raw_detections_after_a_colour_swap_go_back_to_kick_off_colours(): + rows = _VisionRows() + for k in range(3): + packet = SSL_WrapperPacket() + packet.detection.frame_number, packet.detection.camera_id = k, 0 + packet.detection.t_capture = packet.detection.t_sent = float(k) + packet.detection.robots_blue.add(confidence=1.0, robot_id=2, x=0.0, y=0.0, pixel_x=0, pixel_y=0) + rows.add(k * S, packet.detection) + out = rows.arrays(0, 3 * S, np.arange(3) * S, swapped=np.array([False, True, True])) + np.testing.assert_array_equal(out["robot_team"], [1, 0, 0]) + + +def test_backfill_adds_the_sidecars_without_touching_the_replay(log, tmp_path, monkeypatch): + out = tmp_path / "out" + convert_log(log, out) + stem = log.name[:-7] + replay_bytes = (out / f"{stem}.npz").read_bytes() + for suffix in (".kicks.json", ".tracker.zip"): + (out / f"{stem}{suffix}").unlink() + source = json.loads((out / f"{stem}.source.json").read_text()) + del source["extras"] # as converted before the sidecars existed + (out / f"{stem}.source.json").write_text(json.dumps(source)) + assert not convert_ssl_log.has_extras(out, stem, raw_vision=False) + + monkeypatch.setattr( + convert_ssl_log, "_download", lambda url, path, _expected_bytes=0: path.write_bytes(log.read_bytes()) + ) + job = { + "name": log.name, + "year": 2026, + "bytes": 0, + "url": "", + "out": str(tmp_path), + "raw_dir": str(tmp_path / "raw"), + } + (tmp_path / "raw").mkdir() + (tmp_path / "2026").symlink_to(out) + skipped = convert_ssl_log._corpus_job({**job, "raw_vision": True, "backfill_extras": False}) + row = convert_ssl_log._corpus_job({**job, "raw_vision": True, "backfill_extras": True}) + again = convert_ssl_log._corpus_job({**job, "raw_vision": True, "backfill_extras": True}) + + assert (skipped["status"], row["status"], again["status"]) == ("exists", "extras added", "exists") + assert (out / f"{stem}.npz").read_bytes() == replay_bytes + assert (out / f"{stem}.kicks.json").exists() and (out / f"{stem}.vision.zip").exists() + assert json.loads((out / f"{stem}.source.json").read_text())["extras"]["raw_vision"] + assert not (tmp_path / "raw" / log.name).exists() # the download is deleted + + +def test_backfill_refuses_a_replay_of_another_length(log, tmp_path): + out = tmp_path / "out" + convert_log(log, out) + source_path = out / f"{log.name[:-7]}.source.json" + source = json.loads(source_path.read_text()) + source["duration_s"] += 1.0 + source_path.write_text(json.dumps(source)) + with pytest.raises(ValueError, match="gives"): + convert_log(log, out, write_replay=False) diff --git a/utama_core/tests/rsoccer_simulator/test_dribble_with_friction.py b/utama_core/tests/rsoccer_simulator/test_dribble_with_friction.py new file mode 100644 index 00000000..b3f4d64f --- /dev/null +++ b/utama_core/tests/rsoccer_simulator/test_dribble_with_friction.py @@ -0,0 +1,94 @@ +"""A dribbled ball stays at the kicker with and without two-phase ball friction. + +rsim holds a dribbled ball with a fixed ODE joint (`holdBall` in vendor/rSim sslrobot.cpp), while +the two-phase friction (`applyBallFriction` in sslworld.cpp) rewrites the ball's velocity and spin +every substep. These tests check that the friction does not break the hold, and that a released +ball still slows to rest on its own. + +The friction-on case needs a robosim binary built from vendor/rSim (one with `set_physics_params`); +with the stock binary it skips. +""" + +import math + +import numpy as np +import pytest + +from utama_core.rsoccer_simulator.src.ssl.envs import SSLStandardEnv +from utama_core.rsoccer_simulator.src.Utils.sim_noise import SimNoise + +TICK = 1 / 60 +FRONT = 0.1 # the ball's centre, this far in front of the robot's centre, is in the kicker +HOLD_TOLERANCE = 0.15 # m, ball to the robot's front while dribbling +GRAB_TICKS = 20 +DRIBBLE_TICKS = 140 # about 2.3 s of driving: forward, then turning while moving +REST_TICKS = 900 # 15 s of game time for the released ball to come to rest + +FRICTION_OFF = SimNoise() +FRICTION_ON = SimNoise(ball_roll_decel=(0.3, 0.3), ball_slide_decel=(3.0, 3.0)) + + +def _env(sim_noise) -> SSLStandardEnv: + try: + env = SSLStandardEnv(n_robots_blue=1, n_robots_yellow=1, render_mode=None, sim_noise=sim_noise) + except RuntimeError as e: + if "set_physics_params" in str(e): + pytest.skip("needs a robosim binary built from vendor/rSim") + raise + env.reset() + env.teleport_robot(False, 0, -1.0, 0.0, 0.0) + env.teleport_ball(-1.0 + FRONT, 0.0) + return env + + +def _step(env, blue=(0, 0, 0, 0, 0, 0)) -> None: + env.step({"team_blue": (np.array(blue, dtype=float),), "team_yellow": (np.zeros(6),)}) + + +def _front_gap(env) -> float: + """Distance from the ball to the point FRONT metres ahead of the robot's centre. + + Both are read from the frame, which rsim keeps in its own coordinates, so the heading and the + positions are in the same frame. + """ + r = env.frame.robots_blue[0] + th = math.radians(r.theta) + fx, fy = r.x + FRONT * math.cos(th), r.y + FRONT * math.sin(th) + return math.hypot(env.frame.ball.x - fx, env.frame.ball.y - fy) + + +def _ball_speed(env) -> float: + return math.hypot(env.frame.ball.v_x, env.frame.ball.v_y) + + +@pytest.mark.parametrize("sim_noise", [FRICTION_OFF, FRICTION_ON], ids=["friction_off", "friction_on"]) +def test_a_dribbled_ball_stays_at_the_kicker_and_comes_to_rest_after_release(sim_noise): + env = _env(sim_noise) + try: + # Grab: dribbler on, robot still. + for _ in range(GRAB_TICKS): + _step(env, (0, 0, 0, 0, 1, 0)) + assert _front_gap(env) < HOLD_TOLERANCE, "the ball should start at the kicker" + + # Dribble: forward for about a second, then turn while still moving forward. + gaps = [] + for t in range(DRIBBLE_TICKS): + if t < 60: + cmd = (1.0, 0.0, 0.0, 0, 1, 0) + else: + cmd = (0.5, 0.0, 1.5, 0, 1, 0) + _step(env, cmd) + gaps.append(_front_gap(env)) + worst = max(gaps) + assert worst < HOLD_TOLERANCE, f"the ball got {worst:.3f} m from the kicker while dribbling" + + # Release: dribbler off and robot stopped; the ball must roll to rest by itself. + speeds = [] + for _ in range(REST_TICKS): + _step(env, (0, 0, 0, 0, 0, 0)) + speeds.append(_ball_speed(env)) + if len(speeds) > 60 and speeds[-1] < 0.02: + break + assert speeds[-1] < 0.02, f"released ball still moving at {speeds[-1]:.3f} m/s after {len(speeds)} ticks" + finally: + env.close() diff --git a/utama_core/tests/rsoccer_simulator/test_sim_noise.py b/utama_core/tests/rsoccer_simulator/test_sim_noise.py new file mode 100644 index 00000000..d7c765f2 --- /dev/null +++ b/utama_core/tests/rsoccer_simulator/test_sim_noise.py @@ -0,0 +1,167 @@ +"""`SimNoise`: seeded latency, kick speed noise and vision noise in rsim.""" + +import numpy as np +import pytest + +from utama_core.rsoccer_simulator.src.ssl.envs import SSLStandardEnv +from utama_core.rsoccer_simulator.src.Utils.gaussian_noise import RsimGaussianNoise +from utama_core.rsoccer_simulator.src.Utils.sim_noise import ( + CommandDelay, + KickSpeedNoise, + SimNoise, +) + +N = {"team_blue": 2, "team_yellow": 2} +TICK = 1 / 60 + + +def _action(tick: int) -> dict: + """Commands whose first field is the tick they were issued on, so a delay is readable.""" + return {c: tuple(np.array([tick + 1.0, 0, 0, 0, 0, 0]) for _ in range(2)) for c in ("team_blue", "team_yellow")} + + +# --- latency ------------------------------------------------------------------------- + + +def test_a_robot_acts_on_its_command_from_exactly_its_latency_ago(): + delay = CommandDelay({"team_blue": (3, 0), "team_yellow": (0, 1)}) + out = [delay.push(_action(t)) for t in range(6)] + # blue 0, 3 ticks: zeros (stand still) for ticks 0-2, then tick t-3's command + assert [o["team_blue"][0][0] for o in out] == [0, 0, 0, 1, 2, 3] + assert [o["team_blue"][1][0] for o in out] == [1, 2, 3, 4, 5, 6] + assert [o["team_yellow"][1][0] for o in out] == [0, 1, 2, 3, 4, 5] + + +def test_reset_drops_the_queued_commands(): + delay = CommandDelay({"team_blue": (1, 1), "team_yellow": (1, 1)}) + delay.push(_action(0)) + delay.reset() + assert delay.push(_action(1))["team_blue"][0][0] == 0 + + +# --- kick speed ---------------------------------------------------------------------- + + +def _kick_noise(offset: float, sd: float, seed: int = 0) -> KickSpeedNoise: + cfg = SimNoise(seed=seed) + return KickSpeedNoise({"team_blue": (offset, 0.0), "team_yellow": (0.0, 0.0)}, sd, cfg.kick_rngs(N)) + + +def test_a_held_kick_command_keeps_one_speed_and_the_next_kick_draws_again(): + noise = _kick_noise(0.0, 0.5) + first = [noise.speed("team_blue", 0, True, 5.0) for _ in range(4)] + assert len(set(first)) == 1 + assert noise.speed("team_blue", 0, False, 5.0) == 0.0 + assert noise.speed("team_blue", 0, True, 5.0) != first[0] + + +def test_kick_speeds_centre_on_nominal_plus_the_robots_offset_with_the_given_spread(): + noise = _kick_noise(-0.4, 0.3) + speeds = [] + for _ in range(4000): + speeds.append(noise.speed("team_blue", 0, True, 5.0)) + noise.speed("team_blue", 0, False, 5.0) + assert np.mean(speeds) == pytest.approx(4.6, abs=0.02) + assert np.std(speeds) == pytest.approx(0.3, abs=0.02) + + +def test_kick_speed_never_drops_below_the_floor(): + noise = _kick_noise(-10.0, 0.0) + assert noise.speed("team_blue", 0, True, 5.0) == 0.5 + + +# --- the per-match draw ---------------------------------------------------------------- + + +def test_same_seed_same_draw_and_another_seed_another(): + cfg = SimNoise(seed=7, latency_s=(0.0, 0.2), kick_speed_offset_sd=0.5) + assert cfg.draw(N, TICK) == SimNoise.from_dict(cfg.to_dict()).draw(N, TICK) + assert cfg.draw(N, TICK) != SimNoise(**{**cfg.__dict__, "seed": 8}).draw(N, TICK) + + +def test_latency_is_rounded_to_whole_ticks(): + draw = SimNoise(latency_s=(0.05, 0.05)).draw(N, TICK) + assert draw.latency_ticks == {"team_blue": (3, 3), "team_yellow": (3, 3)} + + +def test_defaults_draw_nothing(): + draw = SimNoise().draw(N, TICK) + assert all(t == 0 for ticks in draw.latency_ticks.values() for t in ticks) + assert all(o == 0 for offs in draw.kick_offset.values() for o in offs) + + +# --- in the env (rsim) -------------------------------------------------------------------- + + +def _env(sim_noise=None, **kwargs) -> SSLStandardEnv: + env = SSLStandardEnv(n_robots_blue=2, n_robots_yellow=2, render_mode=None, sim_noise=sim_noise, **kwargs) + env.reset() + return env + + +def _observed(env) -> list: + vision = env._frame_to_observations()[0] + return [(b.x, b.y) for b in vision.balls] + [ + (r.x, r.y, r.orientation) for r in vision.blue_robots + vision.yellow_robots + ] + + +def _step_forward(env) -> None: + cmd = tuple(np.array([1.0, 0, 0, 0, 0, 0]) for _ in range(2)) + env.step({"team_blue": cmd, "team_yellow": cmd}) + + +@pytest.mark.parametrize("noise_kwargs", [{"gaussian_noise": RsimGaussianNoise(0.01, 0.01, 1)}, {}]) +def test_vision_noise_leaves_the_sims_own_frame_alone(noise_kwargs): + """The noise used to be added to `env.frame` in place, and teleports write that frame back + into the physics, so every ball placement moved every robot by its noise.""" + sim_noise = None if noise_kwargs else SimNoise(seed=1, vision=RsimGaussianNoise(0.01, 0.01, 1)) + env = _env(sim_noise, **noise_kwargs) + try: + ball, robot = env.frame.ball, env.frame.robots_blue[0] + before = (ball.x, ball.y, robot.x, robot.y, robot.theta) + env.latest_observation = (-1, None) + observed = _observed(env) + assert (ball.x, ball.y, robot.x, robot.y, robot.theta) == before + assert observed[0] != (ball.x, -ball.y) + finally: + env.close() + + +def test_seeded_vision_noise_and_dropouts_repeat_with_the_seed(): + cfg = SimNoise(seed=3, vision=RsimGaussianNoise(0.01, 0.01, 1), vanishing=0.2) + runs = [] + for seed in (3, 3, 4): + env = _env(SimNoise(**{**cfg.__dict__, "seed": seed})) + try: + obs = [] + for _ in range(20): + _step_forward(env) + obs.append(_observed(env)) + runs.append(obs) + finally: + env.close() + assert runs[0] == runs[1] + assert runs[0] != runs[2] + + +def test_a_delayed_robot_gets_its_first_command_latency_ticks_late(): + env = _env(SimNoise(latency_s=(3 * TICK, 3 * TICK))) + try: + sent = [] + for _ in range(5): + _step_forward(env) + sent.append(env.sent_commands[0].v_x) + assert sent == [0, 0, 0, 1, 1] + finally: + env.close() + + +def test_without_sim_noise_kicks_are_the_nominal_speed(): + env = _env() + try: + cmd = tuple(np.array([0, 0, 0, 1, 0, 0]) for _ in range(2)) + env.step({"team_blue": cmd, "team_yellow": cmd}) + assert {c.kick_v_x for c in env.sent_commands} == {5} + finally: + env.close() diff --git a/utama_core/tests/rsoccer_simulator/test_sim_physics_knobs.py b/utama_core/tests/rsoccer_simulator/test_sim_physics_knobs.py new file mode 100644 index 00000000..66566961 --- /dev/null +++ b/utama_core/tests/rsoccer_simulator/test_sim_physics_knobs.py @@ -0,0 +1,269 @@ +"""`SimNoise`'s physics knobs: two-phase ball friction, kick direction, substeps, motion limits. + +The rsim tests of the C++ knobs need a robosim binary built from `vendor/rSim` (one with +`set_physics_params`); with the stock rc-robosim binary they skip. +""" + +import dataclasses +import math +from types import SimpleNamespace + +import numpy as np +import pytest + +from utama_core.rsoccer_simulator.src.Entities import Robot +from utama_core.rsoccer_simulator.src.ssl.envs import SSLStandardEnv +from utama_core.rsoccer_simulator.src.Utils.sim_noise import ( + KickAngleNoise, + MotionLimits, + SimDraw, + SimNoise, +) + +N = {"team_blue": 2, "team_yellow": 2} +TICK = 1 / 60 + +# --- settings and draws ------------------------------------------------------------------ + + +def test_with_the_new_knobs_off_the_recorded_settings_and_draws_are_what_they_were(): + """Match keys and replays from before the knobs existed must stay the same.""" + noise = SimNoise(seed=4, latency_s=(0.02, 0.05), kick_speed_offset_sd=0.2) + assert set(noise.to_dict()) == {"seed", "latency_s", "kick_speed_offset_sd", "kick_speed_sd", "vision", "vanishing"} + assert set(noise.draw(N, TICK).to_dict()) == {"latency_ticks", "kick_offset"} + assert not noise.native_physics and not noise.motion_limits + + +def test_turning_a_new_knob_on_leaves_the_older_draws_alone(): + old = SimNoise(seed=4, latency_s=(0.02, 0.05), kick_speed_offset_sd=0.2) + new = dataclasses.replace(old, ball_roll_decel=(0.28, 0.33), max_speed=(1.0, 2.0), kick_angle_offset_sd_deg=3.0) + a, b = old.draw(N, TICK), new.draw(N, TICK) + assert (a.latency_ticks, a.kick_offset) == (b.latency_ticks, b.kick_offset) + + +def test_new_knobs_round_trip_and_repeat_with_the_seed(): + noise = SimNoise( + seed=7, + ball_roll_decel=(0.28, 0.33), + ball_slide_decel=(2.0, 4.0), + kick_angle_offset_sd_deg=2.0, + kick_angle_sd_deg=1.0, + max_speed=(1.5, 2.0), + max_accel=(2.0, 4.0), + max_brake=(2.0, 5.0), + substeps=4, + ) + assert SimNoise.from_dict(noise.to_dict()) == noise + d = noise.draw(N, TICK) + assert d == noise.draw(N, TICK) + assert d != dataclasses.replace(noise, seed=8).draw(N, TICK) + assert 0.28 <= d.ball_roll_decel <= 0.33 and 2.0 <= d.ball_slide_decel <= 4.0 + assert all(1.5 <= v <= 2.0 for v in d.max_speed["team_blue"] + d.max_speed["team_yellow"]) + assert len(set(d.max_accel["team_blue"])) == 2, "drawn per robot" + assert noise.native_physics and noise.motion_limits + + +def test_sliding_needs_a_rolling_deceleration(): + with pytest.raises(AssertionError): + SimNoise(ball_slide_decel=(2.0, 4.0)) + + +# --- kick direction ------------------------------------------------------------------------ + + +def test_a_held_kick_keeps_one_direction_and_the_next_kick_draws_again(): + cfg = SimNoise(seed=1) + noise = KickAngleNoise({"team_blue": (5.0, 0.0), "team_yellow": (0.0, 0.0)}, 2.0, cfg.kick_angle_rngs(N)) + first = [noise.angle("team_blue", 0, True) for _ in range(3)] + assert len(set(first)) == 1 + assert noise.angle("team_blue", 0, False) == 0.0 + second = noise.angle("team_blue", 0, True) + assert second != first[0] + many = [] + for _ in range(2000): + many.append(noise.angle("team_blue", 0, True)) + noise.angle("team_blue", 0, False) + assert np.degrees(np.mean(many)) == pytest.approx(5.0, abs=0.2) + assert np.degrees(np.std(many)) == pytest.approx(2.0, abs=0.2) + + +def test_an_offset_alone_kicks_every_time_at_the_offset(): + cfg = SimNoise(seed=1) + noise = KickAngleNoise({"team_blue": (-3.0, 0.0), "team_yellow": (0.0, 0.0)}, 0.0, cfg.kick_angle_rngs(N)) + assert noise.angle("team_blue", 0, True) == pytest.approx(math.radians(-3.0)) + + +# --- motion limits ------------------------------------------------------------------------- + + +def _limits(speed=None, accel=None, brake=None) -> MotionLimits: + def per(v): + return None if v is None else {"team_blue": (v, v), "team_yellow": (v, v)} + + draw = SimDraw(latency_ticks={}, kick_offset={}, max_speed=per(speed), max_accel=per(accel), max_brake=per(brake)) + return MotionLimits(draw, 0.1) + + +def _limited(limits, cmd, vel, theta_deg=0.0): + command = Robot(v_x=cmd[0], v_y=cmd[1]) + limits.apply("team_blue", 0, command, SimpleNamespace(v_x=vel[0], v_y=vel[1], theta=theta_deg)) + return command.v_x, command.v_y + + +def test_speeding_up_is_capped_at_accel_times_dt(): + assert _limited(_limits(accel=2.0), (1.0, 0.0), (0.0, 0.0)) == pytest.approx((0.2, 0.0)) + # exactly at the cap: unchanged + assert _limited(_limits(accel=2.0), (0.2, 0.0), (0.0, 0.0)) == pytest.approx((0.2, 0.0)) + + +def test_slowing_down_is_capped_by_the_braking_rate_not_the_acceleration(): + limits = _limits(accel=1.0, brake=3.0) + assert _limited(limits, (0.0, 0.0), (1.0, 0.0)) == pytest.approx((0.7, 0.0)) + + +def test_top_speed_is_capped_in_any_direction(): + vx, vy = _limited(_limits(speed=1.0), (2.0, 2.0), (0.0, 0.0)) + assert math.hypot(vx, vy) == pytest.approx(1.0) + assert vx == pytest.approx(vy) + + +def test_limits_compare_the_local_command_with_the_world_velocity_by_the_robots_heading(): + """Facing +y and moving +y at 1 m/s, a forward command of 1 m/s is no change at all.""" + assert _limited(_limits(accel=0.1), (1.0, 0.0), (0.0, 1.0), theta_deg=90.0) == pytest.approx((1.0, 0.0)) + # Facing +y but moving +x: the command must turn the velocity, so it is capped. + vx, vy = _limited(_limits(accel=1.0), (1.0, 0.0), (1.0, 0.0), theta_deg=90.0) + world = (-vy, vx) # local -> world for a 90 degree heading + assert math.hypot(world[0] - 1.0, world[1]) == pytest.approx(0.1) + + +def test_no_limits_leave_the_command_alone(): + assert _limited(_limits(), (3.0, -1.0), (0.0, 0.0), theta_deg=37.0) == pytest.approx((3.0, -1.0)) + + +# --- in rsim ------------------------------------------------------------------------------- + + +def _env(sim_noise=None) -> SSLStandardEnv: + try: + env = SSLStandardEnv(n_robots_blue=1, n_robots_yellow=1, render_mode=None, sim_noise=sim_noise) + except RuntimeError as e: + if "set_physics_params" in str(e): + pytest.skip("needs a robosim binary built from vendor/rSim") + raise + env.reset() + env.teleport_robot(True, 0, -3.0, -2.5) + return env + + +def _step(env, blue=(0, 0, 0, 0, 0, 0)) -> None: + env.step({"team_blue": (np.array(blue, dtype=float),), "team_yellow": (np.zeros(6),)}) + + +def _ball_speed(env) -> float: + return math.hypot(env.frame.ball.v_x, env.frame.ball.v_y) + + +def _kick(env, theta=0.0, ticks=30) -> list: + """Blue 0 at (-2, 0) facing `theta` kicks a ball off its dribbler; ball velocity per tick.""" + env.teleport_robot(False, 0, -2.0, 0.0, theta) + env.teleport_ball(-2.0 + 0.1 * math.cos(theta), 0.1 * math.sin(theta)) + for _ in range(10): + _step(env, (0, 0, 0, 0, 1, 0)) + _step(env, (0, 0, 0, 1, 0, 0)) + out = [] + for _ in range(ticks): + _step(env) + out.append((env.frame.ball.v_x, env.frame.ball.v_y)) + return out + + +def test_a_rolling_ball_slows_at_the_set_rolling_deceleration(): + env = _env(SimNoise(ball_roll_decel=(0.3, 0.3))) + try: + env.teleport_robot(False, 0, -3.0, 2.5) + env.teleport_ball(-2.0, 0.0, 2.0, 0.0) + speeds = [] + for _ in range(60): + _step(env) + speeds.append(_ball_speed(env)) + # The first tick reports the ball still; rsim advances int(1000/60) = 16 ms of physics a tick + assert (speeds[1] - speeds[59]) / (58 * 0.016) == pytest.approx(0.3, rel=0.01) + finally: + env.close() + + +def test_a_kicked_ball_slides_then_rolls_from_five_sevenths_of_its_kick_speed(): + env = _env(SimNoise(ball_roll_decel=(0.3, 0.3), ball_slide_decel=(3.0, 3.0))) + try: + speeds = [math.hypot(*v) for v in _kick(env, ticks=60)] + decel = -np.diff(speeds) / 0.016 + assert np.median(decel[3:15]) == pytest.approx(3.0, rel=0.02) + assert np.median(decel[-15:]) == pytest.approx(0.3, rel=0.02) + slide_end = speeds[int(np.argmax(decel[3:] < 1.0)) + 3] + assert slide_end == pytest.approx(5 / 7 * speeds[0], rel=0.05) + finally: + env.close() + + +def test_kicks_leave_at_the_robots_drawn_direction_offset(): + straight = _env() + noisy = _env(SimNoise(seed=2, kick_angle_offset_sd_deg=10.0)) + try: + offset = noisy.sim_draw.kick_angle_offset["team_blue"][0] + assert abs(offset) > 5, "the seed should give a clear offset" + a0 = math.degrees(math.atan2(*reversed(_kick(straight)[5]))) + a1 = math.degrees(math.atan2(*reversed(_kick(noisy)[5]))) + assert abs(a0) < 1 + # rsim's frame is the mirror of ours; the angle is the deflection in rsim's frame + assert a1 - a0 == pytest.approx(offset, abs=2.0) + finally: + straight.close() + noisy.close() + + +def _drive(env, ticks=40) -> list: + env.teleport_robot(False, 0, -2.0, 1.0, 0.0) + out = [] + for _ in range(ticks): + _step(env, (2.0, 0.5, 1.0, 0, 0, 0)) + r = env.frame.robots_blue[0] + out.append((r.x, r.y, r.theta)) + return out + + +def test_substeps_change_the_physics_and_five_is_rsims_own(): + runs = [] + for substeps in (None, 5, 3): + env = _env(SimNoise(substeps=3) if substeps == 3 else None) + try: + if substeps == 5: + # what the env sends when another knob is on; with substeps 5 it must change nothing + try: + env.rsim.set_physics_params( + substeps=5, ball_slide_decel=-1, ball_roll_decel=-1, ball_slide_fraction=5 / 7 + ) + except RuntimeError: + pytest.skip("needs a robosim binary built from vendor/rSim") + runs.append(_drive(env)) + finally: + env.close() + assert runs[0] == runs[1] + assert runs[0] != runs[2] + assert math.dist(runs[0][-1][:2], runs[2][-1][:2]) < 0.05, "fewer substeps: same motion, roughly" + + +def test_motion_limits_cap_speed_and_acceleration_under_any_control(): + env = _env(SimNoise(max_speed=(1.0, 1.0), max_accel=(1.0, 1.0))) + try: + env.teleport_robot(False, 0, -2.0, 1.0, 1.2) + speeds = [] + for _ in range(90): + _step(env, (0.5, 2.0, 0, 0, 0, 0)) + r = env.frame.robots_blue[0] + speeds.append(math.hypot(r.v_x, r.v_y)) + # 1 m/s^2, less 4% (rsim steps 16 ms of physics per 1/60 s tick) and a little wheel lag; + # unlimited, rsim's own motors reach about 3 m/s^2 + assert 0.85 <= (speeds[40] - speeds[10]) / (30 * TICK) <= 1.0 + assert max(speeds) <= 1.01 + finally: + env.close() diff --git a/utama_core/tests/scenario_bench/test_bench_vs_standings_tool.py b/utama_core/tests/scenario_bench/test_bench_vs_standings_tool.py deleted file mode 100644 index 005038d1..00000000 --- a/utama_core/tests/scenario_bench/test_bench_vs_standings_tool.py +++ /dev/null @@ -1,72 +0,0 @@ -import dataclasses -import importlib.util -from pathlib import Path - -import pytest - -from utama_core.scenario_bench.hand_authored_scenarios import ( - all_hand_authored_scenarios, -) - -_TOOL = Path(__file__).resolve().parents[3] / "tools" / "bench_vs_standings.py" -_spec = importlib.util.spec_from_file_location("bench_vs_standings", _TOOL) -tool = importlib.util.module_from_spec(_spec) -_spec.loader.exec_module(tool) - - -def test_spearman_of_the_same_and_the_reversed_order(): - assert tool.spearman([1, 2, 3, 4], [10, 20, 30, 40]) == pytest.approx(1.0) - assert tool.spearman([1, 2, 3, 4], [40, 30, 20, 10]) == pytest.approx(-1.0) - - -def test_spearman_gives_tied_values_their_average_rank(): - assert tool._ranks([5.0, 1.0, 5.0]) == [2.5, 1.0, 2.5] - - -def test_pearson_sees_the_size_of_differences_not_only_their_order(): - assert tool.pearson([1, 2, 3], [2, 4, 6]) == pytest.approx(1.0) - assert tool.pearson([1, 2, 3], [1, 2, 30]) < tool.spearman([1, 2, 3], [1, 2, 30]) == pytest.approx(1.0) - - -def _result(a, b, score_a, score_b): - return {"config_a": a, "config_b": b, "score_a": score_a, "score_b": score_b} - - -def test_goal_difference_is_per_opponent_from_either_side_of_the_match(): - summary = { - "results": [ - _result("x", "o1", 3, 1), # x played o1 as config_a: +2 - _result("o2", "x", 0, 1), # and o2 as config_b: +1 - _result("x", "o3", 0, 5), # an opponent left out - _result("o1", "o2", 9, 0), # not x's - ] - } - assert tool.goal_difference(summary, "x", {"o1", "o2"}) == pytest.approx(1.5) - - -def test_a_candidate_s_effect_leaves_out_starts_played_against_it_or_the_baseline(): - base = list(all_hand_authored_scenarios())[0] - - def start(sid: str, opponent: str): - scenario = dataclasses.replace(base.scenario, config_b_name=opponent) - provenance = dataclasses.replace(base.provenance, situation="own kickoff") - return dataclasses.replace(base, scenario_id=sid, scenario=scenario, provenance=provenance) - - starts = [start("a", "high_press"), start("b", "counter_flow"), start("c", "split_shape")] - ok = {"outcome": 3, "error": None} - results = { - "build_counter_flow_kernel_strategy": {"a": ok, "b": ok, "c": ok}, - "build_split_shape_kernel_strategy": {"a": {"outcome": 0, "error": None}, "b": ok, "c": ok}, - } - - rows = tool.paired_rows(starts, results, "build_counter_flow_kernel_strategy", "build_split_shape_kernel_strategy") - - assert rows == [ - { - "situation": "own kickoff", - "candidate_outcomes": [3], - "baseline_outcomes": [0], - "candidate_errors": [], - "baseline_errors": [], - } - ] diff --git a/utama_core/tests/scenario_bench/test_hand_authored_scenarios.py b/utama_core/tests/scenario_bench/test_hand_authored_scenarios.py deleted file mode 100644 index e338e4bd..00000000 --- a/utama_core/tests/scenario_bench/test_hand_authored_scenarios.py +++ /dev/null @@ -1,80 +0,0 @@ -"""Tests for `utama_core.scenario_bench.hand_authored_scenarios` (the ~20 fixed -bench anchors from roadmap item 14's bank v1 shape). - -Every anchor must pass the static screen — these are meant to be the most -trustworthy scenarios in the bank, so a hand-authored scenario failing the -same screen a harvested one would fail is a bug in the authoring, not a -false positive to special-case around. -""" - -from __future__ import annotations - -from utama_core.config.field_params import STANDARD_FIELD_DIMS -from utama_core.scenario_bench.hand_authored_scenarios import ( - all_hand_authored_scenarios, -) -from utama_core.scenario_bench.start import ( - ScenarioLifecycle, - ScenarioTrigger, - start_tags, - static_screen, -) -from utama_core.tests.replay.test_scenario import ( - _ApplyScenarioTestManager, - _assert_close_to_scenario, - _build_runner_and_scenario, -) - - -def test_all_hand_authored_scenarios_pass_static_screen(): - for bench_scenario in all_hand_authored_scenarios(): - result = static_screen(bench_scenario.scenario) - assert result.ok, f"{bench_scenario.scenario_id} failed static screen: {result.violations}" - - -def test_all_hand_authored_scenarios_have_unique_ids(): - ids = [bs.scenario_id for bs in all_hand_authored_scenarios()] - assert len(ids) == len(set(ids)) - - -def test_all_hand_authored_scenarios_are_tagged_hand_authored(): - for bench_scenario in all_hand_authored_scenarios(): - assert bench_scenario.provenance.trigger is ScenarioTrigger.HAND_AUTHORED - assert bench_scenario.provenance.anchor_tick is None - assert bench_scenario.lifecycle is ScenarioLifecycle.CANDIDATE - - -def test_kickoff_anchor_applies_to_a_live_runner(): - """Proves a hand-authored `BenchScenario` flows through the same - `apply_scenario` path a harvested one would — the schema isn't just - self-consistent on paper, it round-trips through a real headless rsim - runner (same pattern `test_scenario.py` uses for replay-sourced - scenarios, minus the replay file dependency).""" - bench_scenario = next(bs for bs in all_hand_authored_scenarios() if bs.scenario_id == "kickoff_center_v1") - scenario = bench_scenario.to_scenario() - runner = _build_runner_and_scenario(scenario) - manager = _ApplyScenarioTestManager(scenario) - manager._runner = runner # noqa: SLF001 - test-only wiring, see eval_status - - passed = runner.run_test(test_manager=manager, episode_timeout=10.0, rsim_headless=True) - - assert manager.error is None, f"apply_scenario raised: {manager.error}" - assert passed, "test episode did not complete" - assert manager.applied - - _assert_close_to_scenario(manager.post_apply_positions, scenario, tol=0.15) - - -def test_every_anchor_is_played_from_the_candidate_s_side(): - """The candidate is friendly and defends the right goal (scorer: my_team_is_right=True). - The anchors were once returned in the frame they are written in, friendly on the left: - the candidate's keeper started in the goal it attacks, and the free kick "near our box" - was taken in front of the opponent's.""" - anchors = {bs.scenario_id: bs for bs in all_hand_authored_scenarios()} - for bs in anchors.values(): - keeper = next(r for r in bs.scenario.friendly_robots if r.id == 0) - assert keeper.x > STANDARD_FIELD_DIMS.full_field_half_length - 0.5, bs.scenario_id - - assert start_tags(anchors["direct_free_defending_near_box_v1"])["third"] == "defensive" - assert start_tags(anchors["direct_free_attacking_near_box_v1"])["third"] == "attacking" - assert start_tags(anchors["open_play_3v2_counter_v1"])["third"] == "attacking" diff --git a/utama_core/tests/scenario_bench/test_scenario_bench_reuse.py b/utama_core/tests/scenario_bench/test_scenario_bench_reuse.py deleted file mode 100644 index 2440880c..00000000 --- a/utama_core/tests/scenario_bench/test_scenario_bench_reuse.py +++ /dev/null @@ -1,101 +0,0 @@ -"""`tools/scenario_bench.py --reuse`: a start whose key is stored is not played again. - -Starts and keys are stubbed: `fingerprint.bench_key` is replaced by a key built from the -start and a per-config version number, so "editing" a config is bumping its version. -""" - -from __future__ import annotations - -import importlib.util -from pathlib import Path - -import pytest - -from utama_core.replay.match_cache import MatchCache -from utama_core.scenario_bench.hand_authored_scenarios import ( - all_hand_authored_scenarios, -) - -_TOOL = Path(__file__).resolve().parents[3] / "tools" / "scenario_bench.py" -_spec = importlib.util.spec_from_file_location("scenario_bench", _TOOL) -scenario_bench = importlib.util.module_from_spec(_spec) -_spec.loader.exec_module(scenario_bench) - -_SCENARIOS = list(all_hand_authored_scenarios())[:3] - - -@pytest.fixture -def rig(tmp_path, monkeypatch): - rig = type("Rig", (), {})() - rig.played = [] - rig.version = {"build_cand_kernel_strategy": 0, "build_base_kernel_strategy": 0, "build_opp_kernel_strategy": 0} - rig.outcome = 1 - rig.cache = MatchCache(tmp_path) - - def fake_start_result(bench_scenario, config, opponent, horizon_s): - rig.played.append((bench_scenario.scenario_id, config)) - return {"outcome": rig.outcome, "foul": False, "stalled": False, "error": None} - - def fake_key(_graph, start, config, opponent, *, horizon_s): - return f"{start['scenario_id']}-{config}{rig.version[config]}-{opponent}-{horizon_s}" - - monkeypatch.setattr(scenario_bench, "_start_result", fake_start_result) - monkeypatch.setattr(scenario_bench, "bench_key", fake_key) - monkeypatch.setattr(scenario_bench, "CodeGraph", lambda: None) - monkeypatch.setattr(scenario_bench, "_resolve_config_name", lambda name: f"build_{name}_kernel_strategy") - monkeypatch.setattr(scenario_bench.match_cache, "MatchCache", lambda: rig.cache) - - def run(spot_check=0.0): - rig.played.clear() - reuse = scenario_bench._Reuse(_SCENARIOS, ["cand", "base"], "opp", 20.0, 1, spot_check) - rows, _ = scenario_bench._score( - _SCENARIOS, candidate="cand", opponent="opp", horizon_s=20.0, repeats=1, baseline="base", reuse=reuse - ) - return rows, reuse.finish() - - rig.run = run - return rig - - -def test_a_second_run_plays_nothing_and_scores_the_same(rig): - first, _ = rig.run() - second, report = rig.run() - - assert rig.played == [] - assert report == {"reused": 6, "played": 0, "spot_checked": 0, "mismatches": 0} - assert second == first - - -def test_after_the_candidate_changes_only_the_candidate_plays(rig): - rig.run() - rig.version["build_cand_kernel_strategy"] += 1 - - _, report = rig.run() - - assert sorted(config for _sid, config in rig.played) == ["cand"] * 3 - assert report["reused"] == 3 - - -def test_a_spot_check_mismatch_evicts_the_records_the_run_used(rig): - rig.run() - rig.outcome = -1 # every replay now disagrees with its record - - _, report = rig.run(spot_check=0.2) - - assert report["mismatches"] == 1 - assert list(rig.cache.root.rglob("*.json")) == [] - - -def test_each_start_is_reported_as_it_comes_back_not_after_the_batch(rig, monkeypatch): - events = [] - monkeypatch.setattr(scenario_bench, "print", lambda *a, **k: events.append("print"), raising=False) - - def fake_start_result(bench_scenario, config, opponent, horizon_s): - events.append("play") - return {"outcome": 1, "foul": False, "stalled": False, "error": None} - - monkeypatch.setattr(scenario_bench, "_start_result", fake_start_result) - scenario_bench._score(_SCENARIOS, candidate="cand", opponent="opp", horizon_s=20.0, repeats=1, baseline="base") - - # candidate and baseline play each start, then it is reported, before the next start plays - assert events[:3] == ["play", "play", "print"] diff --git a/utama_core/tests/scenario_bench/test_scenario_bench_tool.py b/utama_core/tests/scenario_bench/test_scenario_bench_tool.py deleted file mode 100644 index 1bc26575..00000000 --- a/utama_core/tests/scenario_bench/test_scenario_bench_tool.py +++ /dev/null @@ -1,370 +0,0 @@ -import dataclasses -import importlib.util -import json -import math -import sys -from pathlib import Path - -import pytest - -from utama_core.scenario_bench.hand_authored_scenarios import ( - all_hand_authored_scenarios, -) -from utama_core.scenario_bench.start import load_bank, save_bank - -_TOOL = Path(__file__).resolve().parents[3] / "tools" / "scenario_bench.py" -_spec = importlib.util.spec_from_file_location("scenario_bench", _TOOL) -scenario_bench = importlib.util.module_from_spec(_spec) -_spec.loader.exec_module(scenario_bench) - - -def _moved(bench_scenario, scenario_id: str, dx: float): - return dataclasses.replace( - bench_scenario, - scenario_id=scenario_id, - scenario=dataclasses.replace(bench_scenario.scenario, ball_x=bench_scenario.scenario.ball_x + dx), - ) - - -def test_growing_a_bank_adds_every_new_harvested_start_and_drops_duplicates(tmp_path, monkeypatch): - existing = list(all_hand_authored_scenarios()) - old_bank, new_bank = tmp_path / "bank_old.json", tmp_path / "bank_new.json" - save_bank(existing, old_bank, bank_id="old") - harvested = [ - _moved(existing[0], "copy_of_existing", 0.01), - _moved(existing[0], "new_a", 1.0), - _moved(existing[1], "new_b", 1.0), - ] - monkeypatch.setattr(scenario_bench, "harvest_run_dir", lambda *a, **k: (harvested, {})) - monkeypatch.setattr( - sys, - "argv", - [ - "scenario_bench.py", - "--harvest-from", - str(tmp_path), - "--merge-into", - str(old_bank), - "--save-bank", - str(new_bank), - "--list-scenarios", - ], - ) - - assert scenario_bench.main() == 0 - - # the near-copy duplicates the old bank - bank_id, saved = load_bank(new_bank) - assert bank_id == "bank_new" - assert [s.scenario_id for s in saved] == [s.scenario_id for s in existing] + ["new_a", "new_b"] - - -def test_overall_line_reports_t_and_a_no_difference_run(): - rows = [{"delta": d} for d in (-0.5, -0.1, -0.3, 0.1)] - assert "t -1.55" in scenario_bench._overall(rows) - assert "the same on all 3 scenarios" in scenario_bench._overall([{"delta": 0.0}] * 3) - assert scenario_bench._overall([{"delta": None}]) is None - - -def test_a_harvest_drops_duplicate_starts_even_without_a_bank_to_merge_into(tmp_path, monkeypatch): - base = list(all_hand_authored_scenarios())[0] - harvested = [_moved(base, "first", 1.0), _moved(base, "same_again", 1.01)] - monkeypatch.setattr(scenario_bench, "harvest_run_dir", lambda *a, **k: (harvested, {})) - monkeypatch.setattr( - sys, - "argv", - [ - "scenario_bench.py", - "--harvest-from", - str(tmp_path), - "--families", - base.provenance.family.value, - "--list-scenarios", - ], - ) - - scenarios, merged, _ = scenario_bench._load_bank(scenario_bench.parse_args()) - - assert "same_again" not in [s.scenario_id for s in scenarios] - assert "first" in [s.scenario_id for s in scenarios] - - -def test_a_scenario_that_errored_on_either_side_is_left_out_not_scored_as_neutral(monkeypatch): - # a start that fails to set up comes back NEUTRAL with an error; counting it as a - # real NEUTRAL outcome would put a fake zero (or a fake difference) into the A/B - ok, broken = list(all_hand_authored_scenarios())[:2] - - def fake_runs(bench_scenario, config, opponent, horizon_s, repeats, *_reuse): - failed = bench_scenario.scenario_id == broken.scenario_id and config == "base" - return {"outcomes": [0] * repeats, "fouls": 0, "stalls": 0, "errors": ["setup failed"] if failed else []} - - monkeypatch.setattr(scenario_bench, "_runs", fake_runs) - rows, _ = scenario_bench._score( - [ok, broken], candidate="cand", opponent="opp", horizon_s=1.0, repeats=1, baseline="base" - ) - - assert [r["delta"] for r in rows] == [0.0, None] - - -def test_an_earlier_run_s_errored_scenarios_are_not_a_baseline(tmp_path): - path = tmp_path / "earlier.json" - path.write_text( - json.dumps( - { - "opponent": "opp", - "horizon_s": 20.0, - "repeats": 1, - "results": [ - {"scenario_id": "fine", "candidate_outcomes": [1], "candidate_errors": []}, - {"scenario_id": "broken", "candidate_outcomes": [0], "candidate_errors": ["setup failed"]}, - ], - } - ) - ) - runs, _ = scenario_bench._load_against(path, opponent="opp", horizon_s=20.0, repeats=1) - assert runs == {"fine": {"outcomes": [1], "signals": None}} - - -def _many(n): - base = list(all_hand_authored_scenarios())[0] - return [_moved(base, f"s{i:03d}", 0.2 * i) for i in range(n)] - - -def _fake_runs_candidate_worse(bench_scenario, config, opponent, horizon_s, repeats, *_reuse): - # the candidate loses the ball on 3 starts in 4, the baseline never does - worse = config == "cand" and int(bench_scenario.scenario_id[1:]) % 4 != 0 - return {"outcomes": [-1 if worse else 0] * repeats, "fouls": 0, "stalls": 0, "errors": []} - - -def test_stop_at_t_ends_once_the_difference_is_clear(monkeypatch): - monkeypatch.setattr(scenario_bench, "_runs", _fake_runs_candidate_worse) - kwargs = dict(candidate="cand", opponent="opp", horizon_s=1.0, repeats=1, baseline="base") - - full, full_reason = scenario_bench._score(_many(60), **kwargs) - stopped, reason = scenario_bench._score(_many(60), **kwargs, stop_at_t=4.0, check_every=10) - - assert len(full) == 60 and full_reason is None - assert len(stopped) < 60 and len(stopped) % 10 == 0 # stops at a check, before the end - assert reason == "detected" - assert abs(scenario_bench._t([r["delta"] for r in stopped])) >= 4.0 - - -def test_stop_at_t_scores_a_shuffled_sample_not_the_bank_s_first_starts(monkeypatch): - # a bank is ordered by match and family: its first 20 starts are not a fair sample - monkeypatch.setattr(scenario_bench, "_runs", _fake_runs_candidate_worse) - kwargs = dict(candidate="cand", opponent="opp", horizon_s=1.0, repeats=1, baseline="base") - - first, _ = scenario_bench._score(_many(60), **kwargs, stop_at_t=4.0, check_every=10) - again, _ = scenario_bench._score(_many(60), **kwargs, stop_at_t=4.0, check_every=10) - - ids = [r["scenario_id"] for r in first] - assert ids == [r["scenario_id"] for r in again] # reproducible - assert ids != [f"s{i:03d}" for i in range(len(ids))] - - -def test_stop_at_t_stops_a_candidate_no_different_from_the_baseline_as_futile(monkeypatch): - # the same outcomes on both sides never reach |t| >= 4; without futility this costs - # the whole bank - monkeypatch.setattr( - scenario_bench, - "_runs", - lambda bench_scenario, config, opponent, horizon_s, repeats, *_reuse: { - "outcomes": [0] * repeats, - "fouls": 0, - "stalls": 0, - "errors": [], - }, - ) - kwargs = dict(candidate="cand", opponent="opp", horizon_s=1.0, repeats=1, baseline="base") - - rows, reason = scenario_bench._score(_many(60), **kwargs, stop_at_t=4.0, check_every=10) - - assert (len(rows), reason) == (10, "futile") - - -def _spread(a: float, n: int = 200) -> list[float]: - # mean 0, deltas +a and -a: the only thing deciding futility is the stderr - return [a, -a] * (n // 2) - - -def test_futile_needs_the_upper_bound_of_the_difference_under_futile_below(): - # |mean| + FUTILE_Z * stderr == FUTILE_BELOW exactly at a = a_edge - n = 200 - stderr_per_a = math.sqrt(n / (n - 1)) / math.sqrt(n) - a_edge = scenario_bench.FUTILE_BELOW / (scenario_bench.FUTILE_Z * stderr_per_a) - - assert scenario_bench._stop_reason(_spread(a_edge * 0.99, n), stop_at_t=4.0) == "futile" - assert scenario_bench._stop_reason(_spread(a_edge * 1.01, n), stop_at_t=4.0) is None - # a real mean difference counts against futility, not just the noise - shifted = [d + 0.05 for d in _spread(a_edge * 0.99, n)] - assert scenario_bench._stop_reason(shifted, stop_at_t=4.0) is None - - -def test_a_clear_difference_is_detected_not_futile(): - deltas = [-1.0] * 30 + [0.0] * 70 # t far past 4 - assert scenario_bench._stop_reason(deltas, stop_at_t=4.0) == "detected" - assert scenario_bench._stop_reason([0.0], stop_at_t=4.0) is None # one delta decides nothing - - -def _signals(**changed) -> dict: - return {name: 0 for name in scenario_bench.SIGNALS} | changed - - -def test_signals_are_paired_per_start_and_summarised_with_their_t(monkeypatch): - # the candidate concedes 3 s more danger on every other start and is otherwise the same - def fake_runs(bench_scenario, config, opponent, horizon_s, repeats, *_reuse): - worse = config == "cand" and int(bench_scenario.scenario_id[1:]) % 2 == 0 - signals = _signals(danger_s=3.0 if worse else 0.0) - return {"outcomes": [0] * repeats, "signals": [signals] * repeats, "fouls": 0, "stalls": 0, "errors": []} - - monkeypatch.setattr(scenario_bench, "_runs", fake_runs) - rows, _ = scenario_bench._score( - _many(10), candidate="cand", opponent="opp", horizon_s=1.0, repeats=1, baseline="base" - ) - - summary = scenario_bench._signal_summary(rows) - assert summary["danger_s"]["mean_delta"] == 1.5 - assert summary["danger_s"]["changed"] == 5 - assert summary["danger_s"]["t"] == 1.5 / (math.sqrt(2.5) / math.sqrt(10)) - assert summary["shots"] == {"mean_delta": 0.0, "t": 0.0, "changed": 0, "n_paired": 10} - - -def test_an_earlier_run_without_signals_still_pairs_outcomes(): - rows = [] - cand = {"outcomes": [1], "signals": [_signals(shots=1)], "fouls": 0, "stalls": 0, "errors": []} - against = {"s000": {"outcomes": [0], "signals": None}} - scenario_bench._score_batch(rows, _many(1), [(cand, None)], against, 1) - - assert rows[0]["delta"] == 1.0 - assert rows[0]["signal_deltas"] is None - assert scenario_bench._signal_report({"signals": scenario_bench._signal_summary(rows), "aggregates": []}) == [] - - -def test_a_tactic_change_is_placed_after_the_state_at_the_same_time(): - trace_lines = [" 0.00 STOP ball", " 5.75 NORMAL_START ball", " 6.00 NORMAL_START ball"] - records = [ - {"event": "referee", "sim_time": 0.0}, - {"event": "intention", "sim_time": 5.77, "tactic_id": "attack", "note": "GiveAndGoTactic", "robot_ids": [1, 2]}, - {"event": "trace", "sim_time": 5.9, "key": "x", "value": 1}, - ] - intentions = scenario_bench._intention_lines(records, t0=0.02) - - assert intentions == [(5.75, " 5.75 tactic attack = GiveAndGoTactic [1, 2]")] - assert scenario_bench.merge_timeline(trace_lines, intentions) == [ - " 0.00 STOP ball", - " 5.75 NORMAL_START ball", - " 5.75 tactic attack = GiveAndGoTactic [1, 2]", - " 6.00 NORMAL_START ball", - ] - - -def test_play_records_a_start_and_returns_its_timeline(tmp_path, monkeypatch): - from utama_core.replay import columnar_writer - from utama_core.scenario_bench import scenario_scorer - - for module in (scenario_bench, scenario_scorer, columnar_writer): - monkeypatch.setattr(module, "REPLAY_BASE_PATH", tmp_path) - start = next(bs for bs in all_hand_authored_scenarios() if bs.scenario_id == "kickoff_center_v1") - - play = scenario_bench.play_start(start, candidate="high_press", opponent="split_shape", horizon_s=1.0) - - assert play["result"]["error"] is None - assert set(play["result"]["signals"]) == set(scenario_bench.SIGNALS) - folder = tmp_path / scenario_bench.PLAY_DIR / "kickoff_center_v1" - assert play["replay"] == str(folder / "high_press_vs_split_shape_s0.npz") - for suffix in (".npz", ".intentions.jsonl", ".trace.txt"): - assert (folder / f"high_press_vs_split_shape_s0{suffix}").exists() - assert "PREPARE_KICKOFF" in play["timeline"][0] - assert (folder / "high_press_vs_split_shape_s0.trace.txt").read_text().splitlines() == play["timeline"] - - -def test_play_takes_no_baseline(monkeypatch, capsys): - monkeypatch.setattr( - sys, - "argv", - ["scenario_bench.py", "--play", "x", "--candidate", "a", "--opponent", "b", "--baseline", "c"], - ) - with pytest.raises(SystemExit): - scenario_bench.parse_args() - assert "--play shows starts for --candidate alone" in capsys.readouterr().err - - -def test_where_must_name_a_tag_and_a_value(monkeypatch, capsys): - monkeypatch.setattr(sys, "argv", ["scenario_bench.py", "--list-scenarios", "--where", "zone=defensive"]) - with pytest.raises(SystemExit): - scenario_bench.parse_args() - assert "expected TAG=VALUE" in capsys.readouterr().err - monkeypatch.setattr(sys, "argv", ["scenario_bench.py", "--list-scenarios", "--where", "ball=theirs,loose"]) - assert scenario_bench.parse_args().where == {"ball": {"theirs", "loose"}} - - -def _from_match(config_b): - base = list(all_hand_authored_scenarios())[0] - return dataclasses.replace(base, scenario=dataclasses.replace(base.scenario, config_b_name=config_b)) - - -def test_a_start_is_played_against_the_opponent_it_came_from_unless_one_is_given(monkeypatch): - start = _from_match("build_high_press_kernel_strategy") - assert scenario_bench.start_opponent(start) == "build_high_press_kernel_strategy" - assert scenario_bench.start_opponent(start, "counter_flow") == "build_counter_flow_kernel_strategy" - # a strategy that lives on another branch, and a start with no source match: not playable - assert scenario_bench.start_opponent(_from_match("build_not_on_this_branch_kernel_strategy")) is None - assert scenario_bench.start_opponent(_from_match(None)) is None - - played = [] - - def fake_runs(bench_scenario, config, opponent, horizon_s, repeats, *_reuse): - played.append((config, opponent)) - return {"outcomes": [0] * repeats, "fouls": 0, "stalls": 0, "errors": []} - - monkeypatch.setattr(scenario_bench, "_runs", fake_runs) - scenario_bench._score([start], candidate="cand", opponent=None, horizon_s=1.0, repeats=1, baseline="base") - - assert played == [("cand", "build_high_press_kernel_strategy"), ("base", "build_high_press_kernel_strategy")] - - -def _row(situation, candidate, baseline, errors=()): - return { - "situation": situation, - "candidate_outcomes": candidate, - "baseline_outcomes": baseline, - "candidate_errors": list(errors), - "baseline_errors": [], - } - - -def test_the_effect_weights_each_situation_s_paired_change_by_how_often_it_happens(): - # own free kicks in the attacking third: 0.5 per team per live minute x 10 live minutes = 5 - # a match; the opponent winning the ball in midfield: 0.2 x 10 = 2 a match - weights = { - "live_min_per_match": 10.0, - "per_team_live_min": {"free kick, attacking third": 0.5, "ball won, middle third": 0.2}, - } - goal_for, goal_against, nothing = 3, -3, 0 - rows = [ - _row("own free kick, attacking third", [goal_for], [nothing]), - _row("own free kick, attacking third", [nothing], [nothing]), - _row("opponent ball won, middle third", [goal_against], [goal_against]), - _row("opponent ball won, middle third", [nothing], [goal_against]), - _row(None, [goal_for], [nothing]), # a hand-authored start has no situation - _row("own free kick, attacking third", [goal_for], [nothing], errors=["setup failed"]), - ] - - effect = scenario_bench.situation_effect(rows, weights) - - free_kick = effect["types"]["own free kick, attacking third"] - assert (free_kick["n"], free_kick["per_match"]) == (2, 5.0) - assert (free_kick["candidate_p_for"], free_kick["baseline_p_for"]) == (0.5, 0.0) - assert free_kick["delta_p_for"] == 0.5 - assert free_kick["delta_p_for_se"] == pytest.approx(0.5) # paired differences 1 and 0 - assert free_kick["scored_per_match"] == 2.5 - won = effect["types"]["opponent ball won, middle third"] - assert (won["candidate_p_against"], won["baseline_p_against"]) == (0.5, 1.0) - assert won["conceded_per_match"] == -1.0 - assert won["net_per_match"] == 1.0 - assert effect["total"] == { - "scored_per_match": 2.5, - "conceded_per_match": -1.0, - "net_per_match": 3.5, - "net_per_match_se": pytest.approx(math.sqrt(2.5**2 + 1.0**2)), - } diff --git a/utama_core/tests/scenario_bench/test_scenario_harvester.py b/utama_core/tests/scenario_bench/test_scenario_harvester.py deleted file mode 100644 index 9cb894c0..00000000 --- a/utama_core/tests/scenario_bench/test_scenario_harvester.py +++ /dev/null @@ -1,477 +0,0 @@ -"""Tests for `utama_core.scenario_bench.scenario_harvester`. - -Uses a synthetic `.npz` replay (`ColumnarReplayWriter`, same writer -`round_robin.py` uses) plus a hand-written `.intentions.jsonl` sidecar — -referee-command info lives entirely in the sidecar here (frames carry -`referee=None`), matching `scenario_from_replay`'s documented fallback path, -except in the free-kick tick tests, whose frames carry the command one tick -after the sidecar's time for it, as a real replay's do. -""" - -from __future__ import annotations - -import dataclasses -import json -from pathlib import Path -from types import SimpleNamespace -from typing import Optional - -import pytest - -from utama_core.entities.data.referee import RefereeData -from utama_core.entities.data.vector import Vector2D, Vector3D -from utama_core.entities.game import Ball, GameFrame, Robot -from utama_core.entities.referee.referee_command import RefereeCommand -from utama_core.entities.referee.stage import Stage -from utama_core.replay.columnar_writer import ( - ColumnarReplayWriter, - ColumnarReplayWriterConfig, -) -from utama_core.scenario_bench import scenario_harvester -from utama_core.scenario_bench.hand_authored_scenarios import ( - all_hand_authored_scenarios, -) -from utama_core.scenario_bench.scenario_harvester import ( - RestartTransition, - _kicking_is_yellow, - find_restart_transitions, - harvest_replay, - harvest_run_dir, - match_is_trustworthy, - restart_situation, - sample_by_situation, -) -from utama_core.scenario_bench.start import ScenarioFamily, ScenarioTrigger - - -def _robot(id_: int, x: float, y: float, *, friendly: bool) -> Robot: - return Robot( - id=id_, - is_friendly=friendly, - has_ball=False, - p=Vector2D(x, y), - v=Vector2D(0, 0), - a=Vector2D(0, 0), - orientation=0.0, - ) - - -def _referee(command: RefereeCommand) -> RefereeData: - return RefereeData( - source_identifier=None, - time_sent=0.0, - time_received=0.0, - referee_command=command, - referee_command_timestamp=0.0, - stage=Stage.NORMAL_FIRST_HALF, - stage_time_left=0.0, - blue_team=None, - yellow_team=None, - ) - - -def _frame(ts: float, command: Optional[RefereeCommand] = None, ball_x: float = 0.0) -> GameFrame: - return GameFrame( - ts=ts, - my_team_is_yellow=True, - my_team_is_right=True, - friendly_robots={0: _robot(0, -4.0, 0.0, friendly=True), 1: _robot(1, -1.0, 0.5, friendly=True)}, - enemy_robots={0: _robot(0, 4.0, 0.0, friendly=False), 1: _robot(1, 1.0, -0.5, friendly=False)}, - ball=Ball(p=Vector3D(ball_x, 0.0, 0.0), v=Vector3D(0.0, 0.0, 0.0), a=Vector3D(0.0, 0.0, 0.0)), - referee=_referee(command) if command is not None else None, - ) - - -def _write_replay(tmp_path, name: str, ts_values: list[float], frame=_frame): - path = tmp_path / f"{name}.npz" - writer = ColumnarReplayWriter( - ColumnarReplayWriterConfig(replay_name=name, overwrite_existing=True), - my_team_is_yellow=True, - exp_friendly=2, - exp_enemy=2, - path=path, - ) - for ts in ts_values: - writer.write_frame(frame(ts)) - writer.close() - return path - - -def _write_sidecar(replay_path, rows: list[dict]) -> None: - sidecar_path = replay_path.with_name(f"{replay_path.stem}.intentions.jsonl") - with open(sidecar_path, "w") as f: - for row in rows: - f.write(json.dumps(row) + "\n") - - -def _write_stats(replay_path, stall_events: list) -> None: - stats_path = replay_path.with_name(f"{replay_path.stem}.stats.json") - stats_path.write_text(json.dumps({"stall_events": stall_events})) - - -# --- find_restart_transitions ------------------------------------------------- - - -def test_a_kickoff_or_penalty_is_found_where_it_is_awarded_not_where_it_goes_live(tmp_path): - """The start is the restart's own command, as for a free kick. The NORMAL_START after it - could only be replayed as live play, the penalty or kickoff already over.""" - sidecar = tmp_path / "match.intentions.jsonl" - sidecar.write_text( - "\n".join( - json.dumps(row) - for row in [ - {"event": "referee", "sim_time": 0.0, "command": "PREPARE_KICKOFF_YELLOW"}, - {"event": "referee", "sim_time": 3.0, "command": "NORMAL_START"}, - {"event": "referee", "sim_time": 10.0, "command": "STOP"}, - {"event": "referee", "sim_time": 11.0, "command": "BALL_PLACEMENT_YELLOW"}, - {"event": "referee", "sim_time": 13.0, "command": "PREPARE_PENALTY_BLUE"}, - {"event": "referee", "sim_time": 18.0, "command": "NORMAL_START"}, - ] - ) - ) - transitions = find_restart_transitions(sidecar) - assert [(t.sim_time, t.command, t.kicking_is_yellow) for t in transitions] == [ - (0.0, RefereeCommand.PREPARE_KICKOFF_YELLOW, True), - (13.0, RefereeCommand.PREPARE_PENALTY_BLUE, False), - ] - - -def test_finds_direct_free_transition(tmp_path): - sidecar = tmp_path / "match.intentions.jsonl" - sidecar.write_text( - "\n".join( - json.dumps(row) - for row in [ - {"event": "referee", "sim_time": 10.0, "command": "STOP"}, - {"event": "referee", "sim_time": 15.0, "command": "DIRECT_FREE_BLUE"}, - ] - ) - ) - transitions = find_restart_transitions(sidecar) - assert len(transitions) == 1 - assert transitions[0].command == RefereeCommand.DIRECT_FREE_BLUE - assert transitions[0].kicking_is_yellow is False - - -def test_live_play_and_stoppages_alone_produce_no_transition(tmp_path): - sidecar = tmp_path / "match.intentions.jsonl" - sidecar.write_text( - "\n".join( - json.dumps({"event": "referee", "sim_time": t, "command": c}) - for t, c in [(0.0, "NORMAL_START"), (5.0, "STOP"), (6.0, "BALL_PLACEMENT_BLUE"), (9.0, "NORMAL_START")] - ) - ) - assert find_restart_transitions(sidecar) == [] - - -def test_missing_sidecar_returns_empty(tmp_path): - assert find_restart_transitions(tmp_path / "nonexistent.intentions.jsonl") == [] - - -def test_duplicate_transitions_within_gap_are_deduplicated(tmp_path): - sidecar = tmp_path / "match.intentions.jsonl" - sidecar.write_text( - "\n".join( - json.dumps(row) - for row in [ - {"event": "referee", "sim_time": 0.0, "command": "STOP"}, - {"event": "referee", "sim_time": 3.0, "command": "DIRECT_FREE_YELLOW"}, - {"event": "referee", "sim_time": 3.05, "command": "DIRECT_FREE_YELLOW"}, - {"event": "referee", "sim_time": 3.1, "command": "STOP"}, - {"event": "referee", "sim_time": 3.2, "command": "DIRECT_FREE_YELLOW"}, - ] - ) - ) - # The repeated row (a score or stage change logs the same command again) is not a - # transition at all, and the free kick given again 0.2 s later is within the gap. - transitions = find_restart_transitions(sidecar) - assert len(transitions) == 1 - - -# --- match_is_trustworthy ----------------------------------------------------- - - -def test_trustworthy_when_stats_file_has_zero_stalls(tmp_path): - stats_path = tmp_path / "match.stats.json" - stats_path.write_text(json.dumps({"stall_events": []})) - assert match_is_trustworthy(stats_path) is True - - -def test_untrustworthy_when_stats_file_has_stalls(tmp_path): - stats_path = tmp_path / "match.stats.json" - stats_path.write_text(json.dumps({"stall_events": [{"kind": "RESTART_STALL"}]})) - assert match_is_trustworthy(stats_path) is False - - -def test_untrustworthy_when_stats_file_missing(tmp_path): - assert match_is_trustworthy(tmp_path / "nonexistent.stats.json") is False - - -def test_untrustworthy_when_stats_file_unparseable(tmp_path): - stats_path = tmp_path / "match.stats.json" - stats_path.write_text("not json") - assert match_is_trustworthy(stats_path) is False - - -# --- harvest_replay / harvest_run_dir (synthetic .npz + sidecar) -------------- - - -def test_harvest_replay_extracts_scenario_at_transition(tmp_path): - replay_path = _write_replay(tmp_path, "clean_match", [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]) - _write_sidecar( - replay_path, - [ - {"event": "referee", "sim_time": 0.0, "command": "PREPARE_KICKOFF_YELLOW"}, # opening kickoff: skipped - {"event": "referee", "sim_time": 0.5, "command": "NORMAL_START"}, - {"event": "referee", "sim_time": 1.0, "command": "STOP"}, - {"event": "referee", "sim_time": 2.0, "command": "PREPARE_KICKOFF_BLUE"}, - {"event": "referee", "sim_time": 3.0, "command": "NORMAL_START"}, - ], - ) - - scenarios = harvest_replay(replay_path, source_run_id="test_run", evaluator_version="abc123") - - assert len(scenarios) == 1 - bench_scenario = scenarios[0] - assert bench_scenario.provenance.source_run_id == "test_run" - assert bench_scenario.provenance.evaluator_version == "abc123" - assert bench_scenario.provenance.anchor_tick == 2.0 - assert bench_scenario.provenance.family == ScenarioFamily.KICKOFF - assert bench_scenario.provenance.perspective == "candidate_defending" # blue kicks off; the candidate is yellow - assert bench_scenario.scenario.referee_command == RefereeCommand.PREPARE_KICKOFF_BLUE - assert bench_scenario.scenario.friendly_robots - assert bench_scenario.scenario.enemy_robots - - -def test_harvest_run_dir_skips_untrusted_matches(tmp_path): - trusted_replay = _write_replay(tmp_path, "clean_match", [0.0, 1.0, 2.0, 3.0]) - _write_sidecar( - trusted_replay, - [ - {"event": "referee", "sim_time": 0.0, "command": "STOP"}, - {"event": "referee", "sim_time": 2.0, "command": "FORCE_START"}, - ], - ) - _write_stats(trusted_replay, []) - - untrusted_replay = _write_replay(tmp_path, "buggy_match", [0.0, 1.0, 2.0, 3.0]) - _write_sidecar( - untrusted_replay, - [ - {"event": "referee", "sim_time": 0.0, "command": "STOP"}, - {"event": "referee", "sim_time": 2.0, "command": "FORCE_START"}, - ], - ) - _write_stats(untrusted_replay, [{"kind": "RESTART_STALL"}]) - - no_stats_replay = _write_replay(tmp_path, "no_stats_match", [0.0, 1.0, 2.0, 3.0]) - _write_sidecar( - no_stats_replay, - [ - {"event": "referee", "sim_time": 0.0, "command": "STOP"}, - {"event": "referee", "sim_time": 2.0, "command": "FORCE_START"}, - ], - ) - # Deliberately no .stats.json written for this one. - - # each match's referee log is not a replay of its own - (tmp_path / "another_match.sparse_referee.pkl").write_bytes(b"") - - scenarios, report = harvest_run_dir(tmp_path, evaluator_version="abc123") - - assert report["matches_seen"] == 3 - assert report["matches_trusted"] == 1 - assert report["matches_untrusted"] == 2 - assert len(scenarios) == 1 - assert scenarios[0].provenance.source_replay == trusted_replay - - -def test_finds_free_kick_after_ball_placement(tmp_path): - """`CustomReferee` places the ball before most free kicks; only STOP -> DIRECT_FREE - was matched, so a whole round-robin yielded 14 free kicks.""" - sidecar = tmp_path / "match.intentions.jsonl" - sidecar.write_text( - "\n".join( - json.dumps(row) - for row in [ - {"event": "referee", "sim_time": 10.0, "command": "STOP"}, - {"event": "referee", "sim_time": 11.0, "command": "BALL_PLACEMENT_YELLOW"}, - {"event": "referee", "sim_time": 15.0, "command": "DIRECT_FREE_YELLOW"}, - ] - ) - ) - transitions = find_restart_transitions(sidecar) - assert [t.command for t in transitions] == [RefereeCommand.DIRECT_FREE_YELLOW] - - -def test_harvest_replay_skips_the_opening_kickoff(tmp_path): - """Every match opens with the same kickoff; from a 231-match run it was 222 of 317 - harvested scenarios. A kickoff after a goal is kept.""" - replay_path = _write_replay(tmp_path, "m", [0.0, 1.0, 2.0, 3.0, 4.0]) - _write_sidecar( - replay_path, - [ - {"event": "referee", "sim_time": 0.0, "command": "PREPARE_KICKOFF_YELLOW"}, - {"event": "referee", "sim_time": 1.0, "command": "NORMAL_START"}, - {"event": "referee", "sim_time": 2.0, "command": "PREPARE_KICKOFF_BLUE"}, - {"event": "referee", "sim_time": 3.0, "command": "NORMAL_START"}, - ], - ) - scenarios = harvest_replay(replay_path, source_run_id="r", evaluator_version="abc") - assert [s.provenance.anchor_tick for s in scenarios] == [2.0] - - -_TICK = 1 / 60 - - -def _placement_then_free_kick(ts: float) -> GameFrame: - """A replay frame shows the referee's decision on the frame before it: the match log puts - DIRECT_FREE_YELLOW at t=15.0, the replay first shows it one tick later. The ball moves - 1 m a tick so the test can tell which tick a start was taken from.""" - awarded = ts > 15.0 + _TICK / 2 - command = RefereeCommand.DIRECT_FREE_YELLOW if awarded else RefereeCommand.BALL_PLACEMENT_YELLOW - return _frame(ts, command, ball_x=round((ts - 15.0) / _TICK)) - - -def test_a_free_kick_start_is_the_first_replay_tick_that_shows_the_free_kick(tmp_path): - """The nearest tick to the match log's time still showed BALL_PLACEMENT_*: replayed, the - free kick sat in ball placement and was never taken (bank_v7, 241 starts).""" - ticks = [15.0 + k * _TICK for k in range(-2, 4)] - replay_path = _write_replay(tmp_path, "m", ticks, _placement_then_free_kick) - _write_sidecar( - replay_path, - [ - {"event": "referee", "sim_time": 10.0, "command": "STOP"}, - {"event": "referee", "sim_time": 11.0, "command": "BALL_PLACEMENT_YELLOW"}, - {"event": "referee", "sim_time": 15.0, "command": "DIRECT_FREE_YELLOW"}, - ], - ) - - [start] = harvest_replay(replay_path, source_run_id="r", evaluator_version="abc") - - assert start.scenario.referee_command == RefereeCommand.DIRECT_FREE_YELLOW - assert start.scenario.frame_ts == pytest.approx(15.0 + _TICK, abs=1e-6) - assert start.scenario.ball_x == pytest.approx(1.0) - assert start.provenance.anchor_tick == 15.0 - - -def test_a_start_whose_command_the_replay_never_shows_is_dropped(tmp_path): - ticks = [15.0 + k * _TICK for k in range(-2, 4)] - replay_path = _write_replay(tmp_path, "m", ticks, lambda ts: _frame(ts, RefereeCommand.STOP)) - _write_sidecar( - replay_path, - [ - {"event": "referee", "sim_time": 10.0, "command": "STOP"}, - {"event": "referee", "sim_time": 15.0, "command": "DIRECT_FREE_YELLOW"}, - ], - ) - - assert harvest_replay(replay_path, source_run_id="r", evaluator_version="abc") == [] - - -# --- situation types and the per-situation sample -------------------------------- - - -@pytest.mark.parametrize( - "command, ball_x, situation", - [ - # the candidate is yellow, defending the right goal: it attacks -x - (RefereeCommand.DIRECT_FREE_YELLOW, -3.0, "own free kick, attacking third"), - (RefereeCommand.DIRECT_FREE_YELLOW, 3.0, "own free kick, defensive third"), - (RefereeCommand.DIRECT_FREE_BLUE, 3.0, "opponent free kick, attacking third"), - (RefereeCommand.DIRECT_FREE_BLUE, 0.0, "opponent free kick, middle third"), - (RefereeCommand.PREPARE_KICKOFF_BLUE, 0.0, "opponent kickoff"), - (RefereeCommand.PREPARE_PENALTY_YELLOW, -3.0, "own penalty"), - (RefereeCommand.FORCE_START, 3.0, "force start"), - ], -) -def test_a_restart_start_is_typed_from_the_candidate_s_view(command, ball_x, situation): - transition = RestartTransition(10.0, RefereeCommand.STOP, command, _kicking_is_yellow(command)) - assert restart_situation(transition, True, SimpleNamespace(ball_x=ball_x)) == situation - - -def test_a_harvested_restart_carries_its_situation(tmp_path): - ticks = [15.0 + k * _TICK for k in range(-2, 4)] - replay_path = _write_replay(tmp_path, "m", ticks, _placement_then_free_kick) - _write_sidecar( - replay_path, - [ - {"event": "referee", "sim_time": 11.0, "command": "BALL_PLACEMENT_YELLOW"}, - {"event": "referee", "sim_time": 15.0, "command": "DIRECT_FREE_YELLOW"}, - ], - ) - - [start] = harvest_replay(replay_path, source_run_id="r", evaluator_version="abc") - - assert start.scenario.ball_x == pytest.approx(1.0) # 3.5 m from the goal the candidate attacks - assert start.provenance.situation == "own free kick, middle third" - - -def test_a_ball_won_start_is_each_ledger_spell_not_taking_a_restart_in_live_play(tmp_path, monkeypatch): - replay = _write_replay(tmp_path, "m", [t / 10 for t in range(60)]) - _write_sidecar( - replay, - [ - {"event": "referee", "sim_time": 0.0, "command": "PREPARE_KICKOFF_YELLOW"}, - {"event": "referee", "sim_time": 1.0, "command": "NORMAL_START"}, - ], - ) - - def spell(t, side, third, takes_restart=False): - return {"t": t, "t_end": t + 0.5, "side": side, "third": third, "takes_restart": takes_restart} - - spells = [ - spell(0.5, "friendly", "middle"), # before play went live - spell(1.2, "friendly", "middle", takes_restart=True), # the kickoff being taken - spell(2.0, "friendly", "attacking"), - spell(3.0, "enemy", "defensive"), - ] - monkeypatch.setattr(scenario_harvester, "match_ledger", lambda path: {"spells": spells}) - - assert harvest_replay(replay, source_run_id="r", evaluator_version="x") == [] # the opening kickoff - starts = harvest_replay(replay, source_run_id="r", evaluator_version="x", ball_won=True) - - assert [(s.provenance.anchor_tick, s.provenance.situation, s.provenance.family) for s in starts] == [ - (2.0, "own ball won, attacking third", ScenarioFamily.OPEN_PLAY_POSSESSION), - (3.0, "opponent ball won, defensive third", ScenarioFamily.OPEN_PLAY_COUNTER), - ] - assert all(s.provenance.trigger == ScenarioTrigger.EVENT for s in starts) - assert starts[0].scenario_id == "m_t2.0_own_ball_won_attacking" - - -def _typed(situation: str, match: str, k: int): - base = list(all_hand_authored_scenarios())[0] - return dataclasses.replace( - base, - scenario_id=f"{match}_{situation}_{k}", - # 1 m apart: no two are near-duplicates - scenario=dataclasses.replace(base.scenario, ball_x=-4.0 + k, ball_y=-2.0 + ord(match[-1]) - ord("a")), - provenance=dataclasses.replace(base.provenance, situation=situation, source_replay=Path(f"{match}.npz")), - ) - - -def test_the_sample_keeps_up_to_n_of_each_situation_spread_over_matches_and_half_as_many_kickoffs(): - starts = [ - _typed(situation, match, k) - for situation in ("own ball won, middle third", "own kickoff") - for match in ("match_a", "match_b", "match_c") - for k in range(5) - ] - - sample = sample_by_situation(starts, 4, seed=0) - - won = [s for s in sample if s.provenance.situation == "own ball won, middle third"] - assert len(won) == 4 - assert {s.provenance.source_replay for s in won} == {Path(f"match_{m}.npz") for m in "abc"} - assert len([s for s in sample if s.provenance.situation == "own kickoff"]) == 2 - assert [s.scenario_id for s in sample_by_situation(starts, 4, seed=0)] == [s.scenario_id for s in sample] - assert [s.scenario_id for s in sample_by_situation(starts, 4, seed=1)] != [s.scenario_id for s in sample] - - -def test_the_sample_skips_a_near_duplicate_of_a_start_already_drawn(): - first = _typed("own kickoff", "match_a", 0) - again = dataclasses.replace( - first, scenario_id="again", provenance=dataclasses.replace(first.provenance, source_replay=Path("match_b.npz")) - ) - - assert len(sample_by_situation([first, again], 40)) == 1 diff --git a/utama_core/tests/scenario_bench/test_scenario_scorer.py b/utama_core/tests/scenario_bench/test_scenario_scorer.py deleted file mode 100644 index 3781856d..00000000 --- a/utama_core/tests/scenario_bench/test_scenario_scorer.py +++ /dev/null @@ -1,143 +0,0 @@ -"""Tests for `utama_core.scenario_bench.scenario_scorer`. - -Runs real headless rsim matches (short horizons) — this module can't be -tested with pure fixtures the way the harvester's transition-detection can, -since scoring means actually ticking a `StrategyRunner` forward. Kept fast -by using short horizons and the cheapest available kernel strategy. -""" - -from __future__ import annotations - -import dataclasses - -from utama_core.engine.match_stats import MatchStats -from utama_core.entities.referee.referee_command import RefereeCommand -from utama_core.scenario_bench.hand_authored_scenarios import ( - all_hand_authored_scenarios, -) -from utama_core.scenario_bench.scenario_scorer import ( - FLICKER_S, - SIGNALS, - ScenarioOutcome, - _classify_outcome, - _RealLossWatch, - score_scenario, - start_signals, -) - - -def _kickoff_scenario(): - return next(bs for bs in all_hand_authored_scenarios() if bs.scenario_id == "kickoff_center_v1") - - -def test_score_scenario_returns_a_result_with_stats(): - bench_scenario = _kickoff_scenario() - result = score_scenario( - bench_scenario, - candidate_config="build_default_kernel_strategy", - opponent_config="build_default_kernel_strategy", - horizon_s=3.0, - stats_path="/tmp/test_score_scenario_stats.json", - ) - - assert result.error is None - assert result.scenario_id == "kickoff_center_v1" - assert isinstance(result.outcome, ScenarioOutcome) - assert result.ticks_run > 0 - assert set(result.signals) == set(SIGNALS) - - -def test_score_scenario_reports_error_on_bad_config(): - bench_scenario = _kickoff_scenario() - result = score_scenario( - bench_scenario, - candidate_config="not_a_real_strategy", - opponent_config="build_default_kernel_strategy", - horizon_s=3.0, - stats_path="/tmp/test_score_scenario_bad_config.json", - ) - - assert result.error is not None - assert result.ticks_run == 0 - - -def _watch(ticks) -> int: - """ticks: (t, command, friendly turnovers so far, possession side)""" - watch = _RealLossWatch() - for t, cmd, turnovers, side in ticks: - watch.step(t, cmd, turnovers, side) - return watch.losses - - -_LIVE_CMD = RefereeCommand.NORMAL_START - - -def test_a_turnover_won_back_within_the_flicker_window_is_not_a_loss(): - """Raw `MatchStats.turnovers` counts two robots on one ball flipping "nearest"; the - bench used to score every such flip as TURNOVER. Only an opponent holding the ball - for more than `FLICKER_S` is a loss.""" - flicker = [ - (0.0, _LIVE_CMD, 0, "friendly"), - (0.5, _LIVE_CMD, 1, "enemy"), - (0.5 + FLICKER_S, _LIVE_CMD, 1, "friendly"), - ] - assert _watch(flicker) == 0 - kept = [(0.0, _LIVE_CMD, 0, "friendly"), (0.5, _LIVE_CMD, 1, "enemy"), (0.51 + FLICKER_S, _LIVE_CMD, 1, "enemy")] - assert _watch(kept) == 1 - - -def test_a_restart_to_the_opponent_is_a_loss_only_if_we_had_the_ball(): - blue_free_kick = RefereeCommand.DIRECT_FREE_BLUE - assert _watch([(0.0, _LIVE_CMD, 0, "friendly"), (0.1, blue_free_kick, 0, "friendly")]) == 1 - assert _watch([(0.0, _LIVE_CMD, 0, "enemy"), (0.1, blue_free_kick, 0, "enemy")]) == 0 - # a turnover while play is stopped is a restart handover, not a loss - stop = RefereeCommand.STOP - assert _watch([(0.0, stop, 0, "friendly"), (0.5, stop, 1, "enemy"), (2.0, stop, 1, "enemy")]) == 0 - - -def test_raw_turnovers_without_a_real_loss_do_not_score_turnover(): - before = MatchStats({}, {}, {}) - after = dataclasses.replace(before, turnovers=4, attacking_third_entries=1) - assert _classify_outcome(before, after, False, False, real_losses=0) == ScenarioOutcome.ENTRY_RETAINED - assert _classify_outcome(before, after, False, False, real_losses=1) == ScenarioOutcome.TURNOVER - - -def test_bench_plays_with_the_round_robins_motion_planner(monkeypatch): - """Banks are harvested from round-robins, which run `match.run_match`'s - control scheme (fpp). The scorer played every start with trajsample instead, so a - bench A/B measured play under another planner, and a planner change made in fpp - left every outcome unchanged.""" - import inspect - - from tools.evaluation import match - from utama_core.scenario_bench import scenario_scorer - - seen = {} - monkeypatch.setattr(scenario_scorer, "StrategyRunner", lambda **kwargs: seen.update(kwargs)) - scenario_scorer._build_runner("press_and_pass", "low_block", stats_path="unused") - - assert seen["control_scheme"] == inspect.signature(match.run_match).parameters["control_scheme"].default - - -def test_start_signals_are_the_candidate_s_side_only(): - shot = {"side": "friendly", "open_goal": 0.5, "shot_after_s": None} - chances = { - "shots": [shot, {**shot, "open_goal": 0.25}, {**shot, "side": "enemy", "open_goal": 0.75}], - "regains": [ - {"side": "friendly", "shot_after_s": 2.0}, - {"side": "friendly", "shot_after_s": None}, - {"side": "enemy", "shot_after_s": 1.0}, - ], - "danger": {"friendly": {"s": 4.5, "spells": 1}, "enemy": {"s": 9.0, "spells": 2}}, - } - - assert start_signals(chances, real_losses=2, entries=1) == { - "shots": 2, - "open_shots": 0.75, - "regains_to_shot": 1, - "entries": 1, - "real_losses": 2, - "shots_faced": 1, - "open_shots_faced": 0.75, - "danger_s": 4.5, - } diff --git a/utama_core/tests/scenario_bench/test_start.py b/utama_core/tests/scenario_bench/test_start.py deleted file mode 100644 index 55493c57..00000000 --- a/utama_core/tests/scenario_bench/test_start.py +++ /dev/null @@ -1,365 +0,0 @@ -"""Tests for `utama_core.scenario_bench.start` (bench scenario schema, -provenance, lifecycle, and the static validity screen). - -See that module's docstring for the design constraints these tests pin: -policy-agnostic field state only, conservative lifecycle default, and a -static screen that catches malformed snapshots before any dynamic (play- -forward) screening or scoring happens. -""" - -from __future__ import annotations - -import dataclasses -import json -from pathlib import Path - -import pytest - -from utama_core.config.field_params import STANDARD_FIELD_DIMS -from utama_core.entities.referee.referee_command import RefereeCommand -from utama_core.replay.scenario import RobotState, Scenario -from utama_core.scenario_bench.start import ( - _DUPLICATE_BALL_M, - _DUPLICATE_ROBOT_M, - _JITTER_POS_M, - BenchScenario, - ScenarioFamily, - ScenarioLifecycle, - ScenarioProvenance, - ScenarioTrigger, - drop_near_duplicates, - is_near_duplicate, - jittered, - load_bank, - matches_where, - save_bank, - start_tags, - static_screen, -) - - -def _rs(id_: int, x: float, y: float, vx: float = 0.0, vy: float = 0.0) -> RobotState: - return RobotState(id=id_, x=x, y=y, orientation=0.0, vx=vx, vy=vy) - - -def _valid_scenario(**overrides) -> Scenario: - defaults = dict( - sim_time=0.0, - ball_x=0.0, - ball_y=0.0, - ball_vx=0.0, - ball_vy=0.0, - friendly_robots=(_rs(0, -4.0, 0.0), _rs(1, -1.0, 0.5)), - enemy_robots=(_rs(0, 4.0, 0.0), _rs(1, 1.0, -0.5)), - referee_command=RefereeCommand.NORMAL_START, - source_replay=Path("test"), - config_a_name=None, - config_b_name=None, - frame_ts=0.0, - ) - defaults.update(overrides) - return Scenario(**defaults) - - -def test_static_screen_accepts_well_formed_scenario(): - result = static_screen(_valid_scenario()) - assert result.ok - assert result.violations == () - - -def test_static_screen_rejects_ball_out_of_bounds(): - result = static_screen(_valid_scenario(ball_x=100.0)) - assert not result.ok - assert any("ball out of bounds" in v for v in result.violations) - - -def test_static_screen_rejects_robot_out_of_bounds(): - result = static_screen(_valid_scenario(friendly_robots=(_rs(0, -4.0, 0.0), _rs(1, 50.0, 0.0)))) - assert not result.ok - assert any("out of bounds" in v for v in result.violations) - - -def test_static_screen_rejects_a_robot_the_sim_cannot_be_teleported_to(): - # the sim refuses to place a robot past the field lines (full_field_bounds), so a - # start with one there fails to set up; just inside is fine - half = STANDARD_FIELD_DIMS.full_field_half_length - inside = static_screen(_valid_scenario(friendly_robots=(_rs(0, -(half - 1e-3), 0.0), _rs(1, -1.0, 0.5)))) - outside = static_screen(_valid_scenario(friendly_robots=(_rs(0, -(half + 1e-3), 0.0), _rs(1, -1.0, 0.5)))) - assert inside.ok - assert not outside.ok and any("out of bounds" in v for v in outside.violations) - - -def test_a_jittered_start_keeps_every_robot_inside_the_field_lines(): - half = STANDARD_FIELD_DIMS.full_field_half_length - base = dataclasses.replace( - _sample_bench_scenario(), - scenario=_valid_scenario(friendly_robots=(_rs(0, -(half - 1e-3), 0.0), _rs(1, -1.0, 0.5))), - ) - for seed in range(1, 30): - sc = jittered(base, seed).scenario - assert all(abs(r.x) <= half for r in (*sc.friendly_robots, *sc.enemy_robots)) - - -def test_static_screen_rejects_overlapping_robots(): - result = static_screen( - _valid_scenario( - friendly_robots=(_rs(0, -4.0, 0.0), _rs(1, 0.0, 0.0)), - enemy_robots=(_rs(0, 4.0, 0.0), _rs(1, 0.001, 0.0)), - ) - ) - assert not result.ok - assert any("overlaps" in v for v in result.violations) - - -def test_static_screen_rejects_implausible_robot_speed(): - result = static_screen(_valid_scenario(friendly_robots=(_rs(0, -4.0, 0.0), _rs(1, -1.0, 0.5, vx=50.0)))) - assert not result.ok - assert any("implausible speed" in v for v in result.violations) - - -def test_static_screen_rejects_missing_team(): - result = static_screen(_valid_scenario(enemy_robots=())) - assert not result.ok - assert any("no enemy robots" in v for v in result.violations) - - -def test_bench_scenario_defaults_to_candidate_lifecycle(): - scenario = _valid_scenario() - provenance = ScenarioProvenance( - source_run_id="hand_authored", - evaluator_version="abc123", - trigger=ScenarioTrigger.HAND_AUTHORED, - family=ScenarioFamily.KICKOFF, - ) - bench_scenario = BenchScenario(scenario_id="test_v1", scenario=scenario, provenance=provenance) - - assert bench_scenario.lifecycle is ScenarioLifecycle.CANDIDATE - assert bench_scenario.to_scenario() is scenario - - -def _sample_bench_scenario(scenario_id: str = "test_v1", **provenance_overrides) -> BenchScenario: - provenance_defaults = dict( - source_run_id="tournament_20260904_221937", - evaluator_version="c7d45a3", - trigger=ScenarioTrigger.RESTART, - family=ScenarioFamily.DIRECT_FREE_ATTACKING, - anchor_tick=42.5, - source_replay=Path("replays/tournament_20260904_221937/a_vs_b.npz"), - perspective="candidate_kicking", - ) - provenance_defaults.update(provenance_overrides) - scenario = _valid_scenario( - sim_time=42.5, - ball_x=-4.0, - ball_y=0.75, - friendly_robots=(_rs(0, -4.2, 0.7), _rs(1, -1.0, 0.5)), - ) - return BenchScenario( - scenario_id=scenario_id, - scenario=scenario, - provenance=ScenarioProvenance(**provenance_defaults), - lifecycle=ScenarioLifecycle.ACTIVE, - lead_in_s=1.5, - ) - - -def test_bench_scenario_to_dict_from_dict_round_trips(): - original = _sample_bench_scenario() - restored = BenchScenario.from_dict(original.to_dict()) - - assert restored.scenario_id == original.scenario_id - assert restored.lifecycle == original.lifecycle - assert restored.lead_in_s == original.lead_in_s - assert restored.provenance == original.provenance - assert restored.scenario.sim_time == original.scenario.sim_time - assert restored.scenario.ball_x == original.scenario.ball_x - assert restored.scenario.ball_y == original.scenario.ball_y - assert restored.scenario.referee_command == original.scenario.referee_command - assert restored.scenario.friendly_robots == original.scenario.friendly_robots - assert restored.scenario.enemy_robots == original.scenario.enemy_robots - assert restored.scenario.config_a_name == original.scenario.config_a_name - assert restored.scenario.config_b_name == original.scenario.config_b_name - - -def test_bench_scenario_to_dict_from_dict_round_trips_none_referee_command(): - base = _sample_bench_scenario(scenario_id="no_referee") - original = dataclasses.replace(base, scenario=dataclasses.replace(base.scenario, referee_command=None)) - - restored = BenchScenario.from_dict(original.to_dict()) - - assert restored.scenario.referee_command is None - - -def test_save_bank_and_load_bank_round_trips(tmp_path): - scenarios = [_sample_bench_scenario(scenario_id="s1"), _sample_bench_scenario(scenario_id="s2", anchor_tick=10.0)] - bank_path = tmp_path / "bank_v1.json" - - save_bank(scenarios, bank_path, bank_id="v1") - bank_id, restored = load_bank(bank_path) - - assert bank_id == "v1" - assert [s.scenario_id for s in restored] == ["s1", "s2"] - assert restored[0].provenance == scenarios[0].provenance - assert restored[1].provenance.anchor_tick == 10.0 - - -def test_load_bank_rejects_mismatched_schema_version(tmp_path): - scenarios = [_sample_bench_scenario()] - bank_path = tmp_path / "bank.json" - save_bank(scenarios, bank_path, bank_id="v1") - - payload = json.loads(bank_path.read_text()) - payload["schema_version"] = 999 - bank_path.write_text(json.dumps(payload)) - - with pytest.raises(ValueError, match="schema_version"): - load_bank(bank_path) - - -def test_save_bank_creates_parent_directories(tmp_path): - scenarios = [_sample_bench_scenario()] - bank_path = tmp_path / "nested" / "dir" / "bank.json" - - save_bank(scenarios, bank_path, bank_id="v1") - - assert bank_path.exists() - - -def test_jittered_is_reproducible_small_and_keeps_the_robot_on_the_ball(): - """rsim is deterministic, so repeats need different starts. Seed 0 is the scenario - as authored; other seeds nudge robots away from the ball by at most - `_JITTER_POS_M` per axis, the same way every time; the robot on the ball stays put.""" - on_ball = _rs(2, 0.09, 0.0) - bs = BenchScenario( - scenario_id="x", - scenario=_valid_scenario(friendly_robots=(_rs(0, -4.0, 0.0), _rs(1, -1.0, 0.5), on_ball)), - provenance=ScenarioProvenance( - source_run_id="hand_authored", - evaluator_version="abc123", - trigger=ScenarioTrigger.HAND_AUTHORED, - family=ScenarioFamily.KICKOFF, - ), - ) - - assert jittered(bs, 0) is bs - a, b = jittered(bs, 1), jittered(bs, 1) - assert a == b - assert a != jittered(bs, 2) - assert static_screen(a.scenario).ok - assert a.scenario.friendly_robots[2] == on_ball - for old, new in zip( - bs.scenario.friendly_robots[:2] + bs.scenario.enemy_robots, - a.scenario.friendly_robots[:2] + a.scenario.enemy_robots, - ): - assert (new.x, new.y) != (old.x, old.y) - assert abs(new.x - old.x) <= _JITTER_POS_M and abs(new.y - old.y) <= _JITTER_POS_M - - -def _at(scenario_id: str, *, ball_dx: float = 0.0, robot_dx: float = 0.0, **provenance_overrides) -> BenchScenario: - """`_sample_bench_scenario` with the ball and friendly robot 1 shifted along x.""" - base = _sample_bench_scenario(scenario_id, **provenance_overrides) - s = base.scenario - friendly = (s.friendly_robots[0], dataclasses.replace(s.friendly_robots[1], x=s.friendly_robots[1].x + robot_dx)) - return dataclasses.replace( - base, scenario=dataclasses.replace(s, ball_x=s.ball_x + ball_dx, friendly_robots=friendly) - ) - - -@pytest.mark.parametrize( - "ball_dx, robot_dx, duplicate", - [ - (0.0, 0.0, True), - (_DUPLICATE_BALL_M - 1e-9, 0.0, True), - (_DUPLICATE_BALL_M + 1e-3, 0.0, False), - (0.0, _DUPLICATE_ROBOT_M - 1e-9, True), - (0.0, _DUPLICATE_ROBOT_M + 1e-3, False), - ], -) -def test_near_duplicate_boundaries(ball_dx, robot_dx, duplicate): - assert is_near_duplicate(_at("a"), _at("b", ball_dx=ball_dx, robot_dx=robot_dx)) is duplicate - - -def test_same_positions_in_a_different_situation_are_not_duplicates(): - assert not is_near_duplicate(_at("a"), _at("b", family=ScenarioFamily.DIRECT_FREE_DEFENDING)) - assert not is_near_duplicate(_at("a"), _at("b", perspective="candidate_defending")) - - -def test_drop_near_duplicates_keeps_the_existing_bank_and_first_of_new(): - existing = [_at("old")] - new = [_at("copy_of_old", robot_dx=0.05), _at("fresh", ball_dx=1.0), _at("copy_of_fresh", ball_dx=1.05)] - - assert [s.scenario_id for s in drop_near_duplicates(new, keep=existing)] == ["fresh"] - - -def test_drop_near_duplicates_renames_a_colliding_id(): - kept = drop_near_duplicates([_at("same_id", ball_dx=1.0)], keep=[_at("same_id")]) - - assert [s.scenario_id for s in kept] == ["same_id_tournament_20260904_221937"] - - -def _tagged(ball_x, ball_y, friendly=(), enemy=(), cmd=RefereeCommand.NORMAL_START): - def robots(points): - return tuple(RobotState(id=i, x=x, y=y, orientation=0.0, vx=0.0, vy=0.0) for i, (x, y) in enumerate(points)) - - scenario = Scenario( - sim_time=0.0, - ball_x=ball_x, - ball_y=ball_y, - ball_vx=0.0, - ball_vy=0.0, - friendly_robots=robots(friendly), - enemy_robots=robots(enemy), - referee_command=cmd, - source_replay=Path("t"), - ) - provenance = ScenarioProvenance( - source_run_id="t", - evaluator_version="t", - trigger=ScenarioTrigger.HAND_AUTHORED, - family=ScenarioFamily.OPEN_PLAY_COUNTER, - anchor_tick=None, - source_replay=None, - ) - return start_tags(BenchScenario(scenario_id="t", scenario=scenario, provenance=provenance)) - - -@pytest.mark.parametrize( - "ball_x, third", - [(1.51, "defensive"), (1.49, "middle"), (-1.49, "middle"), (-1.51, "attacking")], -) -def test_thirds_are_measured_from_the_candidate_s_own_goal_on_the_right(ball_x, third): - assert _tagged(ball_x, 0.0)["third"] == third - - -@pytest.mark.parametrize( - "ball_y, lane", [(-1.01, "left_wing"), (-0.99, "centre"), (0.99, "centre"), (1.01, "right_wing")] -) -def test_left_is_minus_y_for_the_candidate_attacking_minus_x(ball_y, lane): - assert _tagged(0.0, ball_y)["lane"] == lane - - -@pytest.mark.parametrize( - "cmd, restart", - [ - (RefereeCommand.DIRECT_FREE_YELLOW, "ours"), - (RefereeCommand.BALL_PLACEMENT_BLUE, "theirs"), - (RefereeCommand.PREPARE_KICKOFF_BLUE, "theirs"), - (RefereeCommand.STOP, "stop"), - (RefereeCommand.FORCE_START, "live"), - (None, "live"), - ], -) -def test_the_restart_belongs_to_the_colour_the_command_names(cmd, restart): - assert _tagged(0.0, 0.0, cmd=cmd)["restart"] == restart - - -def test_the_ball_is_held_by_the_nearest_robot_within_reach_and_near_counts_both_sides(): - tags = _tagged(0.0, 0.0, friendly=[(0.15, 0.0), (1.5, 0.0), (1.6, 0.0)], enemy=[(0.0, 0.18), (0.0, 2.0)]) - assert (tags["ball"], tags["near"]) == ("ours", "2v1") - assert _tagged(0.0, 0.0, friendly=[(0.21, 0.0)], enemy=[(0.0, -0.3)])["ball"] == "loose" - - -def test_where_takes_any_of_the_listed_values_for_every_tag_given(): - tags = {"third": "defensive", "ball": "loose"} - assert matches_where(tags, {"third": {"defensive"}, "ball": {"theirs", "loose"}}) - assert not matches_where(tags, {"third": {"defensive"}, "ball": {"theirs"}}) diff --git a/utama_core/tests/shared/test_pass_and_score_geometry.py b/utama_core/tests/shared/test_pass_and_score_geometry.py index 5aae2811..54b6bb88 100644 --- a/utama_core/tests/shared/test_pass_and_score_geometry.py +++ b/utama_core/tests/shared/test_pass_and_score_geometry.py @@ -727,6 +727,27 @@ def test_carry_exhausted_counts_the_distance_needed_to_stop(): assert not carry_exhausted(moving, origin) +def test_carry_exhausted_counts_the_reaction_time_before_braking(): + """At 1.2m/s, 0.5m carried: braking alone needs 1.2^2 / (2*2.5) = 0.29m (0.79m in + all, under the 0.8m cap), but the carrier keeps going for the 0.15s reaction time + first (another 0.18m), so the carry is spent. Traced in tournament_20261017_002337: + DecoyOverload's lure kept accelerating ~0.1s after the cap fired at ~1.2m/s, carried + 0.5m more and fouled at 1.0m (93 excessive-dribbling fouls).""" + from types import SimpleNamespace + + from utama_core.entities.data.vector import Vector2D, Vector3D + from utama_core.shared.pass_and_score_geometry import carry_exhausted + + origin = Vector2D(0.0, 0.0) + lure = SimpleNamespace(ball=SimpleNamespace(p=Vector3D(0.5, 0.0, 0.0), v=Vector3D(1.2, 0.0, 0.0))) + slower = SimpleNamespace(ball=SimpleNamespace(p=Vector3D(0.5, 0.0, 0.0), v=Vector3D(0.7, 0.0, 0.0))) + + assert carry_exhausted(lure, origin, stops_after=True) + # 0.5 + 0.7*0.15 + 0.7^2/5 = 0.70m: still room to carry. + assert not carry_exhausted(slower, origin, stops_after=True) + assert not carry_exhausted(lure, origin) # a kick-while-moving carrier is unchanged + + def _rolling_game(receiver_xy: tuple, ball_xy: tuple, ball_v: tuple) -> Game: frame = GameFrame( ts=0.0, @@ -756,6 +777,17 @@ def test_ball_line_receive_point_none_just_below_rolling_speed(): assert ball_line_receive_point(_rolling_game((1.5, 0.0), (0.4, 0.0), (_PASS_ROLLING_MPS, 0.0)), 2) is not None +def test_ball_line_receive_point_stays_on_the_field_when_the_ball_rolls_past_the_goal_line(): + # The ball's path runs out past the enemy goal line (x = half length) beside the corner, and + # the receiver projects onto it past the line. Clamped to the field, with clamp_to_field's margin. + half_length = STANDARD_FIELD_DIMS.full_field_half_length + half_width = STANDARD_FIELD_DIMS.full_field_half_width + spot = ball_line_receive_point(_rolling_game((4.4, 2.6), (3.0, 0.0), (1.0, 1.0)), 2) + assert spot is not None + assert abs(spot.x) <= half_length + assert abs(spot.y) <= half_width + + def test_ball_line_receive_point_none_once_the_ball_has_passed_the_receiver(): assert ball_line_receive_point(_rolling_game((1.5, 0.0), (1.5, 0.1), (0.0, 3.0)), 2) is None assert ball_line_receive_point(_rolling_game((1.5, 0.0), (2.0, 0.0), (3.0, 0.0)), 2) is None diff --git a/utama_core/tests/strategy/test_overload_flow_streak.py b/utama_core/tests/strategy/test_overload_flow_streak.py index 9606cdf8..eb2ef924 100644 --- a/utama_core/tests/strategy/test_overload_flow_streak.py +++ b/utama_core/tests/strategy/test_overload_flow_streak.py @@ -1,7 +1,7 @@ """`overload_flow` adds a 4th give-and-go attacker once its team has held the possession edge for `_POSSESSION_STREAK_TICKS` ticks. The streak was one module-level counter, so two `overload_flow` teams in one process (a mirror match, -a bench A/B against itself) counted each other's possession. +an A/B against itself) counted each other's possession. """ from unittest.mock import MagicMock diff --git a/utama_core/tests/strategy_runner/teleport_position_accuracy_test.py b/utama_core/tests/strategy_runner/teleport_position_accuracy_test.py index 8a4daad9..3ca2bae1 100644 --- a/utama_core/tests/strategy_runner/teleport_position_accuracy_test.py +++ b/utama_core/tests/strategy_runner/teleport_position_accuracy_test.py @@ -107,7 +107,7 @@ def _run_teleport_accuracy_test(robot_id: int, x: float, y: float, theta: float) ) # A teleported heading must read back unchanged: rsim stores y negated, and a # heading passed through without the matching flip came back mirrored (-theta), - # so every bench scenario rebuilt from a replay started with its robots facing + # so every scenario rebuilt from a replay started with its robots facing # the wrong way. heading_error = math.atan2( math.sin(test_manager.first_frame_orientation - theta), math.cos(test_manager.first_frame_orientation - theta) diff --git a/utama_core/tests/tools/test_diagram_names.py b/utama_core/tests/tools/test_diagram_names.py new file mode 100644 index 00000000..8ac9b4f1 --- /dev/null +++ b/utama_core/tests/tools/test_diagram_names.py @@ -0,0 +1,41 @@ +"""Every name a drawn architecture picture shows (`NAMES` in docs/img/*/make_*.py) still exists in the code, so +a rename fails here instead of leaving a picture that is quietly wrong.""" + +import ast +import re +from pathlib import Path + +import pytest + +REPO = Path(__file__).resolve().parents[3] +SCRIPTS = sorted(p for p in (REPO / "docs" / "img").glob("*/make_*.py") if "NAMES" in p.read_text()) +SOURCE = "\n".join(p.read_text() for p in (REPO / "utama_core").rglob("*.py") if "tests" not in p.parts) + + +def names_of(script: Path) -> list[str]: + for node in ast.parse(script.read_text()).body: + if isinstance(node, ast.Assign) and any(getattr(t, "id", None) == "NAMES" for t in node.targets): + return ast.literal_eval(node.value) + raise AssertionError(f"{script} has no module-level NAMES") + + +def exists(name: str) -> bool: + """A path from the repo root, or a class, function or module-level constant defined in utama_core.""" + if "/" in name: + return (REPO / name).exists() + return re.search(rf"^\s*(class|def)\s+{re.escape(name)}\b|^{re.escape(name)}\s*[:=]", SOURCE, re.M) is not None + + +def test_there_are_drawn_pictures_to_check(): + assert any(s.name == "make_one_tick.py" for s in SCRIPTS) + + +@pytest.mark.parametrize("script", SCRIPTS, ids=lambda p: p.parent.name) +def test_every_name_a_picture_shows_exists(script): + missing = [n for n in names_of(script) if not exists(n)] + assert not missing, f"{script.relative_to(REPO)} shows names that no longer exist: {missing}" + + +def test_a_renamed_class_or_moved_path_is_caught(): + assert exists("PositionRefiner") and exists("utama_core/engine/strategy.py") + assert not exists("PositionRefinerOld") and not exists("utama_core/engine/no_such_module.py") diff --git a/utama_core/tests/tools/test_package_graph.py b/utama_core/tests/tools/test_package_graph.py new file mode 100644 index 00000000..85dc806a --- /dev/null +++ b/utama_core/tests/tools/test_package_graph.py @@ -0,0 +1,66 @@ +"""`tools/package_graph.py` on a synthetic `utama_core/` tree: edges, relative imports, skipped tests, the rule.""" + +from collections import Counter +from pathlib import Path + +from tools.package_graph import edge_counts, render_mermaid, scan, violations + + +def _write(root: Path, rel: str, text: str) -> None: + path = root / "utama_core" / rel + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(text, encoding="utf-8") + + +def _tree(root: Path) -> None: + _write( + root, + "engine/strategy.py", + "from utama_core.entities.game import Game\nfrom . import helpers\nfrom utama_core.tests.common import z\n", + ) + _write(root, "engine/helpers.py", "from ..replay import x\n") # relative: engine -> replay + _write(root, "replay/r.py", "import utama_core.analysis.z\n") # the rule's violation + _write(root, "replay/__init__.py", "from utama_core import analysis\n") # `from utama_core import pkg` + _write(root, "analysis/z.py", "x = 1\n") + _write(root, "entities/game.py", "from utama_core.config import c\n") + _write(root, "tests/test_x.py", "from utama_core.run import y\n") # test code is not a source + + +def test_scan_counts_package_edges_and_skips_tests(tmp_path): + _tree(tmp_path) + imports = scan(tmp_path) + assert edge_counts(imports) == Counter( + { + ("engine", "entities"): 1, + ("engine", "tests"): 1, + ("engine", "replay"): 1, + ("replay", "analysis"): 2, + ("entities", "config"): 1, + } + ) + assert all(i.src != "run" for i in imports) # the only `run` import was in tests/ + + +def test_violation_lists_each_offending_import_with_its_location(tmp_path): + _tree(tmp_path) + bad = violations(scan(tmp_path)) + assert sorted((i.file, i.line) for i in bad) == [ + ("utama_core/replay/__init__.py", 1), + ("utama_core/replay/r.py", 1), + ] + assert violations(scan(tmp_path), rules=(("engine", "entities"),))[0].dst == "entities" + + +def test_no_violation_on_a_clean_tree(tmp_path): + _write(tmp_path, "engine/a.py", "from utama_core.replay import r\n") + _write(tmp_path, "replay/r.py", "x = 1\n") + assert violations(scan(tmp_path)) == [] + + +def test_mermaid_has_one_labelled_edge_per_pair(): + text = render_mermaid(Counter({("engine", "entities"): 3, ("replay", "analysis"): 1})) + assert text.splitlines() == [ + "flowchart LR", + ' p_engine["engine"] -->|3| p_entities["entities"]', + ' p_replay["replay"] -->|1| p_analysis["analysis"]', + ] diff --git a/vendor/rSim/CMakeLists.txt b/vendor/rSim/CMakeLists.txt index 7d83eb00..52ffe55e 100644 --- a/vendor/rSim/CMakeLists.txt +++ b/vendor/rSim/CMakeLists.txt @@ -6,7 +6,14 @@ set(CMAKE_CXX_STANDARD 11) if(NOT CMAKE_BUILD_TYPE) set(CMAKE_BUILD_TYPE Release) endif() -set(CMAKE_CXX_FLAGS_RELEASE "-O3 -march=native -DNDEBUG") +# -march=native changes floating-point results (FMA contraction), so a native build plays +# different matches than the installed binary and than other CPUs. Opt in for speed only. +option(RSIM_NATIVE_ARCH "Compile with -march=native" OFF) +if(RSIM_NATIVE_ARCH) + set(CMAKE_CXX_FLAGS_RELEASE "-O3 -march=native -DNDEBUG") +else() + set(CMAKE_CXX_FLAGS_RELEASE "-O3 -DNDEBUG") +endif() # Define CMAKE_INSTALL_xxx: LIBDIR, INCLUDEDIR include(GNUInstallDirs) diff --git a/vendor/rSim/FORK_NOTES.md b/vendor/rSim/FORK_NOTES.md index 153c1be9..54b3dde0 100644 --- a/vendor/rSim/FORK_NOTES.md +++ b/vendor/rSim/FORK_NOTES.md @@ -4,11 +4,14 @@ Upstream: https://github.com/robocin/rSim Forked at commit: `b413932b7b4bbc0fc399f6a979e4cfcc4f56264f` (tag `v1.2`, the same version this repo's `pixi.toml` pins via `rc-robosim>=1.2,<2` on PyPI). +This fork is the physics step of the rsim stack. The diagram of the whole tick, from +`StrategyRunner` through the seeded sim noise to this C++ code and back, is in +[docs/sim_realism.md](../../docs/sim_realism.md#how-one-rsim-tick-is-built). + ## Why this is here -`Utama-Core` depends on `rc-robosim` as a compiled PyPI package (the -`robosim` feature environment in `pixi.toml`), not as source — normally -nothing in this repo needs rSim's own source at all. This copy exists +Since 2026-10-16 `pixi.toml` builds the `robosim` environment's `rc-robosim` from this +directory, not from PyPI (see "Installed from source by pixi" below). This copy first existed because we found and fixed real bugs in rSim's native ball/dribbler/kicker physics (see `docs/roadmap.md`'s "TODO — add grsim as a CI/tournament environment" section, item 1, for the dribbler-release writeup, and the @@ -66,6 +69,38 @@ re-clone upstream and re-apply the diffs by hand first. at all on this machine's GCC 13 (pybind11 2.6.2's vendored headers assume ``/`` are transitively included; forces them via `-include`). May not be needed on other toolchains. +5. Tunable physics for `SimNoise` (2026-10-16, `realism/physics-knobs`). Every default + reproduces the old physics bit for bit (checked: a 30 s split_shape vs tiki_taka match's + replay arrays hash the same as with the installed binary, `00c94df54af6231f`). + - `SSL.set_physics_params(substeps, ball_slide_decel, ball_roll_decel, ball_slide_fraction)` + (`robosim_py.cpp`, `SSLWorld::setPhysicsParams`). Kept across `reset()`; a new `SSL` + object starts at the defaults (5, -1, -1, 5/7). + - **Substeps** — physics steps per `step()`; was a hard-coded 5. At 5 the substep length is + still computed as `timeStep * 0.2`, exactly as before (`timeStep / 5` rounds differently). + - **Two-phase ball friction** (`SSLWorld::applyBallFriction`) — with `ball_roll_decel >= 0` + the constant friction force on the ball (about 0.65 m/s^2) is replaced, while the ball is + on the ground (z <= 1.5 r), by a direct speed decrement each substep: `ball_slide_decel` + from a kick until the speed falls to `ball_slide_fraction` of the post-kick speed, then + `ball_roll_decel`. Below 0.01 m/s the ball stops. The angular velocity is set to pure + rolling each substep, so in this mode the ball carries no spin into collisions. A ball + given speed by anything other than a kick (a teleport, a deflection) rolls at once. + `-1` (default) keeps the old force. Real balls: slide about 0.5 s at 2-4 m/s^2, then roll + at about 0.29 m/s^2 (per-match IQR 0.29-0.33). + - **Kick direction** — `setActions` reads an optional 9th action field, an angle in radians + the kick is rotated by from the robot's facing (`SSLRobot::kick(..., angle)`, counter- + clockwise in rsim's own frame, which is the mirror of ours). An 8-field action, or 0, + kicks exactly as before. Python sends 9 fields only on ticks where some kick has an angle. + - `CMakeLists.txt` — `-march=native` is now opt-in (`-DRSIM_NATIVE_ARCH=ON`). It was added + in #144 and changes floating-point results (FMA contraction): the same source built with + it plays a different match from tick 9 (differences of 1e-8 that grow), and differently + on another CPU. The installed `.pixi/envs/robosim` binary was built without it, so a + plain build now matches it. (The default env's `robosim/_robosim.so`, used only with + `UTAMA_USE_NATIVE_RSIM=1`, was copied from a native build, so native mode does not replay + subprocess-mode matches exactly.) + - **Installed 2026-10-16** into `.pixi/envs/robosim` (plain cmake Release build, no + `-march=native`). Knobs off, a 30 s split_shape vs tiki_taka_plus match's replay hashes the + same as with the binary it replaced (`7c27555e8249abc6`). The replaced binary and this one + are kept in `~/dev/ssl/rsim-backup/` (outside the repo) to go back to. ## Known issue: test fragility, not a robot-behavior bug (root-caused 2026-08-16) @@ -138,16 +173,27 @@ fork relationship more explicit) but decided against it for now: a submodule needs a real pushed remote to point to (no `First-Order-RoboCup-SSL/rSim` fork exists on GitHub), and submodules have real day-to-day friction — not cloned by default, easy to leave uninitialized, need CI awareness — that -isn't worth paying while this fix is unshipped and has an open regression -(see below). This directory isn't referenced by `pixi.toml` or installed -anywhere; it's a parked fix, not a build dependency. +isn't worth paying. Now that `pixi.toml` builds from this fork, a pushed fork with a +submodule or pixi's git-source dependency are the alternatives, if vendoring ever gets in +the way. + +## Installed from source by pixi (2026-10-16) + +`pixi.toml` installs this directory as the robosim environment's `rc-robosim` (a pypi path +dependency), so `pixi install` builds it with the machine's C++ compiler: no hand install, and +CI and every checkout run this fork. Needed for that: `setup.py` pins the version +(`1.2.0+utama`; setuptools_scm cannot read one from a vendored subtree) and `FindODE.cmake` +also looks in `$CONDA_PREFIX`, where the env's `libode` lives. After changing the C++ +source, rebuild by deleting `.pixi/envs/robosim` and running `pixi install -e robosim` (a +cached build may otherwise be reused). Built this way (gcc 13, no `-march=native`), a 30 s split_shape vs +tiki_taka_plus match hashes the same as the hand-installed binary (`7c27555e8249abc6`). +From a fresh clone with empty pixi and uv caches (2026-10-17, gcc 13 from the system, as on CI's +ubuntu runner) `pixi install -e robosim` took 9.5 min, most of it downloads, and the built binary +steps 1800 ticks of 12 robots bit for bit like the installed one, with the knobs off and on. +Matches still differ across CPUs and compilers (see the `-march=native` note above), so +compare results from one machine. -If this fix gets finished and actually adopted (regression resolved, and -`pixi.toml` switched from the PyPI `rc-robosim` dependency to building from -this fork), that's the point to revisit this — either push a real fork to -GitHub and convert this to a submodule, or use pixi's own git-source -dependency support to point directly at that fork instead of vendoring -source at all. +The manual procedure below is kept for trying changes without touching the env. ## How to rebuild @@ -188,6 +234,31 @@ rm -rf _skbuild /path/to/Utama-Core/.pixi/envs/robosim/bin/python -m pip install --force-reinstall --no-deps /tmp/wheelout/rc_robosim-*.whl ``` +## Building into a worktree without installing (for trying changes) + +The rsim subprocess (`robosim_subprocess.py`, run by `.pixi/envs/robosim/bin/python`) inherits +the environment, so a build placed first on `PYTHONPATH` overrides the installed binary for one +process tree only, leaving the shared env alone: + +```bash +cmake -S vendor/rSim -B build/rsim-py310 \ + -DPYTHON_EXECUTABLE=$(pwd)/.pixi/envs/robosim/bin/python -DCMAKE_POLICY_VERSION_MINIMUM=3.5 +cmake --build build/rsim-py310 -j2 +mkdir -p build/rsim-override/robosim +echo "from ._robosim import SSL, VSS" > build/rsim-override/robosim/__init__.py +ln -s $(pwd)/build/rsim-py310/_robosim.cpython-310-x86_64-linux-gnu.so build/rsim-override/robosim/ +PYTHONPATH=$(pwd)/build/rsim-override pixi run pytest utama_core/tests/rsoccer_simulator --headless +``` + +Installing for everyone is then a copy of that `.so` over +`.pixi/envs/robosim/lib/python3.10/site-packages/robosim/_robosim.cpython-310-x86_64-linux-gnu.so` +(or the wheel procedure above), revertable with +`.pixi/envs/robosim/bin/python -m pip install --force-reinstall --no-deps rc-robosim==1.2` +(which restores upstream 1.2, without this fork's earlier fixes; keep a copy of the current +`.so` to go back to it instead). The match cache's code fingerprint includes the installed +binary, so installing one reruns every cached match; with the knobs off a non-native build +plays them identically. + ## Testing tip: `get_state()` is stateful, not idempotent `SSLWorld::getState()` computes robot/ball velocity via finite difference diff --git a/vendor/rSim/FindODE.cmake b/vendor/rSim/FindODE.cmake index 8ba075f7..7fa352a4 100644 --- a/vendor/rSim/FindODE.cmake +++ b/vendor/rSim/FindODE.cmake @@ -3,6 +3,7 @@ find_path( NAMES ode/ode.h HINTS + $ENV{CONDA_PREFIX}/include $ENV{HOME}/include /usr/local/include /usr/include @@ -14,6 +15,7 @@ find_library( NAMES oded debugdll/ode HINTS + $ENV{CONDA_PREFIX}/lib $ENV{HOME}/lib /usr/local/lib /usr/lib @@ -25,6 +27,7 @@ find_library( NAMES ode releasedll/ode HINTS + $ENV{CONDA_PREFIX}/lib $ENV{HOME}/lib /usr/local/lib /usr/lib diff --git a/vendor/rSim/pyproject.toml b/vendor/rSim/pyproject.toml index 5c68af5b..4ddd232d 100644 --- a/vendor/rSim/pyproject.toml +++ b/vendor/rSim/pyproject.toml @@ -1,5 +1,4 @@ [build-system] -requires = ["setuptools", "wheel", "scikit-build", "cmake", "ninja", "setuptools_scm>=6.2"] +requires = ["setuptools", "wheel", "scikit-build", "cmake", "ninja"] build-backend = "setuptools.build_meta" -[tool.setuptools_scm] diff --git a/vendor/rSim/setup.py b/vendor/rSim/setup.py index 1d7919a3..8ad46983 100644 --- a/vendor/rSim/setup.py +++ b/vendor/rSim/setup.py @@ -12,6 +12,7 @@ setup( name="rc-robosim", + version="1.2.0+utama", # this fork; setuptools_scm cannot read a version from a vendored subtree url="https://github.com/robocin/rSim", description="SSL and VSS robot soccer simulator", author="Felipe Martins", diff --git a/vendor/rSim/src/robosim/robosim_py.cpp b/vendor/rSim/src/robosim/robosim_py.cpp index 014bf0a4..959cf22d 100644 --- a/vendor/rSim/src/robosim/robosim_py.cpp +++ b/vendor/rSim/src/robosim/robosim_py.cpp @@ -112,12 +112,29 @@ struct SSL { delete m_world; m_world = new SSLWorld(m_fieldType, m_nRobotsBlue, m_nRobotsYellow, m_timeStep_ms / 1000.0, ballPos, blueRobotsPos, yellowRobotsPos); + applyPhysics(); } std::unordered_map getFieldParams() const { return m_world->getFieldParams(); } + // Kept across reset(), which builds a new world. See SSLWorld::setPhysicsParams. + void setPhysicsParams(int substeps, double ballSlideDecel, double ballRollDecel, double ballSlideFraction) { + if (substeps < 1) throw std::runtime_error("substeps must be at least 1"); + m_substeps = substeps; + m_ballSlideDecel = ballSlideDecel; + m_ballRollDecel = ballRollDecel; + m_ballSlideFraction = ballSlideFraction; + applyPhysics(); + } + + void applyPhysics() const { + m_world->setPhysicsParams(m_substeps, m_ballSlideDecel, m_ballRollDecel, m_ballSlideFraction); + } + SSLWorld *m_world; int m_fieldType, m_nRobotsBlue, m_nRobotsYellow, m_timeStep_ms; + int m_substeps = 5; + double m_ballSlideDecel = -1, m_ballRollDecel = -1, m_ballSlideFraction = 5.0 / 7.0; }; @@ -138,7 +155,9 @@ PYBIND11_MODULE(_robosim, m) { .def("get_state", &SSL::getState) .def("get_state_array", &SSL::getStateArray) .def("reset", &SSL::reset) - .def("get_field_params", &SSL::getFieldParams); + .def("get_field_params", &SSL::getFieldParams) + .def("set_physics_params", &SSL::setPhysicsParams, py::arg("substeps"), py::arg("ball_slide_decel"), + py::arg("ball_roll_decel"), py::arg("ball_slide_fraction")); } diff --git a/vendor/rSim/src/robosim/sslrobot.cpp b/vendor/rSim/src/robosim/sslrobot.cpp index bf716e71..81f33f62 100644 --- a/vendor/rSim/src/robosim/sslrobot.cpp +++ b/vendor/rSim/src/robosim/sslrobot.cpp @@ -232,13 +232,19 @@ void SSLRobot::Kicker::toggleDribbler() this->dribblerOn != this->dribblerOn; } -void SSLRobot::Kicker::kick(dReal kickSpeedX, dReal kickSpeedZ) +bool SSLRobot::Kicker::kick(dReal kickSpeedX, dReal kickSpeedZ, dReal angle) { dReal dx,dy,dz; dReal vx,vy,vz; this->rob->chassis->getBodyDirection(dx,dy,dz); dz = 0; + if (angle != 0) { + const dReal c = cos(angle), s = sin(angle); + const dReal rx = dx * c - dy * s; + dy = dx * s + dy * c; + dx = rx; + } unholdBall(); @@ -275,7 +281,9 @@ void SSLRobot::Kicker::kick(dReal kickSpeedX, dReal kickSpeedZ) this->kickerState = FLAT_KICK; this->kickerCounter = 10; + return true; } + return false; } void SSLRobot::Kicker::holdBall(){ diff --git a/vendor/rSim/src/robosim/sslrobot.h b/vendor/rSim/src/robosim/sslrobot.h index fb850756..e030c911 100644 --- a/vendor/rSim/src/robosim/sslrobot.h +++ b/vendor/rSim/src/robosim/sslrobot.h @@ -67,7 +67,8 @@ class SSLRobot Kicker(SSLRobot* robot); ~Kicker(); void step(); - void kick(dReal kickSpeedX, dReal kickSpeedZ); + // Returns whether the kick fired; angle rotates its direction (radians, 0 = facing). + bool kick(dReal kickSpeedX, dReal kickSpeedZ, dReal angle = 0); void setDribbler(bool dribbler); bool getDribbler(); void toggleDribbler(); diff --git a/vendor/rSim/src/robosim/sslworld.cpp b/vendor/rSim/src/robosim/sslworld.cpp index 45d91c93..33fe71c5 100644 --- a/vendor/rSim/src/robosim/sslworld.cpp +++ b/vendor/rSim/src/robosim/sslworld.cpp @@ -19,6 +19,7 @@ Copyright (C) 2011, Parsian Robotic Center (eew.aut.ac.ir/~parsian/grsim) #include "sslworld.h" #include "sslconfig.h" +#include #include #include #include @@ -274,10 +275,13 @@ void SSLWorld::step(std::vector> actions) robots[k]->step(); } - // Pq ele faz isso 5 vezes? - // - Talvez mais precisao (Ele sempre faz um step de dt*0.2 ) - for (int kk = 0; kk < 5; kk++) + // Upstream always took 5 substeps of dt*0.2; `substeps` keeps that exact arithmetic at 5. + const dReal subDt = (this->substeps == 5) ? this->timeStep * 0.2 : this->timeStep / this->substeps; + for (int kk = 0; kk < this->substeps; kk++) { + if (this->ballRollDecel >= 0) { + applyBallFriction(subDt); + } else { const dReal *ballvel = dBodyGetLinearVel(this->ball->body); // Norma do vetor velocidade da bola dReal ballSpeed = ballvel[0] * ballvel[0] + ballvel[1] * ballvel[1] + ballvel[2] * ballvel[2]; @@ -296,12 +300,48 @@ void SSLWorld::step(std::vector> actions) dBodySetAngularVel(this->ball->body, 0, 0, 0); dBodySetLinearVel(this->ball->body, 0, 0, 0); } + } - this->physics->step(this->timeStep * 0.2, this->fullSpeed); + this->physics->step(subDt, this->fullSpeed); } } +void SSLWorld::setPhysicsParams(int substeps, double ballSlideDecel, double ballRollDecel, double ballSlideFraction) +{ + this->substeps = substeps; + this->ballSlideDecel = ballSlideDecel; + this->ballRollDecel = ballRollDecel; + this->ballSlideFraction = ballSlideFraction; + this->ballSliding = false; +} + +// Two-phase ball friction: the ball's ground speed drops by exactly decel * dt each substep. +// Its spin is set to pure rolling every substep, so ODE's own ball-ground contact friction +// (mu = 1) exerts no force and this is the only thing slowing the ball. A ball in the air +// (a chip) is left alone until it lands. +void SSLWorld::applyBallFriction(dReal dt) +{ + const dReal r = SSLConfig::World().getBallRadius(); + const dReal *pos = dBodyGetPosition(this->ball->body); + const dReal *vel = dBodyGetLinearVel(this->ball->body); + if (pos[2] > r * 1.5) return; + dReal speed = sqrt(vel[0] * vel[0] + vel[1] * vel[1]); + if (speed <= 0.01) { + this->ballSliding = false; + dBodySetAngularVel(this->ball->body, 0, 0, 0); + dBodySetLinearVel(this->ball->body, 0, 0, 0); + return; + } + if (this->ballSliding && speed <= this->ballSlideEndSpeed) this->ballSliding = false; + const dReal decel = this->ballSliding ? this->ballSlideDecel : this->ballRollDecel; + const dReal scale = std::max(0, speed - decel * dt) / speed; + const dReal vx = vel[0] * scale; + const dReal vy = vel[1] * scale; + dBodySetLinearVel(this->ball->body, vx, vy, vel[2]); + dBodySetAngularVel(this->ball->body, -vy / r, vx / r, 0); +} + void SSLWorld::setActions(std::vector> actions) { for (int i = 0; i < this->field.getRobotsCount(); i++) @@ -315,7 +355,13 @@ void SSLWorld::setActions(std::vector> actions) } else this->robots[i]->setDesiredSpeedLocal(rbtAction[1], rbtAction[2], rbtAction[3]); if (rbtAction[5] > 0 || rbtAction[6] > 0) { - this->robots[i]->kicker->kick(rbtAction[5], rbtAction[6]); + // Optional 9th field: the kick's direction error in radians (0 = along the facing). + const double kickAngle = rbtAction.size() > 8 ? rbtAction[8] : 0.0; + if (this->robots[i]->kicker->kick(rbtAction[5], rbtAction[6], kickAngle) && this->ballRollDecel >= 0) { + const dReal *v = dBodyGetLinearVel(this->ball->body); + this->ballSliding = true; + this->ballSlideEndSpeed = this->ballSlideFraction * sqrt(v[0] * v[0] + v[1] * v[1]); + } } this->robots[i]->kicker->setDribbler(rbtAction[7] > 0); } @@ -426,6 +472,7 @@ void SSLWorld::replace(std::vector ballPos, std::vectorball->setBodyPosition(ballPos[0], ballPos[1], zz); dBodySetLinearVel(this->ball->body, ballPos[2], ballPos[3], 0); dBodySetAngularVel(this->ball->body, 0, 0, 0); + this->ballSliding = false; for (uint32_t i = 0; i < this->field.getRobotsBlueCount(); i++) { diff --git a/vendor/rSim/src/robosim/sslworld.h b/vendor/rSim/src/robosim/sslworld.h index 77b0b38c..cfb56059 100644 --- a/vendor/rSim/src/robosim/sslworld.h +++ b/vendor/rSim/src/robosim/sslworld.h @@ -59,6 +59,22 @@ class SSLWorld const std::unordered_map getFieldParams(); const std::vector &getState(); void setActions(std::vector> actions); + + // Tunable physics (Utama fork, see FORK_NOTES.md). The defaults reproduce upstream rSim: + // 5 substeps per step, and the ball slowed by a constant friction force + // (ballFriction * g, about 0.65 m/s^2 in practice). + // With ballRollDecel >= 0 the ball instead loses exactly ballSlideDecel m/s^2 after a kick + // until its speed falls to ballSlideFraction of the kick speed, then ballRollDecel m/s^2. + void setPhysicsParams(int substeps, double ballSlideDecel, double ballRollDecel, double ballSlideFraction); + +private: + int substeps = 5; + double ballSlideDecel = -1; + double ballRollDecel = -1; + double ballSlideFraction = 5.0 / 7.0; + bool ballSliding = false; + double ballSlideEndSpeed = 0; + void applyBallFriction(dReal dt); }; #endif // SSLWorld_H