974528d5cf
Gun evaluation previously required a full end-to-end battle (Java server + battle runner + websocket IPC to 2 bot processes, 50 rounds, ~3.4 min) and yielded only ~300-900 REAL shots across 13 guns -- far too few to rank guns, which is why tuning needed many repetitions. VirtualTracker is already a pure function of (WorldState stream, gun list); the only reason it needed Java was where WorldState came from. So the range replays a seq[WorldState] through the SAME tracker: offline and online scores are the same metric by construction, not an approximation. ACCEPTANCE TEST (the point of the whole thing): record one live round, replay it offline, compare per-gun virtual hit rates. 12/12 deterministic guns match EXACTLY, reproduced twice. Tsetlin is compared separately because tmLearnOne calls rand(). Getting to 12/12 exposed two real ordering quirks in the live loop: run() calls go() before the aim/fire block, so tickBullets resolves against the NEXT tick's scan while the prediction used the previous one; and if the target dies during that go() the final tick's spawn+resolution is skipped entirely. The recorder emits an end marker for the second case. The 5th (selected-gun) predict call was verified to be a no-op. Measured cost: 8 fixtures (1770 ticks, ~92k virtual bullets, 13 guns) replay in 2.9 s, ~32k virtual bullets/s -- roughly 70x faster and 100x more samples than a live gauntlet. Also adds a per-tick WorldState recorder behind const RecordWorldState (default off, mirrors the ShotLog idiom) which records the state the bot ACTUALLY builds, staleness included, rather than true positions -- recording the latter would hand the guns perfect information and produce flattering scores. 9 new guard checks (33 total, all passing), including fixture round-trip, replay determinism, stationary->HeadOn 100%, constant-velocity->Linear>HeadOn, and the energy-threshold turner crossing at t=41.
380 lines
16 KiB
Nim
380 lines
16 KiB
Nim
## First guard tests for the gun selector + the speed-sensitivity checks for the
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## per-tick caching bug class.
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##
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## Headless: no Java, no server, no battle. Run with plain
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## nim c -r common_libs/tests/test_gun_harness.nim
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##
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## Selection tests seed the tracker's exported fitness windows directly instead of
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## dragging virtual bullets through spawnBullets/tickBullets. That is deliberate:
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## it makes exact hit-rates (and therefore tie/rng/floor behaviour) deterministic
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## and fast. The spawn/tick pipeline itself is exercised by the droppedBullets
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## test below and by the full gauntlet.
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import std/[math, random, tables, os]
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import gun_harness/gun_interface
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import gun_harness/virtual_bullets
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import gun_harness/selector
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import gun_harness/offline_range
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import guns/stop_shot
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import guns/displacement
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import guns/averaged_lead
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import guns/pattern_matcher
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import guns/head_on
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import guns/linear
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var failures = 0
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proc check(name: string, ok: bool) =
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if ok:
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echo "PASS: ", name
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else:
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echo "FAIL: ", name
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inc failures
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proc recordHit(fw: var FitnessWindow, hit: bool) =
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fw.hits[fw.head] = hit
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fw.head = (fw.head + 1) mod WindowSize
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inc fw.count
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proc seedWindow(t: var VirtualTracker, targetId, gunId, binIdx, hits, misses: int) =
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## Narrowly-scoped test helper: write `hits`/`misses` samples straight into a
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## gun×bin fitness window (fields are exported by virtual_bullets).
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if targetId notin t.fitness:
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t.fitness[targetId] = newSeq[GunFitness](t.numGuns)
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var fw = addr t.fitness[targetId][gunId].bins[binIdx]
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for _ in 0..<hits: recordHit(fw[], true)
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for _ in 0..<misses: recordHit(fw[], false)
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proc ws(tick: int, ex, ey, espeed, eheading: float): WorldState =
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WorldState(selfX: 100.0, selfY: 100.0, enemyX: ex, enemyY: ey,
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enemySpeed: espeed, enemyHeading: eheading,
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arenaWidth: 1000.0, arenaHeight: 1000.0, tick: tick)
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proc pointsDiffer(a, b: GunPrediction): bool =
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abs(a.x - b.x) > 0.5 or abs(a.y - b.y) > 0.5
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# ── selector guards ──────────────────────────────────────────────────────────
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proc testColdBestGun() =
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var t = initTracker(3)
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check "bestGun on a cold tracker returns 0 (HeadOn)", t.bestGun(-1) == 0
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proc testRandomTiebreak() =
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# Two guns with an identical, well-observed hit rate: the tiebreak must expose
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# both ids. Before the random tiebreak landed this always returned index 0.
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var t = initTracker(2)
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seedWindow(t, 7, gunId = 0, binIdx = 0, hits = 50, misses = 0)
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seedWindow(t, 7, gunId = 1, binIdx = 0, hits = 50, misses = 0)
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var seen: array[2, bool]
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for _ in 0..<500:
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let g = t.bestGun(-1)
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if g >= 0 and g < 2: seen[g] = true
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check "random tiebreak returns BOTH tied gun ids (no index-0 determinism)",
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seen[0] and seen[1]
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proc testBestGunDeterministicWinner() =
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# gun 2 clearly best and past MinObsBeforeCompete; must win every call.
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var t = initTracker(3)
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seedWindow(t, 7, gunId = 0, binIdx = 0, hits = 25, misses = 25) # 50 obs, 50%
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seedWindow(t, 7, gunId = 1, binIdx = 0, hits = 0, misses = 0) # cold, skipped
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seedWindow(t, 7, gunId = 2, binIdx = 0, hits = 50, misses = 0) # 50 obs, 100%
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var allTwo = true
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for _ in 0..<100:
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if t.bestGun(-1) != 2: allTwo = false
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check "gun with clearly best rate and >= MinObsBeforeCompete wins deterministically",
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allTwo
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proc testBestPowerCold() =
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var t = initTracker(3)
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let (bin, power) = t.bestPower(0, -1)
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check "bestPower on a zero-observation gun returns bin 0 / power 1.0",
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bin == 0 and power == 1.0
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proc testBestPowerWarmBin3() =
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var t = initTracker(3)
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seedWindow(t, 7, gunId = 0, binIdx = 3, hits = 50, misses = 0) # 100% >= MinHitRate
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let (bin, power) = t.bestPower(0, -1)
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check "bestPower on a warm gun whose bin 3 rate >= MinHitRate returns bin 3",
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bin == 3 and power == 3.0
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proc testFitnessForDeterministic() =
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# Same per-enemy data inserted in opposite orders must aggregate identically.
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# Before fitnessFor sorted enemy ids, std/tables hash order leaked in.
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var t1 = initTracker(2)
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seedWindow(t1, 5, gunId = 0, binIdx = 0, hits = 10, misses = 5)
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seedWindow(t1, 3, gunId = 0, binIdx = 0, hits = 5, misses = 10)
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var t2 = initTracker(2)
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seedWindow(t2, 3, gunId = 0, binIdx = 0, hits = 5, misses = 10)
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seedWindow(t2, 5, gunId = 0, binIdx = 0, hits = 10, misses = 5)
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var same = true
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for _ in 0..<20:
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let r1 = t1.fitnessFor(-1)[0].bins[0].hitRate()
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let r2 = t2.fitnessFor(-1)[0].bins[0].hitRate()
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if r1 != r2: same = false
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let expected = 15.0 / 30.0
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check "fitnessFor is deterministic across insertion orders",
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same and abs(t1.fitnessFor(-1)[0].bins[0].hitRate() - expected) < 1e-12
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proc testDroppedBullets() =
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var t = initTracker(1)
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let state = ws(0, 500.0, 500.0, 0.0, 0.0)
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let preds = [GunPrediction(x: 500.0, y: 500.0),
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GunPrediction(x: 500.0, y: 500.0),
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GunPrediction(x: 500.0, y: 500.0),
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GunPrediction(x: 500.0, y: 500.0)]
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# Fill the ring exactly (4 bullets per spawn, no tickBullets -> never resolve).
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for _ in 0..<(MaxBullets div len(PowerBins)):
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t.spawnBullets(0, preds, state, 5)
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check "droppedBullets stays 0 until the ring wraps", t.droppedBullets == 0
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t.spawnBullets(0, preds, state, 5)
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check "droppedBullets counts unresolved bullets clobbered by the ring",
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t.droppedBullets == 4
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# ── caching-bug speed sensitivity (Task 5) ───────────────────────────────────
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proc testStopShotSpeedSensitivity() =
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let spd0 = bulletSpeed(PowerBins[0])
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let spd3 = bulletSpeed(PowerBins[3])
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var ss = initStopShotGun()
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# Constant speed 4: warm two frames, then compare on the same tick. Before the
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# fix the tick-only cache returned bin 0's lead for every bin.
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discard ss.predict(ws(1, 400.0, 100.0, 4.0, 0.0), spd0)
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discard ss.predict(ws(2, 400.0, 100.0, 4.0, 0.0), spd0)
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let s3 = ws(3, 400.0, 100.0, 4.0, 0.0)
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let p0 = ss.predict(s3, spd0)
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let p3 = ss.predict(s3, spd3)
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check "stop_shot: same tick, different bulletSpeed -> different point",
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pointsDiffer(p0, p3)
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# Task 1a: deceleration is actually detected (8 -> 4 px/tick). The old ordering
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# made prev == speed, so this branch was unreachable and the gun was Linear.
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var ss2 = initStopShotGun()
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discard ss2.predict(ws(1, 400.0, 100.0, 8.0, 0.0), spd0)
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let pd = ss2.predict(ws(2, 400.0, 100.0, 4.0, 0.0), spd0)
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# Stop point is 400 + 4 + 2 = 406 px (BrakeDecel=2); linear lead would be ~470.
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check "stop_shot: deceleration branch reaches the simulated stop point",
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abs(pd.x - 406.0) < 1.0
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proc testDisplacementSpeedSensitivity() =
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let spd0 = bulletSpeed(PowerBins[0])
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let spd3 = bulletSpeed(PowerBins[3])
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var dg = initDisplacementGun()
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# Warm 16 ticks emulating the real harness: 4 predict() calls (one per power
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# bin) on every tick. Feed 16 ticks of constant +5 px/tick motion so the
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# 15-tick window is ready.
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for tick in 1..16:
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for bin in 0..<len(PowerBins):
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discard dg.predict(ws(tick, 300.0 + 5.0 * tick.float, 200.0, 5.0, 0.0),
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bulletSpeed(PowerBins[bin]))
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let s17 = ws(17, 300.0 + 5.0 * 17.0, 200.0, 5.0, 0.0)
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let d0 = dg.predict(s17, spd0)
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let d3 = dg.predict(s17, spd3)
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check "displacement: same tick, different bulletSpeed -> different point",
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pointsDiffer(d0, d3)
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# The real displacement bug: the speed-in-key cache advanced the ring ~4x per
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# tick, so the nominal 15-tick window spanned ~4 ticks. Sampling once per tick
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# means 4 calls/tick must be identical to 1 call/tick.
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var dgMulti = initDisplacementGun()
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var dgOnce = initDisplacementGun()
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for tick in 1..16:
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let s = ws(tick, 300.0 + 5.0 * tick.float, 200.0, 5.0, 0.0)
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for bin in 0..<len(PowerBins):
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discard dgMulti.predict(s, bulletSpeed(PowerBins[bin]))
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discard dgOnce.predict(s, spd0)
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let s18 = ws(18, 300.0 + 5.0 * 18.0, 200.0, 5.0, 0.0)
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let a = dgMulti.predict(s18, spd0)
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let b = dgOnce.predict(s18, spd0)
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check "displacement: ring advances exactly once per tick (4 calls == 1 call)",
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not pointsDiffer(a, b)
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proc testAveragedLeadSpeedSensitivity() =
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let spd0 = bulletSpeed(PowerBins[0])
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let spd3 = bulletSpeed(PowerBins[3])
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var al = initAveragedLeadGun()
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discard al.predict(ws(1, 400.0, 100.0, 3.0, 0.0), spd0) # warm circular's omega
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let s2 = ws(2, 400.0, 100.0, 3.0, 0.0)
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let a0 = al.predict(s2, spd0)
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let a3 = al.predict(s2, spd3)
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check "averaged_lead: same tick, different bulletSpeed -> different point",
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pointsDiffer(a0, a3)
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proc testPatternMatcherSpeedSensitivity() =
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let spd0 = bulletSpeed(PowerBins[0])
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let spd3 = bulletSpeed(PowerBins[3])
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var pm = PatternMatcherGun()
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for tick in 1..25:
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discard pm.predict(ws(tick, 300.0 + 5.0 * tick.float, 200.0, 5.0, 0.0), spd0)
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let s26 = ws(26, 300.0 + 5.0 * 26.0, 200.0, 5.0, 0.0)
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let m0 = pm.predict(s26, spd0)
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let m3 = pm.predict(s26, spd3)
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check "pattern_matcher: same tick, different bulletSpeed -> different point",
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pointsDiffer(m0, m3)
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# ── range-aware firing gate (Task B) ────────────────────────────────────────
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proc testToleranceStrictlyDecreases() =
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# In the unclamped band the tolerance must fall monotonically with distance.
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# 100..10000 px sits strictly inside [floor, ceiling] for the shipped
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# SafetyFactor, so clamping cannot mask a flat or rising curve.
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let dists = [100.0, 200.0, 400.0, 800.0, 1600.0, 3200.0, 10000.0]
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var ok = true
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for i in 0..<dists.high:
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if aimToleranceDeg(dists[i]) <= aimToleranceDeg(dists[i+1]): ok = false
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check "range gate: tolerance strictly decreases as distance grows (unclamped)",
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ok
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proc testToleranceClamps() =
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let near = aimToleranceDeg(1.0) # point blank -> ceiling
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let far = aimToleranceDeg(1.0e9) # effectively infinite -> floor
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let mid = aimToleranceDeg(500.0)
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check "range gate: point-blank clamps to MaxAimThresholdDeg",
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near == MaxAimThresholdDeg
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check "range gate: extreme range clamps to MinAimThresholdDeg",
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far == MinAimThresholdDeg
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check "range gate: mid-range tolerance is strictly inside the clamps",
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mid > MinAimThresholdDeg and mid < MaxAimThresholdDeg
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proc testToleranceFormula() =
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let d = 500.0
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let expected = radToDeg(arctan(BotRadius * SafetyFactor / d))
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check "range gate: tolerance matches radToDeg(arctan(BotRadius*SF/dist))",
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abs(aimToleranceDeg(d) - expected) < 1e-9
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proc testPerfectAlignmentAlwaysFires() =
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var allPass = true
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for d in [0.0, 1.0, 100.0, 1000.0, 1.0e9]:
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if not shouldFire(100.0, 100.0, 0.0, d): allPass = false
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check "range gate: a perfectly aligned cool gun passes at every distance",
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allPass
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proc testGrossMisalignmentFailsLongRange() =
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# 5 deg is far outside the ~0.8 deg cone at 800 px.
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check "range gate: gross misalignment fails at long range",
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not shouldFire(100.0, 105.0, 0.0, 800.0)
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proc testDegenerateDistance() =
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let t = aimToleranceDeg(0.0)
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check "range gate: distPx = 0 falls back to the ceiling",
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t == MaxAimThresholdDeg
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check "range gate: distPx = 0 yields a finite, non-NaN tolerance",
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t == t and t < Inf and t > -Inf
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check "range gate: NaN distance also falls back to the ceiling",
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aimToleranceDeg(NaN) == MaxAimThresholdDeg
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# ── offline range (Task 5) ────────────────────────────────────────────────────
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proc testFixtureRoundTrip() =
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let fx = synthesizeConstantVelocity(ticks = 40)
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let path = getTempDir() / "gun_range_roundtrip.jsonl"
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saveFixture(path, fx)
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let back = loadFixture(path)
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check "range: fixture round-trip preserves tick count",
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back.states.len == fx.states.len
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var same = back.states.len == fx.states.len
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for i in 0..<min(back.states.len, fx.states.len):
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let a = fx.states[i]
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let b = back.states[i]
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if a.tick != b.tick or abs(a.enemyX - b.enemyX) > 1e-9 or
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abs(a.enemyY - b.enemyY) > 1e-9 or
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abs(a.enemyHeading - b.enemyHeading) > 1e-9 or
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abs(a.enemySpeed - b.enemySpeed) > 1e-9 or
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abs(a.selfX - b.selfX) > 1e-9:
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same = false
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check "range: fixture round-trip preserves per-tick fields", same
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check "range: fixture round-trip defaults arena to 800x600",
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back.meta.arenaW == 800.0 and back.meta.arenaH == 600.0
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check "range: fixture round-trip preserves enemy id", back.enemyId == fx.enemyId
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removeFile(path)
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proc testFixtureEndMarkerRoundTrip() =
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let fx = synthesizeStationary(ticks = 10)
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var withEnd = fx
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withEnd.enemyDied = true
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let path = getTempDir() / "gun_range_end.jsonl"
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saveFixture(path, withEnd)
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let back = loadFixture(path)
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check "range: fixture round-trip preserves the enemyDied end marker", back.enemyDied
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removeFile(path)
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proc testReplayDeterminism() =
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let fx = synthesizeCircular(ticks = 80)
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let r1 = replayFixture(fx, @[makeDriver("HeadOn", HeadOnGun()),
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makeDriver("Linear", LinearGun())])
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let r2 = replayFixture(fx, @[makeDriver("HeadOn", HeadOnGun()),
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makeDriver("Linear", LinearGun())])
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var same = r1.len == r2.len
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for i in 0..<r1.len:
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if r1[i].name != r2[i].name or r1[i].shots != r2[i].shots or
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r1[i].hits != r2[i].hits:
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same = false
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for b in 0..<len(PowerBins):
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if r1[i].bins[b].shots != r2[i].bins[b].shots or
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r1[i].bins[b].hits != r2[i].bins[b].hits:
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same = false
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check "range: replaying the same fixture twice is byte-identical (deterministic guns)",
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same
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proc testRangeGroundTruthStationary() =
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let fx = synthesizeStationary(ticks = 120)
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let r = replayFixture(fx, @[makeDriver("HeadOn", HeadOnGun())])
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# The last bullets are still in flight when the fixture ends, so shots < 480;
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# every resolved shot must still be a hit.
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check "range: stationary enemy -> HeadOn scores 100%",
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r[0].shots > 300 and r[0].hits == r[0].shots
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proc testRangeConstantVelocityLinearWins() =
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let fx = synthesizeConstantVelocity(ticks = 120)
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let r = replayFixture(fx, @[makeDriver("HeadOn", HeadOnGun()),
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makeDriver("Linear", LinearGun())])
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check "range: constant velocity -> Linear beats HeadOn",
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r[1].hitRate() > r[0].hitRate()
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proc testEnergyThresholdFixtureRule() =
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# RULE e(t) = max(5, 50 - 0.5*t); e drops below 30 at t = 41.
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let fx = synthesizeEnergyThresholdTurner(ticks = 100, e0 = 50.0,
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decay = 0.5, threshold = 30.0)
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let idx = 41
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check "range: energy-threshold fixture crosses the rule threshold at t=41",
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fx.states[idx].enemyEnergy < 30.0 and
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fx.states[idx-1].enemyEnergy >= 30.0
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# ── driver ───────────────────────────────────────────────────────────────────
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randomize()
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testColdBestGun()
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testRandomTiebreak()
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testBestGunDeterministicWinner()
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testBestPowerCold()
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testBestPowerWarmBin3()
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testFitnessForDeterministic()
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testDroppedBullets()
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testStopShotSpeedSensitivity()
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testDisplacementSpeedSensitivity()
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testAveragedLeadSpeedSensitivity()
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testPatternMatcherSpeedSensitivity()
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testToleranceStrictlyDecreases()
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testToleranceClamps()
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testToleranceFormula()
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testPerfectAlignmentAlwaysFires()
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testGrossMisalignmentFailsLongRange()
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testDegenerateDistance()
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testFixtureRoundTrip()
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testFixtureEndMarkerRoundTrip()
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testReplayDeterminism()
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testRangeGroundTruthStationary()
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testRangeConstantVelocityLinearWins()
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testEnergyThresholdFixtureRule()
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if failures > 0:
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echo "\n", failures, " check(s) FAILED"
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quit(1)
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echo "\nAll gun-harness checks passed."
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