Dylan
4af4e3411a
T03 — telemetry.dart, format.dart, live_telemetry.dart, plus pure domain models (Trip/Segment/TrackPoint/RideStats) with no persistence dependency, so Drift can map to them in T08 rather than the domain depending on the database. T04 — ride_statistics.dart including ElevationAccumulator, ported structurally faithfully: moving average, reversal hysteresis, gainIncludingPending, and the finish() reconciliation against lastRaw. T07 (early, because T04 forced it) — tool/parity/ drives identical fixtures through the real Kotlin files and the Dart port, then diffs. Result: every value byte-identical, including noisy_gain=38.959594555022136 to the last digit. The sole difference is run_avg_speed, where Kotlin's 32-bit Float widens to double with artefacts Dart's binary64 does not reproduce. Documented, not papered over. That harness settled a real question. The ported elevation test failed at 50.9m against Kotlin's 35m bound, which looked like a porting bug. It was not: Kotlin's and Dart's Random(42) are different streams. On a shared LCG fixture both produce 39.0m -- which would also fail Kotlin's own bound. The native guard passes on seed luck rather than on a property of the algorithm. The Dart test now uses the shared LCG, asserts bit-equality with Kotlin, and sets its bound from measured behaviour (25 seeds spanned 24.7-46.7m). 52 tests passing. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
64 lines
2.2 KiB
Dart
64 lines
2.2 KiB
Dart
/// Pure conversion and serialization logic, free of Flutter and platform types.
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///
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/// Ported from `com.rippr.Telemetry`. The Kotlin original also held process-wide
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/// recording state; that was already deleted in v2 (state lives in the database, because
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/// it must survive process death) and is not resurrected here.
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library;
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import 'dart:convert';
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import '../domain/models.dart';
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const double msToKmhFactor = 3.6;
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double msToKmh(double metersPerSecond) => metersPerSecond * msToKmhFactor;
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/// A parked bike still emits jittering fixes. Anything under this is reported as zero so
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/// "max speed" is not set by GPS noise while the phone sits in a pocket.
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const double speedNoiseFloorKmh = 1.5;
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double sanitizeSpeedKmh(double raw) {
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if (!raw.isFinite || raw < speedNoiseFloorKmh) return 0.0;
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return raw;
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}
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/// Drop fixes too imprecise to be worth storing. 0 means "accuracy unknown".
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bool isUsableFix(double accuracyMeters, {double maxAccuracyMeters = 50.0}) =>
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accuracyMeters <= 0.0 || accuracyMeters <= maxAccuracyMeters;
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String formatDuration(int millis) {
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if (millis <= 0) return '00:00:00';
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final totalSeconds = millis ~/ 1000;
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final h = (totalSeconds ~/ 3600).toString().padLeft(2, '0');
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final m = ((totalSeconds % 3600) ~/ 60).toString().padLeft(2, '0');
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final s = (totalSeconds % 60).toString().padLeft(2, '0');
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return '$h:$m:$s';
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}
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/// Encode a batch of points for the upload endpoint.
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///
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/// The trip and segment ids are written **per point, not per batch**: the unsynced-point
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/// query draws by id and can straddle a segment or, after a discard-and-restart, a trip
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/// boundary. Hoisting them to batch level would silently mislabel points.
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String encodeBatch(String deviceId, List<TrackPoint> points) {
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final array = points
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.map((p) => <String, Object?>{
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'id': p.id,
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'trip_id': p.tripId,
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'segment_id': p.segmentId,
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'ts': p.timestamp,
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'lat': p.latitude,
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'lon': p.longitude,
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'speed_kmh': p.speedKmh,
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'alt_m': p.altitudeM,
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'acc_m': p.accuracyM,
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'bearing': p.bearingDeg,
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})
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.toList(growable: false);
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return jsonEncode(<String, Object?>{
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'device_id': deviceId,
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'points': array,
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});
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}
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