import com.rippr.data.TrackPoint
import com.rippr.data.Segment
import com.rippr.geo.Geo
import com.rippr.geo.LatLon
import com.rippr.stats.RideStatistics

// A deterministic LCG implemented identically in Kotlin and Dart, so both languages
// see the SAME noise sequence. Neither language's built-in Random can do this.
class Lcg(private var s: Long) {
    fun nextDouble(): Double {
        s = s * 6364136223846793005L + 1442695040888963407L
        val bits = (s ushr 11) and ((1L shl 53) - 1L)
        return bits.toDouble() / (1L shl 53).toDouble()
    }
}

fun p(seg: Long, ts: Long, lat: Double, lon: Double, sp: Float, alt: Double, id: Long) =
    TrackPoint(id = id, tripId = 1, segmentId = seg, timestamp = ts,
        latitude = lat, longitude = lon, speedKmh = sp, altitudeM = alt)

fun main() {
    fun out(k: String, v: Any) = println("$k=$v")

    out("haversine_calgary_edmonton", Geo.haversineMeters(51.0447, -114.0719, 53.5461, -113.4938))
    out("haversine_short_hop", Geo.haversineMeters(51.0447, -114.0719, 51.04480, -114.0719))
    out("perp_beyond_end", Geo.perpendicularDistanceMeters(
        LatLon(51.003, -114.0), LatLon(51.000, -114.0), LatLon(51.002, -114.0)))

    val ride = (0 until 21_600).map { LatLon(51.0 + it * 0.00001, -114.0 + Math.sin(it / 100.0) * 0.001) }
    val simplified = Geo.simplify(ride, 5.0)
    out("simplify_count", simplified.size)
    out("simplify_last_lat", simplified.last().lat)
    out("path_length_ride", Geo.pathLengthMeters(ride))

    // The disputed fixture: 600 stationary fixes with shared LCG +/-8 m altitude noise.
    val rng = Lcg(42L)
    val noisy = (0 until 600).map {
        p(1, it * 1000L, 51.0, -114.0, 0f, 1000.0 + (rng.nextDouble() * 16.0 - 8.0), (it + 1).toLong())
    }
    out("noisy_gain", RideStatistics.compute(noisy).elevationGainM)
    out("noisy_loss", RideStatistics.compute(noisy).elevationLossM)

    val climb = (0 until 101).map { p(1, it * 1000L, 51.0 + it * 0.0001, -114.0, 50f, 1000.0 + it, (it + 1).toLong()) }
    out("climb_gain", RideStatistics.compute(climb).elevationGainM)

    val run = (0 until 100).map { p(1, it * 1000L, 51.0 + it * 0.0001, -114.0, 40f, 1000.0, (it + 1).toLong()) }
    val s = RideStatistics.compute(run)
    out("run_distance", s.distanceM)
    out("run_moving_millis", s.movingMillis)
    out("run_avg_speed", s.avgMovingSpeedKmh)

    val prof = RideStatistics.elevationProfile(run, 200)
    out("profile_size", prof.size)
    out("profile_last_distance", prof.last().distanceM)

    val hist = RideStatistics.speedHistogram(run, 10)
    out("hist_buckets", hist.size)
    out("hist_first_millis", hist.first().millis)
}