// Author: Simon-Pierre Boucher — contact@spboucher.ai
//
// LTTB (largest-triangle-three-buckets) downsampling: preserves the visual
// shape of a series — endpoints kept exactly, one representative point per
// bucket chosen to maximize the triangle area with its neighbors. Used to
// keep chart data at ~2× pixel width regardless of run length.
import Foundation

enum Downsampler {
    struct XY: Equatable, Sendable {
        var x: Double
        var y: Double
    }

    static func lttb(_ points: [XY], threshold: Int) -> [XY] {
        let n = points.count
        guard threshold >= 3, n > threshold else { return points }

        var sampled: [XY] = []
        sampled.reserveCapacity(threshold)
        sampled.append(points[0])

        let bucketSize = Double(n - 2) / Double(threshold - 2)
        var a = 0 // index of the previously selected point

        for i in 0..<(threshold - 2) {
            // Average of the NEXT bucket is the third triangle vertex.
            let nextStart = Int(Double(i + 1) * bucketSize) + 1
            let nextEnd = min(Int(Double(i + 2) * bucketSize) + 1, n)
            var avgX = 0.0, avgY = 0.0
            let span = max(nextEnd - nextStart, 1)
            for j in nextStart..<max(nextEnd, nextStart + 1) where j < n {
                avgX += points[j].x
                avgY += points[j].y
            }
            avgX /= Double(span)
            avgY /= Double(span)

            let start = Int(Double(i) * bucketSize) + 1
            let end = min(Int(Double(i + 1) * bucketSize) + 1, n - 1)

            var maxArea = -1.0
            var chosen = start
            let pa = points[a]
            for j in start..<max(end, start + 1) {
                let area = abs((pa.x - avgX) * (points[j].y - pa.y)
                    - (pa.x - points[j].x) * (avgY - pa.y))
                if area > maxArea {
                    maxArea = area
                    chosen = j
                }
            }
            sampled.append(points[chosen])
            a = chosen
        }
        sampled.append(points[n - 1])
        return sampled
    }
}