import org.openrndr.application
import org.openrndr.color.ColorRGBa
import org.openrndr.extra.kdtree.kdTree
import org.openrndr.math.Vector2
import org.openrndr.shape.LineSegment

/**
 * This demo initializes an interactive graphical application where 1000 randomly distributed points
 * are displayed on a 2D canvas. A KD-tree structure is used for spatial querying of the points, enabling
 * efficient nearest-neighbor searches based on the user's cursor position. The closest 7 points to the
 * cursor are highlighted with circles and lines connecting them to the cursor.
 *
 * Key features:
 * - Generates 1000 random 2D points within the canvas.
 * - Builds a KD-tree from the list of points for optimized spatial querying.
 * - Visualizes the points and highlights the 7 nearest neighbors to the user's cursor position dynamically.
 * - Highlights include red-colored circles around the nearest points and red lines connecting them to the cursor.
 */
fun main() = application {
    configure {
        width = 720
        height = 720
    }

    program {
        val points = MutableList(1000) {
            Vector2(Math.random() * width, Math.random() * height)
        }
        val tree = points.kdTree()

        extend {
            drawer.circles(points, 5.0)

            val kNearest = tree.findKNearest(mouse.position, k = 7)
            drawer.fill = ColorRGBa.RED
            drawer.stroke = ColorRGBa.RED
            drawer.strokeWeight = 2.0
            drawer.circles(kNearest, 7.0)
            drawer.lineSegments(kNearest.map { LineSegment(mouse.position, it) })
        }
    }
}