{
  "version": 3,
  "sources": ["../../src/clustering/replay.ts"],
  "sourcesContent": ["import type { ClusterNode, LeafInput, LeafScreenOffsets, MstEdge, RawMergeEvent } from './types'\n\nexport const D_FLOOR = 1e-9\n\nexport function cappedReplay(\n\tleaves: readonly LeafInput[],\n\tedges: readonly MstEdge[],\n\topts: { Tc: number; Dmax: number },\n\t// Render offsets for markers that draw off their anchor (imprecise pins). Omitted or empty,\n\t// every code path below is the offset-unaware original \u2014 pricing, events, and floats alike.\n\tscreenOffsets?: LeafScreenOffsets\n): RawMergeEvent[] {\n\tvalidateOptions(opts)\n\n\tif (edges.length === 0) return []\n\n\tconst clusters = new ClusterState(leaves, screenOffsets)\n\tconst heap = new EdgeMaxHeap(edges, leaves)\n\t// Edges incident to each current cluster root, for eager repricing: a merge\n\t// moves the result's centroid, which can RAISE an incident edge's key (the\n\t// centroid may move toward a neighbor), so the lazy pop-time recheck alone\n\t// is not enough \u2014 every price change pushes a fresh heap entry.\n\tconst incident: number[][] = leaves.map(() => [])\n\tfor (let i = 0; i < edges.length; i++) {\n\t\theap.push({ edgeIndex: i, z: zForEdge(edges[i], clusters, opts) })\n\t\tincident[edges[i].a].push(i)\n\t\tincident[edges[i].b].push(i)\n\t}\n\n\tconst events: RawMergeEvent[] = []\n\t// Emitted thresholds are clamped non-increasing: with centroid pricing a\n\t// later merge's raw price can in principle exceed an earlier one's, and the\n\t// table's descending sort is load-bearing for the runtime cursor.\n\tlet lastZ = Number.POSITIVE_INFINITY\n\twhile (events.length < edges.length) {\n\t\tconst entry = heap.pop()\n\t\tif (!entry) break\n\n\t\tconst edge = edges[entry.edgeIndex]\n\t\tconst aRoot = clusters.find(edge.a)\n\t\tconst bRoot = clusters.find(edge.b)\n\t\tif (aRoot === bRoot) continue // stale duplicate of an already-fired edge\n\n\t\tconst current = { edgeIndex: entry.edgeIndex, z: zForRoots(edge, aRoot, bRoot, clusters, opts) }\n\t\tconst next = heap.peek()\n\t\tif (next && heap.higherPriority(next, current)) {\n\t\t\theap.push(current)\n\t\t\tcontinue\n\t\t}\n\n\t\tconst z = Math.min(current.z, lastZ)\n\t\tlastZ = z\n\t\tevents.push(clusters.merge(aRoot, bRoot, z))\n\n\t\t// eager reprice: the new cluster's centroid and bbox changed, so every\n\t\t// surviving incident edge gets a fresh entry at its current price\n\t\tconst root = clusters.find(edge.a)\n\t\tconst other = root === aRoot ? bRoot : aRoot\n\t\tconst survivors: number[] = []\n\t\tfor (const idx of [...incident[root], ...incident[other]]) {\n\t\t\tconst e2 = edges[idx]\n\t\t\tconst ra = clusters.find(e2.a)\n\t\t\tconst rb = clusters.find(e2.b)\n\t\t\tif (ra === rb) continue\n\t\t\tsurvivors.push(idx)\n\t\t\theap.push({ edgeIndex: idx, z: zForRoots(e2, ra, rb, clusters, opts) })\n\t\t}\n\t\tincident[root] = survivors\n\t}\n\n\treturn events\n}\n\nfunction validateOptions(opts: { Tc: number; Dmax: number }) {\n\tif (!Number.isFinite(opts.Tc) || opts.Tc <= 0) {\n\t\tthrow new Error('Tc must be greater than 0')\n\t}\n\tif (!Number.isFinite(opts.Dmax) || opts.Dmax <= 0) {\n\t\tthrow new Error('Dmax must be greater than 0')\n\t}\n\tif (opts.Dmax < opts.Tc) {\n\t\tthrow new Error('Dmax must be greater than or equal to Tc')\n\t}\n}\n\nfunction zForEdge(\n\tedge: MstEdge,\n\tclusters: ClusterState,\n\topts: { Tc: number; Dmax: number }\n): number {\n\treturn zForRoots(edge, clusters.find(edge.a), clusters.find(edge.b), clusters, opts)\n}\n\nfunction zForRoots(\n\tedge: MstEdge,\n\taRoot: number,\n\tbRoot: number,\n\tclusters: ClusterState,\n\topts: { Tc: number; Dmax: number }\n): number {\n\t// Null unless offset pricing is active AND this pair's mean render offsets differ. Every\n\t// other pair \u2014 including every pair when no offsets were passed \u2014 takes the branch below,\n\t// which is the offset-unaware pricing verbatim.\n\tconst off = clusters.offsetDelta(aRoot, bRoot)\n\tif (off === null) {\n\t\tif (edge.d < D_FLOOR) return Number.POSITIVE_INFINITY\n\t\t// Badge-anchored gap pricing: clusters render as badges at their centroids,\n\t\t// so the merge is priced by the distance between the rendered centers, not\n\t\t// the nearest members. For leaves the two are identical; for clusters the\n\t\t// centroid distance is larger, so groups merge later than their closest\n\t\t// members would suggest \u2014 matching what the user actually sees.\n\t\t// (Coincident CENTROIDS with non-coincident members leave the gap term\n\t\t// Infinity and the fit term finite \u2014 the min stays finite, no special case.)\n\t\tconst gap = opts.Tc / clusters.centroidDistance(aRoot, bRoot)\n\t\tconst fit = opts.Dmax / clusters.unionBboxDiag(aRoot, bRoot)\n\t\treturn Math.min(gap, fit)\n\t}\n\n\t// Offset-aware gap pricing: these markers render at `z\u00B7centroid + meanOffset`, so their\n\t// visual distance at zoom z is |z\u00B7\u0394C + \u0394\u014D| and the merge prices at its Tc crossing. When\n\t// that distance never reaches Tc (the constant offsets hold the visuals apart harder than\n\t// the anchors close), the pair prices at 0 \u2014 visually never mergeable, pruned by finalize's\n\t// minZoom cut. No D_FLOOR fast path here: coincident anchors with differing offsets are a\n\t// constant |\u0394\u014D| apart on screen, which is exactly the \u0394C = 0 case below.\n\tconst dc = clusters.centroidDelta(aRoot, bRoot)\n\tif (dc.x === 0 && dc.y === 0) {\n\t\treturn Math.hypot(off.x, off.y) < opts.Tc ? Number.POSITIVE_INFINITY : 0\n\t}\n\tconst gap = largestVisualCrossing(dc.x, dc.y, off.x, off.y, opts.Tc)\n\tif (gap === null) return 0\n\tconst fit = opts.Dmax / clusters.unionBboxDiag(aRoot, bRoot)\n\treturn Math.min(gap, fit)\n}\n\n/**\n * The largest positive root of `|z\u00B7\u0394C + \u0394\u014D| = level`, i.e. of\n * `|\u0394C|\u00B2\u00B7z\u00B2 + 2(\u0394C\u00B7\u0394\u014D)\u00B7z + (|\u0394\u014D|\u00B2 \u2212 level\u00B2) = 0` \u2014 the zoom at which two offset markers are\n * exactly `level` screen px apart, with the distance below the level for every smaller zoom\n * (matching the \"merged at z \u2264 threshold\" model; a lower second crossing, where opposed offsets\n * push the visuals back above the level near z = 0, is deliberately collapsed). Null when the\n * visual distance never reaches the level. Callers handle \u0394C = 0 (constant distance) themselves.\n */\nfunction largestVisualCrossing(\n\tdcx: number,\n\tdcy: number,\n\tdox: number,\n\tdoy: number,\n\tlevel: number\n): number | null {\n\tconst a = dcx * dcx + dcy * dcy\n\tconst b = 2 * (dcx * dox + dcy * doy)\n\tconst c = dox * dox + doy * doy - level * level\n\tconst disc = b * b - 4 * a * c\n\tif (disc < 0) return null\n\tconst z = (-b + Math.sqrt(disc)) / (2 * a)\n\treturn z > 0 ? z : null\n}\n\nclass ClusterState {\n\tprivate readonly parent: Int32Array\n\tprivate readonly minX: Float64Array\n\tprivate readonly minY: Float64Array\n\tprivate readonly maxX: Float64Array\n\tprivate readonly maxY: Float64Array\n\tprivate readonly centroidX: Float64Array\n\tprivate readonly centroidY: Float64Array\n\tprivate readonly counts: Int32Array\n\tprivate readonly nodes: ClusterNode[]\n\tprivate readonly memberLists: string[][]\n\tprivate readonly minMemberIds: string[]\n\t// Per-cluster sums of member render offsets (screen px), maintained like the centroid sums.\n\t// Null when no offsets were passed \u2014 offsetDelta() then answers null unconditionally, which\n\t// routes every pricing call down the offset-unaware path.\n\tprivate readonly offsetX: Float64Array | null\n\tprivate readonly offsetY: Float64Array | null\n\n\tconstructor(leaves: readonly LeafInput[], screenOffsets?: LeafScreenOffsets) {\n\t\tconst n = leaves.length\n\t\tthis.parent = new Int32Array(n)\n\t\tthis.minX = new Float64Array(n)\n\t\tthis.minY = new Float64Array(n)\n\t\tthis.maxX = new Float64Array(n)\n\t\tthis.maxY = new Float64Array(n)\n\t\tthis.centroidX = new Float64Array(n)\n\t\tthis.centroidY = new Float64Array(n)\n\t\tthis.counts = new Int32Array(n)\n\t\tthis.nodes = new Array(n)\n\t\tthis.memberLists = new Array(n)\n\t\tthis.minMemberIds = new Array(n)\n\n\t\tif (screenOffsets !== undefined && screenOffsets.size > 0) {\n\t\t\tthis.offsetX = new Float64Array(n)\n\t\t\tthis.offsetY = new Float64Array(n)\n\t\t\tfor (let i = 0; i < n; i++) {\n\t\t\t\tconst offset = screenOffsets.get(leaves[i].id)\n\t\t\t\tif (offset) {\n\t\t\t\t\tthis.offsetX[i] = offset.x\n\t\t\t\t\tthis.offsetY[i] = offset.y\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tthis.offsetX = null\n\t\t\tthis.offsetY = null\n\t\t}\n\n\t\tfor (let i = 0; i < n; i++) {\n\t\t\tconst leaf = leaves[i]\n\t\t\tthis.parent[i] = i\n\t\t\tthis.minX[i] = leaf.point.x\n\t\t\tthis.minY[i] = leaf.point.y\n\t\t\tthis.maxX[i] = leaf.point.x\n\t\t\tthis.maxY[i] = leaf.point.y\n\t\t\tthis.centroidX[i] = leaf.point.x\n\t\t\tthis.centroidY[i] = leaf.point.y\n\t\t\tthis.counts[i] = 1\n\t\t\tthis.memberLists[i] = [leaf.id]\n\t\t\tthis.minMemberIds[i] = leaf.id\n\t\t\tthis.nodes[i] = {\n\t\t\t\tid: leaf.id,\n\t\t\t\tcentroid: { x: leaf.point.x, y: leaf.point.y },\n\t\t\t\tcount: 1,\n\t\t\t\tmembers: [leaf.id],\n\t\t\t}\n\t\t}\n\t}\n\n\tfind(index: number): number {\n\t\tlet root = index\n\t\twhile (this.parent[root] !== root) {\n\t\t\troot = this.parent[root]\n\t\t}\n\t\twhile (this.parent[index] !== index) {\n\t\t\tconst next = this.parent[index]\n\t\t\tthis.parent[index] = root\n\t\t\tindex = next\n\t\t}\n\t\treturn root\n\t}\n\n\tcentroidDistance(aRoot: number, bRoot: number): number {\n\t\treturn Math.hypot(\n\t\t\tthis.centroidX[aRoot] - this.centroidX[bRoot],\n\t\t\tthis.centroidY[aRoot] - this.centroidY[bRoot]\n\t\t)\n\t}\n\n\tcentroidDelta(aRoot: number, bRoot: number): { x: number; y: number } {\n\t\treturn {\n\t\t\tx: this.centroidX[aRoot] - this.centroidX[bRoot],\n\t\t\ty: this.centroidY[aRoot] - this.centroidY[bRoot],\n\t\t}\n\t}\n\n\t/** The difference of the two clusters' mean render offsets (screen px), or null when it's\n\t *  zero \u2014 including always when no offsets were passed. Null routes pricing down the\n\t *  offset-unaware path. */\n\toffsetDelta(aRoot: number, bRoot: number): { x: number; y: number } | null {\n\t\tif (this.offsetX === null || this.offsetY === null) return null\n\t\tconst x = this.offsetX[aRoot] / this.counts[aRoot] - this.offsetX[bRoot] / this.counts[bRoot]\n\t\tconst y = this.offsetY[aRoot] / this.counts[aRoot] - this.offsetY[bRoot] / this.counts[bRoot]\n\t\tif (x === 0 && y === 0) return null\n\t\treturn { x, y }\n\t}\n\n\tunionBboxDiag(aRoot: number, bRoot: number): number {\n\t\tconst minX = Math.min(this.minX[aRoot], this.minX[bRoot])\n\t\tconst minY = Math.min(this.minY[aRoot], this.minY[bRoot])\n\t\tconst maxX = Math.max(this.maxX[aRoot], this.maxX[bRoot])\n\t\tconst maxY = Math.max(this.maxY[aRoot], this.maxY[bRoot])\n\t\treturn Math.hypot(maxX - minX, maxY - minY)\n\t}\n\n\tmerge(aRoot: number, bRoot: number, z: number): RawMergeEvent {\n\t\tconst leftRoot = this.minMemberIds[aRoot] < this.minMemberIds[bRoot] ? aRoot : bRoot\n\t\tconst rightRoot = leftRoot === aRoot ? bRoot : aRoot\n\t\tconst left = this.nodes[leftRoot]\n\t\tconst right = this.nodes[rightRoot]\n\t\tconst count = this.counts[leftRoot] + this.counts[rightRoot]\n\t\tconst members = mergeSortedMembers(this.memberLists[leftRoot], this.memberLists[rightRoot])\n\n\t\tconst minX = Math.min(this.minX[leftRoot], this.minX[rightRoot])\n\t\tconst minY = Math.min(this.minY[leftRoot], this.minY[rightRoot])\n\t\tconst maxX = Math.max(this.maxX[leftRoot], this.maxX[rightRoot])\n\t\tconst maxY = Math.max(this.maxY[leftRoot], this.maxY[rightRoot])\n\t\tconst centroidX =\n\t\t\t(this.counts[leftRoot] * this.centroidX[leftRoot] +\n\t\t\t\tthis.counts[rightRoot] * this.centroidX[rightRoot]) /\n\t\t\tcount\n\t\tconst centroidY =\n\t\t\t(this.counts[leftRoot] * this.centroidY[leftRoot] +\n\t\t\t\tthis.counts[rightRoot] * this.centroidY[rightRoot]) /\n\t\t\tcount\n\n\t\t// Leaf ids are assumed not to start with `cluster:`; see the step 2 contract.\n\t\tconst result: ClusterNode = {\n\t\t\tid: `cluster:${count}:${members[0]}`,\n\t\t\tcentroid: { x: centroidX, y: centroidY },\n\t\t\tcount,\n\t\t\tmembers,\n\t\t}\n\n\t\tthis.parent[rightRoot] = leftRoot\n\t\tthis.minX[leftRoot] = minX\n\t\tthis.minY[leftRoot] = minY\n\t\tthis.maxX[leftRoot] = maxX\n\t\tthis.maxY[leftRoot] = maxY\n\t\tthis.centroidX[leftRoot] = centroidX\n\t\tthis.centroidY[leftRoot] = centroidY\n\t\tthis.counts[leftRoot] = count\n\t\tthis.nodes[leftRoot] = result\n\t\tthis.memberLists[leftRoot] = members\n\t\tthis.minMemberIds[leftRoot] = members[0]\n\t\tif (this.offsetX !== null && this.offsetY !== null) {\n\t\t\tthis.offsetX[leftRoot] += this.offsetX[rightRoot]\n\t\t\tthis.offsetY[leftRoot] += this.offsetY[rightRoot]\n\t\t}\n\n\t\treturn { z, children: [left, right], result }\n\t}\n}\n\nfunction mergeSortedMembers(a: readonly string[], b: readonly string[]): string[] {\n\tconst out = new Array<string>(a.length + b.length)\n\tlet i = 0\n\tlet j = 0\n\tlet k = 0\n\twhile (i < a.length && j < b.length) {\n\t\tif (a[i] < b[j]) {\n\t\t\tout[k++] = a[i++]\n\t\t} else {\n\t\t\tout[k++] = b[j++]\n\t\t}\n\t}\n\twhile (i < a.length) out[k++] = a[i++]\n\twhile (j < b.length) out[k++] = b[j++]\n\treturn out\n}\n\ninterface HeapEntry {\n\tedgeIndex: number\n\tz: number\n}\n\nclass EdgeMaxHeap {\n\tprivate readonly items: HeapEntry[] = []\n\t// Per-edge normalized (lo, hi) id pair for the z tie-break, precomputed once so comparisons\n\t// allocate nothing. With coincident anchors every edge prices to the same z (+Infinity), so\n\t// the tie-break runs on nearly every comparison of a rebuild \u2014 allocating the pair there\n\t// churned millions of short-lived tuples.\n\tprivate readonly loIds: string[]\n\tprivate readonly hiIds: string[]\n\n\tconstructor(edges: readonly MstEdge[], leaves: readonly LeafInput[]) {\n\t\tconst n = edges.length\n\t\tthis.loIds = new Array(n)\n\t\tthis.hiIds = new Array(n)\n\t\tfor (let i = 0; i < n; i++) {\n\t\t\tconst aId = leaves[edges[i].a].id\n\t\t\tconst bId = leaves[edges[i].b].id\n\t\t\tif (aId < bId) {\n\t\t\t\tthis.loIds[i] = aId\n\t\t\t\tthis.hiIds[i] = bId\n\t\t\t} else {\n\t\t\t\tthis.loIds[i] = bId\n\t\t\t\tthis.hiIds[i] = aId\n\t\t\t}\n\t\t}\n\t}\n\n\tpeek(): HeapEntry | undefined {\n\t\treturn this.items[0]\n\t}\n\n\tpush(entry: HeapEntry) {\n\t\tthis.items.push(entry)\n\t\tthis.siftUp(this.items.length - 1)\n\t}\n\n\tpop(): HeapEntry | undefined {\n\t\tif (this.items.length === 0) return undefined\n\t\tconst first = this.items[0]\n\t\tconst last = this.items.pop()!\n\t\tif (this.items.length > 0) {\n\t\t\tthis.items[0] = last\n\t\t\tthis.siftDown(0)\n\t\t}\n\t\treturn first\n\t}\n\n\thigherPriority(a: HeapEntry, b: HeapEntry): boolean {\n\t\tif (a.z > b.z) return true\n\t\tif (a.z < b.z) return false\n\t\t// Tie-break on the normalized leaf-id pair, ascending.\n\t\tconst aLo = this.loIds[a.edgeIndex]\n\t\tconst bLo = this.loIds[b.edgeIndex]\n\t\tif (aLo !== bLo) return aLo < bLo\n\t\treturn this.hiIds[a.edgeIndex] < this.hiIds[b.edgeIndex]\n\t}\n\n\tprivate siftUp(index: number) {\n\t\twhile (index > 0) {\n\t\t\tconst parent = (index - 1) >> 1\n\t\t\tif (!this.higherPriority(this.items[index], this.items[parent])) break\n\t\t\t;[this.items[index], this.items[parent]] = [this.items[parent], this.items[index]]\n\t\t\tindex = parent\n\t\t}\n\t}\n\n\tprivate siftDown(index: number) {\n\t\twhile (true) {\n\t\t\tconst left = index * 2 + 1\n\t\t\tconst right = left + 1\n\t\t\tlet best = index\n\t\t\tif (left < this.items.length && this.higherPriority(this.items[left], this.items[best])) {\n\t\t\t\tbest = left\n\t\t\t}\n\t\t\tif (right < this.items.length && this.higherPriority(this.items[right], this.items[best])) {\n\t\t\t\tbest = right\n\t\t\t}\n\t\t\tif (best === index) break\n\t\t\t;[this.items[index], this.items[best]] = [this.items[best], this.items[index]]\n\t\t\tindex = best\n\t\t}\n\t}\n}\n"],
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