mirror of https://github.com/tidwall/tile38.git
Updated rtree library
This commit is contained in:
parent
3ed048242e
commit
b37e7395a3
2
go.mod
2
go.mod
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@ -21,7 +21,7 @@ require (
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github.com/tidwall/gjson v1.6.8
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github.com/tidwall/match v1.0.3
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github.com/tidwall/pretty v1.0.2
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github.com/tidwall/rbang v1.2.2
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github.com/tidwall/rbang v1.2.3
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github.com/tidwall/redbench v0.1.0
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github.com/tidwall/redcon v1.4.0
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github.com/tidwall/resp v0.1.0
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2
go.sum
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go.sum
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@ -141,6 +141,8 @@ github.com/tidwall/pretty v1.0.2 h1:Z7S3cePv9Jwm1KwS0513MRaoUe3S01WPbLNV40pwWZU=
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github.com/tidwall/pretty v1.0.2/go.mod h1:XNkn88O1ChpSDQmQeStsy+sBenx6DDtFZJxhVysOjyk=
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github.com/tidwall/rbang v1.2.2 h1:j5JZDSsybpGzCabqFpabaQNU5MCmIrlThXVUF7LD99I=
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github.com/tidwall/rbang v1.2.2/go.mod h1:aMGOM1Wj50tooEO/0aO9j+7gyHUs3bUW0t4Q+xiuOjg=
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github.com/tidwall/rbang v1.2.3 h1:Eg48GtzQEqqwU6kxAna0H0G/m41bm/MQl/EIqU7jfK8=
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github.com/tidwall/rbang v1.2.3/go.mod h1:aMGOM1Wj50tooEO/0aO9j+7gyHUs3bUW0t4Q+xiuOjg=
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github.com/tidwall/redbench v0.1.0 h1:UZYUMhwMMObQRq5xU4SA3lmlJRztXzqtushDii+AmPo=
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github.com/tidwall/redbench v0.1.0/go.mod h1:zxcRGCq/JcqV48YjK9WxBNJL7JSpMzbLXaHvMcnanKQ=
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github.com/tidwall/redcon v1.4.0 h1:y2PmDD55STRdy4S98qP/Dn+gZG+cPVvIDi9BJV2aOwA=
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@ -1,4 +1,4 @@
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# `rbang`
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# rbang
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[![GoDoc](https://godoc.org/github.com/tidwall/rbang?status.svg)](https://godoc.org/github.com/tidwall/rbang)
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@ -27,7 +27,7 @@ var tr rbang.RTree
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// insert a point
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tr.Insert([2]float64{-112.0078, 33.4373}, [2]float64{-112.0078, 33.4373}, "PHX")
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// insert a box
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// insert a rect
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tr.Insert([2]float64{10, 10}, [2]float64{20, 20}, "rect")
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// search
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@ -48,11 +48,11 @@ This implementation is a variant of the original paper:
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### Inserting
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Same as the original algorithm. From the root to the leaf, the boxes which will incur the least enlargment are chosen. Ties go to boxes with the smallest area.
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Same as the original algorithm. From the root to the leaf, the rects which will incur the least enlargment are chosen. Ties go to rects with the smallest area.
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### Deleting
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Same as the original algorithm. A target box is deleted directly. When the number of children in a box falls below it's minumum entries, it is removed from the tree and it's items are re-inserted.
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Same as the original algorithm. A target rect is deleted directly. When the number of children in a rect falls below it's minumum entries, it is removed from the tree and it's items are re-inserted.
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### Splitting
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@ -60,29 +60,19 @@ This is a custom algorithm.
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It attempts to minimize intensive operations such as pre-sorting the children and comparing overlaps & area sizes.
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The desire is to do simple single axis distance calculations each child only once, with a target 50/50 chance that the child might be moved in-memory.
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When a box has reached it's max number of entries it's largest axis is calculated and the box is split into two smaller boxes, named `left` and `right`.
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Each child boxes is then evaluated to determine which smaller box it should be placed into.
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When a rect has reached it's max number of entries it's largest axis is calculated and the rect is split into two smaller rects, named `left` and `right`.
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Each child rects is then evaluated to determine which smaller rect it should be placed into.
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Two values, `min-dist` and `max-dist`, are calcuated for each child.
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- `min-dist` is the distance from the parent's minumum value of it's largest axis to the child's minumum value of the parent largest axis.
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- `max-dist` is the distance from the parent's maximum value of it's largest axis to the child's maximum value of the parent largest axis.
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When the `min-dist` is less than `max-dist` then the child is placed into the `left` box.
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When the `max-dist` is less than `min-dist` then the child is placed into the `right` box.
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When the `min-dist` is less than `max-dist` then the child is placed into the `left` rect.
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When the `max-dist` is less than `min-dist` then the child is placed into the `right` rect.
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When the `min-dist` is equal to `max-dist` then the child is placed into an `equal` bucket until all of the children are evaluated.
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Each `equal` box is then one-by-one placed in either `left` or `right`, whichever has less children.
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## Performance
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In my testing:
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- Insert show similar performance as the quadratic R-tree and ~1.2x - 1.5x faster than R*tree.
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- Search and Delete is ~1.5x - 2x faster than quadratic and about the same as R*tree.
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I hope to provide more details in the future.
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Each `equal` rect is then one-by-one placed in either `left` or `right`, whichever has less children.
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## License
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`rbang` source code is available under the MIT License.
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rbang source code is available under the MIT License.
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@ -140,18 +140,49 @@ func (tr *RTree) insert(item *rect) {
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tr.count++
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}
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func (r *rect) chooseLeastEnlargement(b *rect) int {
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j, jenlargement, jarea := -1, 0.0, 0.0
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const inlineEnlargedArea = true
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func (r *rect) chooseLeastEnlargement(b *rect) (index int) {
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n := r.data.(*node)
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j, jenlargement, jarea := -1, 0.0, 0.0
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for i := 0; i < n.count; i++ {
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area := n.rects[i].area()
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enlargement := n.rects[i].enlargedArea(b) - area
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if j == -1 || enlargement < jenlargement {
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j, jenlargement, jarea = i, enlargement, area
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} else if enlargement == jenlargement {
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if area < jarea {
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j, jenlargement, jarea = i, enlargement, area
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var earea float64
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if inlineEnlargedArea {
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earea = 1.0
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if b.max[0] > n.rects[i].max[0] {
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if b.min[0] < n.rects[i].min[0] {
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earea *= b.max[0] - b.min[0]
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} else {
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earea *= b.max[0] - n.rects[i].min[0]
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}
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} else {
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if b.min[0] < n.rects[i].min[0] {
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earea *= n.rects[i].max[0] - b.min[0]
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} else {
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earea *= n.rects[i].max[0] - n.rects[i].min[0]
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}
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}
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if b.max[1] > n.rects[i].max[1] {
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if b.min[1] < n.rects[i].min[1] {
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earea *= b.max[1] - b.min[1]
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} else {
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earea *= b.max[1] - n.rects[i].min[1]
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}
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} else {
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if b.min[1] < n.rects[i].min[1] {
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earea *= n.rects[i].max[1] - b.min[1]
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} else {
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earea *= n.rects[i].max[1] - n.rects[i].min[1]
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}
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}
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} else {
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earea = n.rects[i].enlargedArea(b)
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}
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area := n.rects[i].area()
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enlargement := earea - area
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if j == -1 || enlargement < jenlargement ||
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(enlargement == jenlargement && area < jarea) {
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j, jenlargement, jarea = i, enlargement, area
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}
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}
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return j
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@ -233,8 +264,25 @@ func (r *rect) insert(item *rect, height int) (grown bool) {
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grown = !r.contains(item)
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return grown
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}
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// choose subtree
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index := r.chooseLeastEnlargement(item)
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index := -1
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narea := 0.0
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// first take a quick look for any nodes that contain the rect
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for i := 0; i < n.count; i++ {
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if n.rects[i].contains(item) {
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area := n.rects[i].area()
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if index == -1 || area < narea {
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narea = area
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index = i
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}
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}
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}
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// found nothing, now go the slow path
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if index == -1 {
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index = r.chooseLeastEnlargement(item)
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}
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// insert the item into the child node
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child := &n.rects[index]
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grown = child.insert(item, height-1)
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if grown {
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@ -374,8 +422,7 @@ func (tr *RTree) Delete(min, max [2]float64, data interface{}) {
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return
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}
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var removed, recalced bool
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removed, recalced, tr.reinsert =
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tr.root.delete(&item, tr.height, tr.reinsert[:0])
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removed, recalced = tr.root.delete(tr, &item, tr.height)
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if !removed {
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return
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}
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@ -393,60 +440,62 @@ func (tr *RTree) Delete(min, max [2]float64, data interface{}) {
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if recalced {
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tr.root.recalc()
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}
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for i := range tr.reinsert {
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tr.insert(&tr.reinsert[i])
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tr.reinsert[i].data = nil
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if len(tr.reinsert) > 0 {
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for i := range tr.reinsert {
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tr.insert(&tr.reinsert[i])
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tr.reinsert[i].data = nil
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}
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tr.reinsert = tr.reinsert[:0]
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}
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}
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func (r *rect) delete(item *rect, height int, reinsert []rect) (
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removed, recalced bool, reinsertOut []rect,
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) {
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func (r *rect) delete(tr *RTree, item *rect, height int,
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) (removed, recalced bool) {
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n := r.data.(*node)
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rects := n.rects[0:n.count]
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if height == 0 {
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for i := 0; i < n.count; i++ {
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if n.rects[i].data == item.data {
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for i := 0; i < len(rects); i++ {
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if rects[i].data == item.data {
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// found the target item to delete
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recalced = r.onEdge(&n.rects[i])
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n.rects[i] = n.rects[n.count-1]
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n.rects[n.count-1].data = nil
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recalced = r.onEdge(&rects[i])
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rects[i] = rects[len(rects)-1]
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rects[len(rects)-1].data = nil
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n.count--
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if recalced {
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r.recalc()
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}
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return true, recalced, reinsert
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return true, recalced
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}
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}
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} else {
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for i := 0; i < n.count; i++ {
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if !n.rects[i].contains(item) {
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for i := 0; i < len(rects); i++ {
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if !rects[i].contains(item) {
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continue
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}
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removed, recalced, reinsert =
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n.rects[i].delete(item, height-1, reinsert)
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removed, recalced = rects[i].delete(tr, item, height-1)
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if !removed {
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continue
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}
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if n.rects[i].data.(*node).count < minEntries {
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if rects[i].data.(*node).count < minEntries {
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// underflow
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if !recalced {
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recalced = r.onEdge(&n.rects[i])
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recalced = r.onEdge(&rects[i])
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}
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reinsert = n.rects[i].flatten(reinsert, height-1)
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n.rects[i] = n.rects[n.count-1]
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n.rects[n.count-1].data = nil
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tr.reinsert = rects[i].flatten(tr.reinsert, height-1)
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rects[i] = rects[len(rects)-1]
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rects[len(rects)-1].data = nil
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n.count--
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}
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if recalced {
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r.recalc()
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}
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return removed, recalced, reinsert
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return removed, recalced
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}
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}
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return false, false, reinsert
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return false, false
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}
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// flatten flattens all leaf rects into a single list
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// flatten all leaf rects into a single list
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func (r *rect) flatten(all []rect, height int) []rect {
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n := r.data.(*node)
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if height == 0 {
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@ -525,8 +574,9 @@ func (tr *RTree) Children(
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return children
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}
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// Replace an item in the structure. This is effectively just a Delete
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// followed by an Insert.
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// Replace an item.
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// This is effectively just a Delete followed by an Insert. Which means the
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// new item will always be inserted, whether or not the old item was deleted.
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func (tr *RTree) Replace(
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oldMin, oldMax [2]float64, oldData interface{},
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newMin, newMax [2]float64, newData interface{},
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@ -146,7 +146,7 @@ github.com/tidwall/match
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# github.com/tidwall/pretty v1.0.2
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## explicit
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github.com/tidwall/pretty
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# github.com/tidwall/rbang v1.2.2
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# github.com/tidwall/rbang v1.2.3
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## explicit
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github.com/tidwall/rbang
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# github.com/tidwall/redbench v0.1.0
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