Tried out Google's AI response to the question of a C++ spatial hash and it kind of came close. This took some massaging but it could work.
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@ -38,4 +38,9 @@ myecstest = executable('myecstest', [
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],
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],
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dependencies: dependencies)
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dependencies: dependencies)
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collider = executable('collider', [
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'./scratchpad/collider.cpp'
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],
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dependencies: dependencies)
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test('tests', runtests)
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test('tests', runtests)
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70
scratchpad/collider.cpp
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70
scratchpad/collider.cpp
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@ -0,0 +1,70 @@
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#include <iostream>
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#include <vector>
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#include <unordered_map>
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struct Point {
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int x, y;
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bool operator==(const Point& other) const {
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return other.x == x && other.y == y;
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}
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};
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struct Object {
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Point position;
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// Add other object data as needed
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};
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struct PointHash {
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size_t operator()(const Point& p) const {
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return std::hash<int>()(p.x) ^ std::hash<int>()(p.y);
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}
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};
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class SpatialHashTable {
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public:
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SpatialHashTable() {}
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void insert(Object* obj) {
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table[obj->position].push_back(obj);
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}
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std::vector<Object*> getNearbyObjects(Point position) {
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std::vector<Object*> result;
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Point cell = position;
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// Check the current cell and its 8 neighbors
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for (int x = cell.x - 1; x <= cell.x + 1; x++) {
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for (int y = cell.y - 1; y <= cell.y + 1; y++) {
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Point neighborCell = {x, y};
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auto it = table.find(neighborCell);
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if (it != table.end()) {
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result.insert(result.end(), it->second.begin(), it->second.end());
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}
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}
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}
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return result;
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}
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private:
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std::unordered_map<Point, std::vector<Object*>, PointHash> table;
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};
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int main() {
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SpatialHashTable hashTable;
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Object obj1 = {{5, 5}};
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Object obj2 = {{15, 15}};
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Object bomb = {{25, 25}};
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hashTable.insert(&obj1);
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hashTable.insert(&obj2);
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hashTable.insert(&bomb);
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std::vector<Object*> nearby = hashTable.getNearbyObjects({24, 24});
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for (Object* obj : nearby) {
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std::cout << obj->position.x << ", " << obj->position.y << std::endl;
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}
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return 0;
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}
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