# Voronoi Hive/Cell Maps

**About this tutorial**

*This tutorial is free and open source, and all code uses the MIT license - so you are free to do with it as you like. My hope is that you will enjoy the tutorial, and make great games!*

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We've touched on Voronoi diagrams before, in our spawn placement. In this section, we'll use them to make a map. The algorithm basically subdivides the map into regions, and places walls between them. The result is a bit like a hive. You can play with the distance/adjacency algorithm to adjust the results.

## Scaffolding

We'll make scaffolding like in the previous chapters, making `voronoi.rs`

with the structure `VoronoiBuilder`

in it. We'll also adjust our `random_builder`

function to only return `VoronoiBuilder`

for now.

## Building a Voronoi Diagram

In previous usages, we've skimmed over how to actually make a Voronoi diagram - and relied on the `FastNoise`

library inside `rltk`

. That's all well and good, but it doesn't really show us *how* it works - and gives very limited opportunities to tweak it. So - we'll make our own.

The first step in making some Voronoi noise it to populate a set of "seeds". These are randomly chosen (but not duplicate) points on the map. We'll make the number of seeds a variable so it can be tweaked later. Here's the code:

`#![allow(unused)] fn main() { let n_seeds = 64; let mut voronoi_seeds : Vec<(usize, rltk::Point)> = Vec::new(); while voronoi_seeds.len() < n_seeds { let vx = rng.roll_dice(1, self.map.width-1); let vy = rng.roll_dice(1, self.map.height-1); let vidx = self.map.xy_idx(vx, vy); let candidate = (vidx, rltk::Point::new(vx, vy)); if !voronoi_seeds.contains(&candidate) { voronoi_seeds.push(candidate); } } }`

This makes a `vector`

, each entry containing a `tuple`

. Inside that tuple, we're storing an index to the map location, and a `Point`

with the `x`

and `y`

coordinates in it (we could skip saving those and calculate from the index if we wanted, but I feel that this is clearer). Then we randomly determine a position, check to see that we haven't already rolled that location, and add it. We repeat the process until we have the desired number of seeds. `64`

is quite a lot, but will give a relatively dense hive-like structure.

The next step is to determine each cell's Voronoi membership:

`#![allow(unused)] fn main() { let mut voronoi_distance = vec![(0, 0.0f32) ; n_seeds]; let mut voronoi_membership : Vec<i32> = vec![0 ; self.map.width as usize * self.map.height as usize]; for (i, vid) in voronoi_membership.iter_mut().enumerate() { let x = i as i32 % self.map.width; let y = i as i32 / self.map.width; for (seed, pos) in voronoi_seeds.iter().enumerate() { let distance = rltk::DistanceAlg::PythagorasSquared.distance2d( rltk::Point::new(x, y), pos.1 ); voronoi_distance[seed] = (seed, distance); } voronoi_distance.sort_by(|a,b| a.1.partial_cmp(&b.1).unwrap()); *vid = voronoi_distance[0].0 as i32; } }`

In this block of code, we:

- Create a new
`vector`

, called`voronoi_distance`

. It contains tuples of a`usize`

and a`f32`

(float), and is pre-made with`n_seeds`

entries. We could make this for every iteration, but it's a lot faster to reuse the same one. We create it zeroed. - We create a new
`voronoi_membership`

vector, containing one entry per tile on the map. We set them all to 0. We'll use this to store which Voronoi cell the tile belongs to. - For every tile in
`voronoi_membership`

, we obtain an enumerator (index number) and the value. We have this mutably, so we can make changes.- We calculate the
`x`

and`y`

position of the tile from the enumerator (`i`

). - For each entry in the
`voronoi_seeds`

structure, we obtain the index (via`enumerate()`

) and the position tuple.- We calculate the distance from the seed to the current tile, using the
`PythagorasSquared`

algorithm. - We set
`voronoi_distance[seed]`

to the seed index and the distance.

- We calculate the distance from the seed to the current tile, using the
- We sort the
`voronoi_distance`

vector by the distance, so the closest seed will be the first entry. - We set the tile's
`vid`

(Voronoi ID) to the first entry in the`voronoi_distance`

list.

- We calculate the

You can summarize that in English more easily: each tile is given membership of the Voronoi group to whom's seed it is physically closest.

Next, we use this to draw the map:

`#![allow(unused)] fn main() { for y in 1..self.map.height-1 { for x in 1..self.map.width-1 { let mut neighbors = 0; let my_idx = self.map.xy_idx(x, y); let my_seed = voronoi_membership[my_idx]; if voronoi_membership[self.map.xy_idx(x-1, y)] != my_seed { neighbors += 1; } if voronoi_membership[self.map.xy_idx(x+1, y)] != my_seed { neighbors += 1; } if voronoi_membership[self.map.xy_idx(x, y-1)] != my_seed { neighbors += 1; } if voronoi_membership[self.map.xy_idx(x, y+1)] != my_seed { neighbors += 1; } if neighbors < 2 { self.map.tiles[my_idx] = TileType::Floor; } } self.take_snapshot(); } }`

In this code, we visit every tile except for the very outer edges. We count how many neighboring tiles are in a *different* Voronoi group. If the answer is 0, then it is entirely in the group: so we can place a floor. If the answer is 1, it only borders 1 other group - so we can also place a floor (to ensure we can walk around the map). Otherwise, we leave the tile as a wall.

Then we run the same culling and placement code we've used before. If you `cargo run`

the project now, you will see a pleasant structure:

.

## Tweaking the Hive

There are two obvious variables to expose to the builder: the number of seeds, and the distance algorithm to use. We'll update the structure signature to include these:

`#![allow(unused)] fn main() { #[derive(PartialEq, Copy, Clone)] pub enum DistanceAlgorithm { Pythagoras, Manhattan, Chebyshev } pub struct VoronoiCellBuilder { map : Map, starting_position : Position, depth: i32, history: Vec<Map>, noise_areas : HashMap<i32, Vec<usize>>, n_seeds: usize, distance_algorithm: DistanceAlgorithm } }`

Then we'll update the Voronoi code to use them:

`#![allow(unused)] fn main() { fn build(&mut self) { let mut rng = RandomNumberGenerator::new(); // Make a Voronoi diagram. We'll do this the hard way to learn about the technique! let mut voronoi_seeds : Vec<(usize, rltk::Point)> = Vec::new(); while voronoi_seeds.len() < self.n_seeds { let vx = rng.roll_dice(1, self.map.width-1); let vy = rng.roll_dice(1, self.map.height-1); let vidx = self.map.xy_idx(vx, vy); let candidate = (vidx, rltk::Point::new(vx, vy)); if !voronoi_seeds.contains(&candidate) { voronoi_seeds.push(candidate); } } let mut voronoi_distance = vec![(0, 0.0f32) ; self.n_seeds]; let mut voronoi_membership : Vec<i32> = vec![0 ; self.map.width as usize * self.map.height as usize]; for (i, vid) in voronoi_membership.iter_mut().enumerate() { let x = i as i32 % self.map.width; let y = i as i32 / self.map.width; for (seed, pos) in voronoi_seeds.iter().enumerate() { let distance; match self.distance_algorithm { DistanceAlgorithm::Pythagoras => { distance = rltk::DistanceAlg::PythagorasSquared.distance2d( rltk::Point::new(x, y), pos.1 ); } DistanceAlgorithm::Manhattan => { distance = rltk::DistanceAlg::Manhattan.distance2d( rltk::Point::new(x, y), pos.1 ); } DistanceAlgorithm::Chebyshev => { distance = rltk::DistanceAlg::Chebyshev.distance2d( rltk::Point::new(x, y), pos.1 ); } } voronoi_distance[seed] = (seed, distance); } voronoi_distance.sort_by(|a,b| a.1.partial_cmp(&b.1).unwrap()); *vid = voronoi_distance[0].0 as i32; } for y in 1..self.map.height-1 { for x in 1..self.map.width-1 { let mut neighbors = 0; let my_idx = self.map.xy_idx(x, y); let my_seed = voronoi_membership[my_idx]; if voronoi_membership[self.map.xy_idx(x-1, y)] != my_seed { neighbors += 1; } if voronoi_membership[self.map.xy_idx(x+1, y)] != my_seed { neighbors += 1; } if voronoi_membership[self.map.xy_idx(x, y-1)] != my_seed { neighbors += 1; } if voronoi_membership[self.map.xy_idx(x, y+1)] != my_seed { neighbors += 1; } if neighbors < 2 { self.map.tiles[my_idx] = TileType::Floor; } } self.take_snapshot(); } ... }`

As a test, lets change the constructor to use `Manhattan`

distance. The results will look something like this:

.

Notice how the lines are straighter, and less organic looking. That's what Manhattan distance does: it calculates distance like a Manhattan Taxi Driver - number of rows plus number of columns, rather than a straight line distance.

## Restoring Randomness

So we'll put a couple of constructors in for each of the noise types:

`#![allow(unused)] fn main() { pub fn pythagoras(new_depth : i32) -> VoronoiCellBuilder { VoronoiCellBuilder{ map : Map::new(new_depth), starting_position : Position{ x: 0, y : 0 }, depth : new_depth, history: Vec::new(), noise_areas : HashMap::new(), n_seeds: 64, distance_algorithm: DistanceAlgorithm::Pythagoras } } pub fn manhattan(new_depth : i32) -> VoronoiCellBuilder { VoronoiCellBuilder{ map : Map::new(new_depth), starting_position : Position{ x: 0, y : 0 }, depth : new_depth, history: Vec::new(), noise_areas : HashMap::new(), n_seeds: 64, distance_algorithm: DistanceAlgorithm::Manhattan } } }`

Then we'll restore the `random_builder`

to once again be random:

`#![allow(unused)] fn main() { pub fn random_builder(new_depth: i32) -> Box<dyn MapBuilder> { let mut rng = rltk::RandomNumberGenerator::new(); let builder = rng.roll_dice(1, 16); match builder { 1 => Box::new(BspDungeonBuilder::new(new_depth)), 2 => Box::new(BspInteriorBuilder::new(new_depth)), 3 => Box::new(CellularAutomataBuilder::new(new_depth)), 4 => Box::new(DrunkardsWalkBuilder::open_area(new_depth)), 5 => Box::new(DrunkardsWalkBuilder::open_halls(new_depth)), 6 => Box::new(DrunkardsWalkBuilder::winding_passages(new_depth)), 7 => Box::new(DrunkardsWalkBuilder::fat_passages(new_depth)), 8 => Box::new(DrunkardsWalkBuilder::fearful_symmetry(new_depth)), 9 => Box::new(MazeBuilder::new(new_depth)), 10 => Box::new(DLABuilder::walk_inwards(new_depth)), 11 => Box::new(DLABuilder::walk_outwards(new_depth)), 12 => Box::new(DLABuilder::central_attractor(new_depth)), 13 => Box::new(DLABuilder::insectoid(new_depth)), 14 => Box::new(VoronoiCellBuilder::pythagoras(new_depth)), 15 => Box::new(VoronoiCellBuilder::manhattan(new_depth)), _ => Box::new(SimpleMapBuilder::new(new_depth)) } } }`

## Wrap-Up

That's another algorithm under our belts! We really have enough to write a pretty good roguelike now, but there are still more to come!

**The source code for this chapter may be found here**

## Run this chapter's example with web assembly, in your browser (WebGL2 required)

Copyright (C) 2019, Herbert Wolverson.