Hence you may need to increase resolution of the noise. In my view, you will need to have shared common noise, across multiple tiles. For those who have followed it I would like something alike but with more realistic graphics.īut regarding smoothing between tiles, that was my second thought, what maybe your goal. I've done the catlikecoding tutorial on hexmaps (which I recommend). But for performance issues at run time meh (the texture has to be quite large for the whole chunk). With maybe the RGB for the texture and alpha for the height. The second approach was to use Height Maps of mountains and 'paint' them on a blank texture which I apply on the chunks (with something like setPixels/GetPixels). I'm also using chunks for performance and the transistion seems complicated.

But I'm not sure what type of mask I should use (for now I've made a non-optimised one with a gaussian function in the center of the selected hex but the result is not very good with the noise (maybe a mask in x^3?)). My first approach was to use Perlin noise and a mask to tell which hexs has mountains on it. (Maybe a hexagonal mesh is not the good approach?) With maybe a procedural generation in the futur. I want to be able to click on hexs to generate mountains (or cliffs, etc).

I already made a hexagonal mesh where i can define a resolution for each hex. I would like to make a 'realistic' terrain, like in civilization 5 or 6 for exemple. I've already tried some things but I'm not experienced enough to tell if it's feasible. I would like some guidance on the method I should use.

Using a KDTreedata structure instead of a list/numpy array can get you an even higher speedup (265 faster in my tests), as this structure is built precisely with your application in mind (querying a point cloud for the closest one): from scipy.I'm currently trying to build a Map Editor for a Hexagonal Grid based game. This version is about 75 faster than your code on my computer, with 5000 elements in CENTER_LIST. # You can convert a python list to a numpy array with my_array = np.array(my_list)ĭist = np.sum((CENTER_LIST - pos)**2, axis=1) # We create a numpy array (basically a list of lists) of 5000 random coordinates Should we keep to your list of center points approach, however,Ī good way to speed up this kind of computation is usually to use the numpy library, which was built with scientific computing in mind and comes with extremely quick built-in functions: import numpy as np Keeping a list of central points probably isn't the best way to represent hex tiles, and using a hex coordinate system is probably the most appropriate way of solving the problem (see the comments below your post for good resources to get started).

Hexagon board game map