I wrote an article for Dev.Mag covering some techniques for working with seamless tile sets such as making blend tiles, getting more variety with procedural colour manipulation, tile placement strategies, and so on.
Check it out!
The Python Image Code has also been updated with some of the algorithms explained in the article.
I have already covered how to generate random numbers from arbitrary distributions in the one-dimensional case. Here we look at a generalisation of that method that works for higher dimensions.
The basic trick, while easy to understand, is hard to put in words (without reverting to mathematical equations). For two dimensions, we divide the plane into slices. Each slice is a 1D distribution. We also calculate a distribution from summing the frequencies in each slice. The latter distribution gives us one coordinate, and the appropriate slice to use. The distribution of that slice then gives the second coordinate. All distributions are put into inverse accumulative response curves as was done to generate one-dimensional random numbers. (You should review that before implementing the 2D case).
In more dimensions, we also slice the space up into 1D distributions. Sums of these give us more distributions, which we can sum again, and again, until we reach a single distribution. This is used for the first coordinate, and to determine which distribution to use for the next coordinate. This goes on, until a 1D slice gives us the final coordinate. Again, all distributions are converted to inverse accumulative response curves.
If the above is unclear, I hope the detailed description below clears things up.
Continue reading “Generating Random Points from Arbitrary Distributions for 2D and Up”
A cellular automata system is one of the best demonstrations of emergence. If you do not know what cellular automata (CA) is, then you should go download Conway’s Game of Life immediately:
Conway’s Game of Life
Essentially, CA is a collection of state machines, updated in discrete time intervals. The next state of one of these depends on the current state as well as the states of neighbours. Usually, the state machines correspond to cells in a grid, and the neighbours of a cell are the cells connected to that cell. For a more detailed explanation, see the Wikipedia article.
Even simple update rules can lead to interesting behaviour: patterns that cannot be predicted from the rules except by running them. With suitable rules, CA can simulate many systems:
- Natural phenomena: weather, fire, plant growth, migration patterns, spread of disease.
- Socio-economic phenomena: urbanisation, segregation, construction and property development, traffic, spread of news.
Continue reading “Cellular Automata for Simulation in Games”
(Photo by Darren Hester)
Some algorithms take a long time to return their results. Whether it is because the algorithm has to operate on a huge data set, or because it has combinatorial complexity; every time you run it you have to wait minutes or even hours for the thing to finish, making errors very expensive.
This post gives some advice on how to prototype slow algorithms with as little frustration as possible. We assume that this algorithm is being implemented experimentally – that is, you will tweak it and change it often before it is finished (it is not the kind of algorithm you type in straight from a text book). For example, I used the ideas outlined here while playing with the texture generating algorithm of the previous post.
Continue reading “5 Tips for Prototyping Slow Algorithms”