Here I’m trying today’s new graphics by filtering and stocking items by color. Seems to work well and gives a lot of creative possibilities!
Here’s the graphics of the membrane items I worked on today. Not sure on the final colors but we’ll see!
I must say that pathfinding has always fascinated me, because it’s a class of algorithms which produce visible and elegant output.
Broadly speaking, it’s a sort of procedural way of generating graphics.
I’m investigating different approaches to computing all the necessary graphs, trying to optimize paths when possible according to the geometry but this is not easy.
Underneath a classic A* algorithm is used, but procedurally placing the nodes ot the graph is proving itself harder than I though!
Some proteins can be assembled also by directly dropping one onto the other. This is useful because you don’t require any specific building to do so!
Of course this is only available for simple reactions, but it can be handy, especially at beginning when your organism doesn’t have much automation.
Since there’s no way to let a microtubule decide the direction of an item, it will continue on it’s direction unless a kicker changes it! There is only a specific circumstance in which it’s possible to determine which direction should an item go and that’s when it’s dropped on a microtubule end, in that scenario there’s only one direction to choose from.
It’s rather hard to find a proper way to generate resources inside an infinite map and being able to use a seed to generate them in the same way. You need two seeds: one is used as the general seed, while another seed is used for each kind of procedural data you want to generate so that you can combine the two and obtain a different start point in the PNRG sequence.
This is rather useful when you want to scatter around a start point resources randomly but at the same time lazily. Using seeds allows to avoid precomputing all the data, you will be always able to figure out if at coordinate
(x,y) there is a specific resource or not with a constant (more or less) time complexity.
You add some bias and mapping function to the results and you are also able to decide how far from the center and how often compared to distance it should spawn.