Prolegomenon To Noise Field Theory (via Signal Science)
Noise Field Theory is a somewhat complex domain that I have been investigating, exploring, now for many years. For now, I just want to share some actual working code in order to eventually develop the theory of Noise Fields in a way that people can actually understand. That way me critics can finally realize that I’m not so crazy after all.
Simply put, you have these “fields” called noise fields. The simplest way to imagine a noise field is to just think of a matrix of numbers, but not just any old matrix. Imagine a digital image, a Bitmap, say, that is 256 pixels by 256 pixels. The simplest form of a noise field is just such an image/matrix made up of random values.
As you shall see in the code, I create something called a “noxel” which is meant to be a kind of “pixel”, i.e. a “noise pixel” = “noxel”. In RGB, it has three values, each of which is a random number between 0 and 255, the possible values for an image inside the RGB model.
What we do is create an image and assign a new noxel to each pixel, creating essentially a random matrix, a.k.a. a noise field. What I will do a little later is explain what this all means, because I can assure you, there is a much deeper meaning that is not obvious at first. (It took me 20 years to get this far, this is not simple, yet it is quite elegant).
That’s it for now. Stay tuned for more details on the noise field as data model — for we are using Python programming to explore Noise Field Theory. It becomes a language, if you will, to speak about noise fields. I will also show why Noise Field Theory is not only best explained through Python itself, but through what we have called Signal Science. In the above case in point, in the code, the “noxel” function is “passed” as an “input” into the “noisefield” function. This allows us to tackle the also complex subject of Signal Science, which essentially is a purely signal-based view of the world and everything in it. To be continued…