Noise Fields, Noise Fields, Noise Fields

Noise Field (512 px x 512 px). An applied noise function. A.G. (c) 2016.

Here we have pretty much the same thing as before, i.e. a function called noisefield() that returns an image. This time what we did, or tried to do, was to generate a "LINE" first that is a list of 512 "RGB tuples" which in this case, as before, are randomly generated (via the noxel() function).

Then, simply enough, all we did was create an entity called "MATRIX" that is made up of 512 "LINEs". We used an "iter" function to create an object called "nixel", as you can see, that iterates over the "MATRIX" object which is basically just a very long sequence of RGB tuples made up of the concatenation of 512 "LINEs"

What you can see in the image above, created via this Python script, is an interesting series of LINEs, each with randomly chosen RGB tuple values.

Here is the code, hosted on Github as a Github Gist.

You might as well ask, since I know you're thinking it: Why go through all the troube? Couldn't you do this much more simply with less code and in a much more idiomatic style, plus much more elegantly? The answer is, yes, of course I could. This is only the beginning. I have structured the code this way because I want to be able to create what I call "mod" functions which are "modulations of the noise field", so to speak. I want to be able to create noise fields and then apply functions to them, either on a pixel-by-pixel basis or else on the whole image, or else on individual "LINEs" which in this case, as vectors if you will of random "RGB tuples", the LINEs that is, are actually "column" vectors and not "row" vectors, if you will. That is to say, the LINEs in the Image, of individual random RGB tuples, appear "horizontally", but the actual LINE in the code ends up being in the "vertical" direction. This is not a glitch, it's because in the code in the 5th line of the noisefield() function, we have (x,y), i.e.

im.putpixel((x, y), nixel.next())

We could just as easily make the uniform-colored lines appear on the vertical axis in the final image just by switching (x,y) to (y,x). To be continued...

Prolegomenon to Noise Field Theory (via Signal Science)

Noise Field (512 px x 512 px). An applied noise function. A.G. (c) 2016.

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...