Break time! We have been playing with all this random functions that looks like TV white noise, our head is still spinning around thinking on shaders, and our eyes are tired. Time to get out for a walk!
We feel the air in our skin, the sun in our face. The world is such a vivid and rich place. Colors, textures, sounds. While we walk we can't avoid noticing the surface of the roads, rocks, trees and clouds.
The stochasticity of this textures could be call "random", but definitely not the type of random we were playing before in the previus chapter. The “real world” is such a rich place! It's rando is way complex. How we can approximate to this level of variety computationally?
This was the question [Ken Perlin](https://mrl.nyu.edu/~perlin/) was trying to solve arround 1982 when he was commissioned with the job of generating "more realistic" textures for a new disney movie call "Tron". In response to that he came up with an elegant *oscar winner* noise algorithm.
In the following graph you will see what we were doing on the previus chapter, obtaining ```rand()``` numbers of the integers of the `x` position (assigning it to `i` variable), while we keep the [```fract()```](.../glossary/?search=fract) part of it (and storing it on the `f` variable).
You will also see, two commented lines. The first one interpolates linearly between the random value of the integer position and it next one.
```glsl
y = mix(rand(i), rand(i + 1.0), f);
```
Uncomment it an see how that looks. See how we use the [```fract()```](.../glossary/?search=fract) value store in `f` to [```mix()```](.../glossary/?search=mix) the two random values.
At this point on the book, we learned that we can do better than a linear interpolation. Right?
The following commented line, will transfor the linear interpolation of `f` with a [```smoothstep()```](.../glossary/?search=smoothstep) interpolation.
Uncomment that line and notice how the transition between the peaks got smooth. On some noise implementations you will find that people that some programers prefere to code their own cubic curves (like the following formula) instead of using the [```smoothstep()```](.../glossary/?search=smoothstep).
The *smooth random* is a game changer for graphical coders, it provides the hability to generate images and geometries with an organic feeling. Perlin's Noise Algorithm have been reimplemented over and over in different lenguage and dimensions for all kind of uses to create all sort of mesmerizing pieces.
Now is your turn:
* Make your own ```float noise(float x)``` function.
* Use the noise funtion to animate a shape by moving it, rotating it or scaling it.
* Make an animated composition of several shapes 'dancing' together using noise.
* Construct "organic-looking" shapes using the noise function.
* Once you have your "creature", try to develop further this into a character by assigning it a particular movement.
Now that we know how to do noise in one dimension, is time to port it to two. For that instead of interpolating between two points (```fract(x)``` and ```fract(x)+1.0```) we are going to interpolate between the four coorners of square (```fract(st)```, ```fract(st)+vec2(1.,0.)```, ```fract(st)+vec2(0.,1.)``` and ```fract(st)+vec2(1.,1.)```).
Take a look to the following 2D noise function, see how on line 32 we interpolate random values (lines 25-28) acording to the the position of ```st``` the four corners of the squared area.
* Now that you achieve some control over order and chaos, is time to use that knowledge. Make a composition of rectangles, colors and noise that resemble some of the complexity of the texture of the following painting made by [Mark Rothko](http://en.wikipedia.org/wiki/Mark_Rothko).
As we saw, noise algorithms was original designed to give a natural *je ne sais quoi* to digital textures. Our first step to use it will be to differenciate different types of noise algorithms. So far all the implementations in 1D and 2D we saw, were interpolation between values and so they are usually call **Value Noise**.
As you discovery on the previus excercises this type of noise tends to look "block", as a solution to this effect in 1985 again [Ken Perlin](https://mrl.nyu.edu/~perlin/) develop another implementation of the algorithm call **Gradient Noise**. In it what is interpolated per coorner is not a value but a direction (represented by a ```vec2```).
Take a minute to look to these two examples by [Inigo Quilez](http://www.iquilezles.org/) and pay attention on the differences between [value noise](https://www.shadertoy.com/view/lsf3WH) and [gradient noise](https://www.shadertoy.com/view/XdXGW8).
As a painter that understand how the pigments of their paint works, the more we know about noise implementations the better we will learn how to use it. The following step is to find interesting way of combining and using it.
Another way to get interesting patterns from noise is to treat it like a distance field and apply some of the tricks described on the [Shapes chapter](../07/)
* What other generative pattern can you make? What about granite? marble? magma? water? Find three pictures of textures you are interested and implement them algorithmically using noise.
* Use noise to modulate a shapes.
* What about using noise for motion? Go back to the [Matrix chapter](../08/) and use the translation example that move a the "+" around to apply some *random* and *noise* movements to it.
For Ken Perlin the success of his algorithm wasn't enough. He thought it could performance better. In Siggraph 2001 he presented the "simplex noise" in wich he achive the following improvements over the previus one:
Yeah, right? I know what you are thinking... "Who is this man?". Yes, his work is fantastic. But seriusly, How he did that? Well we saw how for two dimensions he was interpolating 4 points (coorners of a square); also we can correctly preasume that for [three (see an implementation here)](../edit.html#11/3d-noise.frag) and four dimensions we need to interpolate 8 and 16 points. Right? In other words for N dimensions you need to smoothly interpolate 2 to the N points (2^N). But Ken smartly notice that although the obvious choice for a space-filling shape is a squar, but actually the formal simplex shape in 2D is an equilateral triangle.
That means that the simplex shape for N dimensions is a shape with N + 1 corners. In other words one less corner to compute in 2D, 4 less coorners in 3D and 11 less coorners in 4D! That's a huge improvement!
So, in two dimension the interpolation happens, in a similar way than regular noise, by interpolating the values of the corners of a section. But, in this particular case, because we are using a simplex grid we just need to interpolate the sum of only 3 coornes (or contributors) to interpolate.
How the simplex grid works? In another brillant and elegant move, simplex grid can be obtain by subdividing the cells of a regular 4 corners grid into two isoceles triangles and then skewing it unitl each triangle is equilateral.
Then, as [Stefan Gustavson describe in this paper](http://staffwww.itn.liu.se/~stegu/simplexnoise/simplexnoise.pdf): _"...by looking at the integer parts of the transformed coordinates (x,y) for the point we want to evaluate, we can quickly determine which cell of two simplices that contain the point. By also compareing the magnitudes of x and y, we can determine whether the points is in the upper or the lower simplex, and traverse the correct three corners points."_. On the following code you can un comment the line 44 to see how the grid is skew and then line 47 to see how a simplex grid can be constructed. Note how on line 22 we are subdiving the skewed squared on two equilateral triangles just buy detecting if ```x > y``` ("lower" triangle) or ```y > x``` ("upper" triangle).
Other improvements introduce by Perlin, is the replacement of the Cubic Hermite Curve ( _f(x) = 3x^2-2x^3_ ) for a Quintic Hermite Curve ( _f(x) = 6x^5-15x^4+10x^3_ ). In an efford to keep the "math stuff" simple this makes both ends of the curve more flat and by that a more continuos transition with the next interpolation. You can watch it for your self by uncommenting the second formula on the following graph example (or by see the [two equations side by side here](https://www.desmos.com/calculator/2xvlk5xp8b)). Note how the ends of the curve changes. You can read more about this in [on words of Ken it self in this paper](http://mrl.nyu.edu/~perlin/paper445.pdf).
Following is an actual GLSL implementation of this algorithm made by Ian McEwan (and presented in [this paper](http://webstaff.itn.liu.se/~stegu/jgt2012/article.pdf)) which is probably over complicated for educational porposes because have been higly optimized, but you will be happy to click on it and see that is less cryptic than you expect.
[  ](../edit.html#11/2d-snoise-clear.frag)
Well enought technicalities, is time for you to use this resource in your own expressive way:
* Contemplate how each noise implementation looks. Imagine them as a raw material. Like a marble rock for a sculptor. What you can say about about the "feeling" that each one have? Squinch your eyes to trigger your imagination, like when you want to find shapes on a cloud, What do you see? what reminds you off? How do you imagine each noise implementation could be model into? Following your guts try to make it happen on code.
Well in this chapter we have introduce some control over the chaos. Is not an easy job. Becoming a noise bender master takes time and efford. On the following chapters we will review some "well-know" techniques to perfect your skills and get more out of your noise functions to design generative content on shaders.
