One Dimensional Perlin noise vs Multi-Dimensional

Question

I am reading through a book called "The Nature of Coding" and I am working on porting the examples from Processing.js to Regular old Java. The example I am on uses the following method call in processing...

noise(x)

I noticed this class provided by Perlin himself, however, it is three dimensional. Can I just do something like this to make it one dimensional (seems too easy)...

static public double noise(double x) {
    return noise(x, 0, 0);
}

Or is there some other changes I have to make?

Answer 1

No, you should avoid doing that. 3D Perlin noise is 7 or 8 times as expensive as 1D Perlin noise. You'd be better off reimplementing the 1D function, because it's very simple. If I'm not mistaken:

static public double noise(double x) {
   int X = (int)Math.floor(x) & 255;
   x -= Math.floor(x);
   double u = fade(x);
   return lerp(u, grad(p[X  ], x  ),
                  grad(p[X+1], x-1));
}

static double grad(int hash, double x) {
   return ((hash&1) == 0 ? x : -x);
}

Answer 2

Noise along any of the axes will be consistent noise. Just imagine you're flying through a cloud of noise sampling a single line of data.

So yes, return noise(x, 0, 0); is exactly like traveling along a single line of noise inside a cloud of noise. You'll be traveling along the line that represents the x axis. You could even do return noise(x, x, x); and travel diagonally through the noise if you wanted to. The point being, as long as you're moving linearly through the noise you'll get consistent noise with consistent values of x.