deleted

src/experimental/_impl/_worley_noise_impl.scad

@@ -1,6 +0,0 @@
-use <experimental/sum.scad>;
-
-function _manhattan(v) = sum([for(d = v) abs(d)]);
-
-function _chebyshev(p1, p2) =
-    max([for(i = [0:len(p1) - 1]) abs(p1[i] - p2[i])]);

src/experimental/demo/worley_noise_demo.scad

@@ -1,24 +0,0 @@
-use <experimental/zip.scad>;
-use <experimental/worley_noise.scad>;
-
-size = [50, 50];
-n = 50;
-xs = rands(0, size[0] - 1, n);
-ys = rands(0, size[1] - 1, n);
-points = zip([xs, ys]);
-
-noised = [
-    for(y = [0:size[1] - 1]) 
-        for(x = [0:size[0] - 1]) 
-            [x, y, worley_noise([x, y], points)]
-];
-
-max_dist = max([for(n = noised) n[2]]);
-
-for(n = noised) {
-    c = n[2] / max_dist;
-    color([c, c, c])
-    linear_extrude(c * max_dist)
-    translate([n[0], n[1]])
-        square(1);
-}

src/experimental/worley_noise.scad

@@ -1,10 +0,0 @@
-use <experimental/_impl/_worley_noise_impl.scad>;
-
-function worley_noise(p, points, dist = "euclidean") =
-    let(
-        dists = dist == "euclidean" ? [for(i = [0:len(points) - 1]) norm(points[i] - p)] :
-                dist == "manhattan" ? [for(i = [0:len(points) - 1]) _manhattan(points[i] - p)] :
-                dist == "chebyshev" ? [for(i = [0:len(points) - 1]) _chebyshev(points[i], p)] : 
-                                      assert("Unknown distance option")
-    )
-    min(dists);