color = getColor(angle)
q = 3 // The level of detail, e.g., sample every 4 pixels in x and y.
period = 0.008
n = Math.ceil(width / q) + 1
m = Math.ceil(height / q) + 1
projection = d3.geoIdentity().translate([-q / 2, -q / 2]).scale(q)
contours = d3
.contours()
.size([n, m])
noise = (regenerate, new Noise(3))
class Noise {
static lerp(t, a, b) {
return a + t * (b - a);
}
static grad2d(i, x, y) {
const v = (i & 1) === 0 ? x : y;
return (i & 2) === 0 ? -v : v;
}
constructor(octaves = 1) {
this.p = new Uint8Array(512);
this.octaves = octaves;
this.init();
}
init() {
for (let i = 0; i < 512; ++i) {
this.p[i] = Math.random() * 256;
}
}
noise2d(x2d, y2d) {
const X = Math.floor(x2d) & 255;
const Y = Math.floor(y2d) & 255;
const x = x2d - Math.floor(x2d);
const y = y2d - Math.floor(y2d);
const fx = (3 - 2 * x) * x * x;
const fy = (3 - 2 * y) * y * y;
const p0 = this.p[X] + Y;
const p1 = this.p[X + 1] + Y;
return Noise.lerp(
fy,
Noise.lerp(
fx,
Noise.grad2d(this.p[p0], x, y),
Noise.grad2d(this.p[p1], x - 1, y)
),
Noise.lerp(
fx,
Noise.grad2d(this.p[p0 + 1], x, y - 1),
Noise.grad2d(this.p[p1 + 1], x - 1, y - 1)
)
);
}
noise(x, y) {
let e = 1,
k = 1,
s = 0;
for (let i = 0; i < this.octaves; ++i) {
e *= 0.5;
s += (e * (1 + this.noise2d(k * x, k * y))) / 2;
k *= 2;
}
return s;
}
}
d3 = require("d3@6")
film = d3.image(
"https://raw.githubusercontent.com/amandaghassaei/SoapFlow/main/dist/lookup.png",
{ crossOrigin: "anonymous" }
)
import { height } from "@fil/height"
import { Scrubber } from "@mbostock/scrubber"
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
