Hello, spectral.jsPseudo-blue noise shaderHow fast does walk-on-spheres converge?KDE estimationSpherical Perlin NoiseHull paddingDithered heatmapMean value coordinatesHeatmapPoisson potentialK-means binningImage FFTTransport to a mapSquircle interpolatorDisc TransportAn approximate formula for the chord area inverse problemWalk on SpheresnoFrame contoursHue blurMoving average blurFitting a sigmoidGaussian SmoothingPlateau detectionHello, LOESSLinde–Buzo–Gray stipplinginterpolateBalanced?Hello, delatinHillshading & supersamplingSpherical contours iteratorSpherical contours as a gradientGeo Voronoi interpolationModified Shepard’s method II (with geoContour)MapfillSpherical contoursModified Shepard’s methodShepard’s methodSpherical kernel interpolation with nearest neighborsSpherical HeatmapKrigingSpherical KDE Interpolation
Blurry contours
blur = (d, i) =>
d3.median([i, ...triangulation.neighbors(i)], j => points[j][2])
points = d3.json(
"https://gist.githubusercontent.com/Fil/6bc12c535edc3602813a6ef2d1c73891/raw/3ae88bf307e740ddc020303ea95d7d2ecdec0d19/points.json"
)
triangulation = {
const c = d3.geoContour();
c.contour(points, 0); // compute a single contour to force the computation of the triangulation
return c._triangulation();
}
contour = d3
.geoContour()
.thresholds(100)
.value(!plain ? blur : d => d[2])
contours = ({
type: "FeatureCollection",
features: [{ type: "Sphere", value: -50 }, ...contour(points)]
})
color = d3.scaleSequential(d3.interpolateRdBu).domain([40, -20])
projection = d3
.geoArmadillo()
.fitExtent([[0, 0], [width, height]], { type: "Sphere" })
land = d3.json(
"https://unpkg.com/visionscarto-world-atlas/world/110m_land.geojson"
)
d3 = require("d3@7", "d3-geo-voronoi@2", "d3-geo-projection@3")
height = ((width * 2) / 3) | 0
import {checkbox} from "@jashkenas/inputs"
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.