Chandrupatla’s root-finding methodSidi’s root-finding methodRegular numbersDruidJS workerNatural breaksDistance to a segmentRay out of a convex hullWord Tour: 40k words and their friendsHello, @thi.ng/grid-iteratorsHead/tail breaksPseudo-blue noise shaderHow fast does walk-on-spheres converge?AoC 12: shortest path under constraintsKDE estimationPlot: Correlation heatmapPoisson Finish 2Poisson disk sampling functionsWoS with transportSimple and surprising sortLocal medianTime series topological subsamplingUnion-FindLevel set experiment 1Mean value coordinatesPoisson potentialMiddle-squareWorld of squares (spherical)World of squaresLargest Inscribed SquareHello, PyWaveletsGeothmetic meandianHello, Reorder.jsGeometric MedianImage FFTTransport to a mapDisc TransportTP3: Power Diagram and Semi-Discrete Optimal TransportThe blue waveHello, genetic-jsSliced Optimal TransportDruidJSSelf-Organizing Maps meet DelaunayHello, polygon-clippingseedrandom, minimalWalk on Spheres 2Walk on SpheresHello, AutoencoderKaprekar’s numberVoronoiMap2DHello, ccwt.jsPolygon TriangulationQuantile.invert?Linear congruential generatorHue blurNeedle in a haystackMoving average blurApollo 11 implementation of trigonometric functions, by Margaret H. Hamilton (march 1969)2D curves intersectionThe 2D approximate Newton-Raphson methodInverting Lee’s Tetrahedral projectionLinde–Buzo–Gray stippling2D point distributionsShortest pathKahan SummationHello, delatinDijkstra’s algorithm in gpu.jsLloyd’s relaxation on a graphManhattan DiameterManhattan VoronoiMobility landscapes — an introductionDijkstra’s shortest-path treeH3 odditiesProtein MatrixConvex Spectral WeightsSort stuff by similarityKrigingDelaunay.findTrianglen-dimensions binning?Travelling with a self-organizing mapUMAP-o-MaticMNIST & UMAP-jsHello UMAP-jsMean shift clusteringLevenshtein transitionRd quasi-random sequencesAutomated label placement (countries)Phyllotaxis explainedMotionrugsPlanar hull (Andrew’s monotone chain algorithm)South Africa’s medial axisTravelling salesperson approximation with t-SNEDistance to shoreWorkerngraph: pagerank, louvain…t-SNE VoronoiCloud ContoursCircular function drawingKruskal MazeMyceliumTravelling salesperson approximation on the globe, with t-SNEtsne.jstsne.js & worker
Mean shift clustering with kd-tree
Also listed in…
Clustering
distance = (a, b) => Math.hypot(a[0] - b[0], a[1] - b[1])
kernel = d => Math.exp(-0.5 * (d / sigma) ** 2)
function* meanshift({ points, tree, sigma, kn, delta }) {
const pos = Array.from(points, p => Object.create(p));
delta = delta || 1e-3;
kn = kn || 100;
do {
pos.delta = 0;
for (let i = 0; i < pos.length; i++) {
const p = pos[i],
q = p.map(_ => 0);
let weight = 0;
const nn = tree.nearest(p, kn);
for (let j = 0; j < nn.length; j++) {
const ip = nn[j][0],
d = nn[j][1],
w = kernel(d) * (nn[j][0].weight || 1);
weight += w;
for (let k = 0; k < q.length; k++) q[k] += w * ip[k];
}
for (let k = 0; k < q.length; k++) q[k] /= weight;
pos.delta = Math.max(pos.delta, distance(pos[i], q));
pos[i] = q;
}
yield pos;
} while (pos.delta > delta);
}
points = Array.from({ length: N, replay }).map((_, i) => [
i % 2
? Math.random()
: (Math.random() - 0.5) / 4.5 + (0.5 + Math.floor(3 * Math.random())) / 3,
i % 2
? Math.random()
: (Math.random() - 0.5) / 4.5 + (0.5 + Math.floor(3 * Math.random())) / 3
])
kd = require("kd-tree-javascript@1")
palette = d3.schemePastel2.concat(d3.schemePastel1)
d3 = require("d3@7")
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.