31:T406,m = { const scale = 1.75 const context = DOM.context2d(data.width * scale, data.height * scale); context.lineCap = "round"; context.lineJoin = "round"; for (let m = 0; true; m = (m + 1) % M) { context.save(); context.fillStyle = "rgba(255, 255, 255, .1)"; context.fillRect(0, 0, data.width, data.height); context.scale(scale, scale) for(let x = 0; x < dftArray.length; x++) { const thisPath = dftArray[x]; context.beginPath(); for (let t = 0; t < q; ++t) { const a = t * 2 / q * Math.PI; let p = [0, 0]; for (let i = 0; i < m; ++i) { p = add(p, mul(thisPath.DFT[i], expim(a * thisPath.K[i]))); } if (t === 0) context.moveTo(...p); else context.lineTo(...p); } context.closePath(); context.fillStyle = colorScale(x) context.strokeStyle = colorScale(2) context.fill(); context.stroke(); } context.restore(); context.canvas.value = m; yield context.canvas; } }32:T480,{ const scale = 1.25; const div = html`

` d3.select(div).style("text-align", 'left') const mArray = [2, 10, 20, 30, 40, 50, M] mArray.forEach(m => { const canvas = d3.select(div).append("canvas").attr("width", data.width * scale).attr("height", data.height * scale); const context = canvas.node().getContext('2d'); context.scale(scale, scale); context.lineCap = "round"; context.lineJoin = "round"; context.fillStyle = "rgba(255, 255, 255, .1)"; context.fillRect(0, 0, data.width, data.height); for(let x = 0; x < dftArray.length; x++) { const thisPath = dftArray[x]; context.beginPath(); for (let t = 0; t < q; ++t) { const a = t * 2 / q * Math.PI; let p = [0, 0]; for (let i = 0; i < m; ++i) { p = add(p, mul(thisPath.DFT[i], expim(a * thisPath.K[i]))); } if (t === 0) context.moveTo(...p); else context.lineTo(...p); } context.closePath(); context.fillStyle = colorScale(x) context.strokeStyle = colorScale(2) context.fill(); context.stroke(); } }) return div }33:T2ce3,palette = extractPalette("data:image/jpeg;base64,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")34:[{"id":"dca7f6368b61c93d432b42d1472c09f3edf99f66ac9c6524dbce49560069a4a1fdaa173aa71b18e5786281cfaa0627dd61974f16690589d2148985c1b43585d0","href":"https://static.observableusercontent.com/files/dca7f6368b61c93d432b42d1472c09f3edf99f66ac9c6524dbce49560069a4a1fdaa173aa71b18e5786281cfaa0627dd61974f16690589d2148985c1b43585d0","name":"lou.jpg","type":"image/jpeg","lastModified":1593049045526.929,"size":81055}] 2f:{"title":"Lou Reed, The Bells - Fourier Series","cells":[{"id":0,"value":"md`# Lou Reed, The Bells - Fourier Series`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":125,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"colorScale = d3.scaleOrdinal()\n // .range([\"#000\", \"#C40017\", \"#E6DB31\",\"#fff\"])\n // .domain([0, 1, 2, 3])\n .range(palette)\n .domain(d3.range(palette).map(d => d))","pinned":false,"hidden":false,"mode":"ojs"},{"id":58,"value":"md`## Data Processing`","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"dftArray = generateDFTArray(svgData)","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"N = 1000","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"M = 100","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"q = 1000","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"svgData = potrace.traceCanvas(data.canvas.canvas)","pinned":false,"hidden":false,"mode":"ojs"},{"id":21,"value":"data = {\n let img = await FileAttachment(\"lou.jpg\").image();\n let ctx = DOM.canvas(img.width, img.height).getContext('2d');\n ctx.drawImage(img, 0, 0);\n return {\n image: ctx.getImageData(0, 0, img.width, img.height).data,\n width: img.width,\n height: img.height,\n canvas: ctx\n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":227,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":52,"value":"md`#### Original`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":" FileAttachment(\"lou.jpg\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":67,"value":"md`## Imported Modules`","pinned":false,"hidden":false,"mode":"ojs"},{"id":23,"value":"potrace = require(\"potrace-js-dev@0.0.3/lib/dist/index.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"import { PathSampler, abs, expim, add, mul, L, C } from '@pstuffa/image-to-progressive-fourier-series'","pinned":false,"hidden":false,"mode":"ojs"},{"id":224,"value":"import { extractPalette } from '@makio135/give-me-colors'","pinned":false,"hidden":false,"mode":"ojs"},{"id":25,"value":"d3 = require(\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"function* generateDFTArray(input) {\n \n const filter = input.filter(d => d.area > 20 && d.m > 15);\n const data = filter.map(d => `M ${d.points.map(e => `${e.x} ${e.y}`).join(\"L \")} Z`)\n console.log(data)\n const output = []\n for(let i = 0; i < data.length; i++) {\n const array = data[i]\n const sampler = new PathSampler(array);\n const P = Array.from({length: N}, (_, i) => sampler.pointAt(i / N));\n const K = Int16Array.from({length: M}, (_, i) => (1 + i >> 1) * (i & 1 ? -1 : 1));\n const DFT = Array.from(K, k => {\n let x = [0, 0];\n for (let i = 0; i < N; ++i) {\n x = add(x, mul(P[i], expim(k * i * 2 / N * -Math.PI)));\n }\n return [x[0] / N, x[1] / N];\n })\n output.push({\n P: P,\n K: K,\n DFT: DFT,\n sampler: sampler,\n array: array\n })\n }\n \n yield output\n\n}","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W34","stdlib":"1","titling":"auto"}