29:null 2a:["$","title",null,{"children":"cSP v2 | Dominik's Workspace | Observable"}] 2f:Tab8,randomTimeWarp = { function NaturalCubicSpline(knots, values) { const n = knots.length - 1, t = new Float64Array(n + 3), // knots y = new Float64Array(n + 3), // values s = new Float64Array(n + 2), // horizontal scales m = new Float64Array(n + 3), // slope at each knot d = new Float64Array(n + 2), // diagonal matrix coeffs = new Float64Array(4 * (n + 2)); // Chebyshev coefficients t.set(knots, 1), y.set(values, 1); // Natural cubic spline algorithm for (let i = 1; i < n + 1; i++) { s[i] = 1 / (t[i + 1] - t[i]); m[i] += m[i + 1] = 3 * s[i] * s[i] * (y[i + 1] - y[i]); } d[1] = 0.5 / s[1]; m[1] = d[1] * m[1]; for (let i = 2; i <= n + 1; i++) { d[i] = 1 / (2 * (s[i] + s[i - 1]) - s[i - 1] * s[i - 1] * d[i - 1]); m[i] = d[i] * (m[i] - s[i - 1] * m[i - 1]); } for (let i = n; i >= 1; i--) { m[i] -= d[i] * s[i] * m[i + 1]; } // Linear extrapolation t[0] = t[1] - 1; t[n + 2] = t[n + 1] + 1; y[0] = y[1] - m[1]; y[n + 2] = y[n + 1] + m[n + 1]; s[0] = s[n + 1] = 1; m[0] = m[1]; m[n + 2] = m[n + 1]; // Set up Chebyshev coefficients for (let i = 0; i < n + 2; i++) { const h = t[i + 1] - t[i]; const y0 = y[i], y1 = y[i + 1], m0 = h * m[i], m1 = h * m[i + 1], j = 4 * i; coeffs[j] = 0.5 * (y0 + y1 + 0.125 * (m0 - m1)); coeffs[j + 1] = 0.03125 * (18 * (y1 - y0) - m0 - m1); coeffs[j + 2] = 0.0625 * (m1 - m0); coeffs[j + 3] = 0.03125 * (2 * (y0 - y1) + m0 + m1); } return Object.assign( function eval_spline(u) { // Binary search let nn = t.length - 1, low = 0, half; while ((half = (nn / 2) | 0) > 0) { low += half * (t[low + half] <= u); nn -= half; } const i = low, j = 4 * i; u = (2 * u - t[i] - t[i + 1]) * s[i]; // scale to [–1, 1] // Clenshaw's method. let b1 = coeffs[j + 3], b2 = coeffs[j + 2] + 2 * u * b1; b1 = coeffs[j + 1] + 2 * u * b2 - b1; return coeffs[j] + u * b1 - b2; }, { knots: t, values: y, dvalues: m, scales: s, coeffs: coeffs } ); } return (args) => { const defaultArgs = { pts: 2, mag: 0.5, y0: 0, y1: 1 }; var { pts, mag, y0, y1 } = { ...defaultArgs, ...args }; const xs = d3.range(pts + 2).map((x) => x / (pts + 1)); // np.arange(pts+2, dtype='float') / (pts+1) mag = (mag * (y1 - y0)) / 2 / (pts + 1); const ys = xs.map( (x, i) => y0 + (y1 - y0) * x + (i > 0 && i < pts + 1 ? d3.randomUniform(-mag, mag)() : 0) ); return NaturalCubicSpline(xs, ys); }; }30:T4dd,summary = Object.fromEntries( d3.map( d3.rollup( cspTable, (v) => ({ NN_err: d3.mean(v, (e) => e.NN_err), NN_within: d3.mean(v, (e) => (e.NN_err <= scatterSettings.tol ? 1 : 0)), Julkunen_err: d3.mean(v, (e) => e.Julkunen_err), Julkunen_within: d3.mean(v, (e) => e.Julkunen_err <= scatterSettings.tol ? 1 : 0 ), num: v.length }), (e) => e.type, (e) => e.split ), ([type, entries]) => [ type, Inputs.table( Array.from(entries.entries()).map(([split, entry]) => ({ split, ...entry })), { header: { split: "Split", type: "On/Off/Duration", NN_err: "NN mean error", NN_within: `NN within ${scatterSettings.tol}ms`, Julkunen_err: "Julkunen mean error", Julkunen_within: `Julkunen within ${scatterSettings.tol}ms` }, format: { NN_err: (v) => `${v.toFixed(1)}ms`, NN_within: (v) => `${(v * 100).toFixed(0)}%`, Julkunen_err: (v) => `${v.toFixed(1)}ms`, Julkunen_within: (v) => `${(v * 100).toFixed(0)}%` } } ) ] ) )31:T4e9,cspTable = Array.from(cspData.entries()) .flatMap(([key, { id, split, csp_label }]) => [ { id, split, type: "on", Label: csp_label[0] * 1000, NN: labelsNN.get(id).on.median * 1000, NN_std: labelsNN.get(id).on.std * 1000, NN_min: labelsNN.get(id).on.min * 1000, NN_max: labelsNN.get(id).on.max * 1000, Julkunen: labelsJulkunen.get(id)[0] * 1000 }, { id, split, type: "off", Label: csp_label[1] * 1000, NN: labelsNN.get(id).off.median * 1000, NN_std: labelsNN.get(id).off.std * 1000, NN_min: labelsNN.get(id).off.min * 1000, NN_max: labelsNN.get(id).off.max * 1000, Julkunen: labelsJulkunen.get(id)[1] * 1000 }, { id, split, type: "duration", Label: derivative(csp_label)[0] * 1000, NN: labelsNN.get(id).duration.median * 1000, NN_std: labelsNN.get(id).duration.std * 1000, NN_min: labelsNN.get(id).duration.min * 1000, NN_max: labelsNN.get(id).duration.max * 1000, Julkunen: derivative(labelsJulkunen.get(id))[0] * 1000 } ]) .map((entry) => ({ ...entry, NN_err: Math.abs(entry.NN - entry.Label), Julkunen_err: Math.abs(entry.Julkunen - entry.Label) }))32:T855,scatter = (name) => { const current_markers = scatterSettings.show == "Duration" ? ["duration"] : ["on", "off"]; var labelmd = ""; var labelwt = ""; if (name == "NN") { const md = d3.rollup( cspTable.filter((x) => current_markers.includes(x["type"])), (v) => d3.mean(v, (x) => x[name + "_err"]), (x) => x["split"] ); const wt = d3.rollup( cspTable.filter((x) => current_markers.includes(x["type"])), (v) => d3.mean(v, (x) => x[name + "_err"] <= scatterSettings.tol), (x) => x["split"] ); labelmd = `${md.get("train").toFixed(1)}/${md.get("test").toFixed(1)}`; labelwt = `${(wt.get("train") * 100).toFixed(0)}/${( wt.get("test") * 100 ).toFixed(0)}`; } else { labelmd = d3.mean(cspTable, (x) => x[name + "_err"]).toFixed(1); labelwt = ( d3.mean(cspTable, (x) => x[name + "_err"] <= scatterSettings.tol) * 100 ).toFixed(0); } return Plot.plot({ x: { label: "Manual cSP", domain: [0, 280] }, y: { label: name + " cSP", domain: [0, 280] }, aspectRatio: 1, width: 400, grid: true, title: "cSP", symbol: { legend: true }, color: { legend: true }, r: { legend: true }, subtitle: `mean deviation = ${labelmd}ms, within ${scatterSettings.tol}ms = ${labelwt}%`, marks: [ Plot.dot(cspTable, { y: (x) => x[name], x: (x) => x.Label, sort: (x) => x.id == sample.id, fill: (x) => (x.id == sample.id ? "red" : "white"), stroke: name == "Julkunen" ? "black" : "split", opacity: (x) => (x.id == sample.id ? 1 : 0.3), filter: (x) => current_markers.includes(x["type"]), symbol: "type", ...(name == "NN" ? { r: "NN_std" } : { r: 10 }) }), Plot.area([0.0, 300], { x1: (x) => x, y1: (x) => x - scatterSettings.tol, y2: (x) => x + scatterSettings.tol, opacity: 0.1 }), Plot.tip( cspTable, Plot.pointer({ y: (x) => x[name].toFixed(1), x: (x) => x["Label"].toFixed(1), channels: { Sample: "id" } }) ) ] }); }33:T9b1,timeline = (name) => { const entry = Object.fromEntries( ["duration", "on", "off"].map((typ) => [ typ, cspTable.find((x) => x.id == sample.id && x["type"] == typ) ]) ); console.log(entry); return Plot.plot({ marks: [ Plot.frame({ strokeOpacity: 0.1 }), ...(timelineSettings.show.includes("Signal") ? [Plot.lineY(cspData.get(sample.id).signal)] : []), ...(timelineSettings.show.includes("Derivative") ? [ Plot.lineY(derivative(cspData.get(sample.id).signal), { stroke: "blue" }) ] : []), Plot.ruleX([0]), ...(name == "Julkunen" && timelineSettings.show.includes("Derivative") ? [ Plot.ruleY([ (plot_julkunen_threshold / frequency) * 1000, (-plot_julkunen_threshold / frequency) * 1000 ]) ] : []), Plot.rect( [ { onset: entry.on["Label"], offset: entry.off["Label"], y: [0, 1], type: "Label" }, { onset: entry.on[name], offset: entry.off[name], y: [-1, 0], type: name == "NN" ? `${name} (${entry.on.split})` : name }, ...(name == "NN" ? [ { onset: entry.on.NN_min, offset: entry.on.NN_max, y: [-0.5, 0], type: "NN uncertainty" }, { onset: entry.off.NN_min, offset: entry.off.NN_max, y: [-0.5, 0], type: "NN uncertainty" } ] : []) ], { x1: (x) => (x.onset * frequency) / 1000, x2: (x) => (x.offset * frequency) / 1000, y1: (x) => x.y[0], y2: (x) => x.y[1], fill: "type", opacity: 0.2 } ) ], x: { type: "linear", domain: [0, timelineSettings.x], transform: (d) => d / frequency, label: "time (s)" }, y: { type: "linear", domain: [-0.005 / timelineSettings.y, 0.005 / timelineSettings.y] }, grid: true, color: { legend: true }, title: "Sample #" + sample.id, subtitle: `cSP: manual=${entry.duration["Label"].toFixed( 1 )}ms, ${name}=${entry.duration[name].toFixed(1)}ms` }); }34:[] 2d:{"id":"61ce68d174bd19cf","slug":null,"title":"cSP v2","thumbnail":"b4001c5f7e4f5dafc77589b537b70279fea3badf785c64ad13f4b330f99dc96d","access_level":"public","create_time":"$D2023-12-21T06:55:47.462Z","update_time":"$D2023-12-22T20:12:26.250Z","view_time":null,"first_public_time":"$D2023-12-21T06:55:47.462Z","fork":{"id":"34cd7cc815b1602a","slug":null,"title":"cSP","owner":{"id":"6fbae63626342e49","login":"dominik2-ws","name":"Dominik's Workspace"}},"stars":0,"starred":false,"owner":{"type":"team","id":"6fbae63626342e49","login":"dominik2-ws","name":"Dominik's Workspace","avatar_url":"https://avatars-next.observableusercontent.com/29d54f7b0ada4485ab66a5b753687a16ebd840cee3486735bc72600f3928a351","tier":"starter_2024"},"version":1444,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"8d3a001f2b495452","login":"dominik2","name":"Dominik","avatar_url":"https://avatars-next.observableusercontent.com/2be6a595e5ee20eb59db0ea25f43e8e6d39f847748b68407f16331b31d5d6d82","tier":null}],"members":[{"type":"individual","id":"8d3a001f2b495452","login":"dominik2","name":"Dominik","avatar_url":"https://avatars-next.observableusercontent.com/2be6a595e5ee20eb59db0ea25f43e8e6d39f847748b68407f16331b31d5d6d82","tier":null,"role":"editor"},{"type":"team","id":"6fbae63626342e49","login":"dominik2-ws","name":"Dominik's Workspace","avatar_url":"https://avatars-next.observableusercontent.com/29d54f7b0ada4485ab66a5b753687a16ebd840cee3486735bc72600f3928a351","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"cSP v2","cells":[{"id":0,"value":"# cSP v2","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1136,"value":"Choose the training data set\n\n- Katrin: 80% training, 20% testing (André: 100% testing)\n- André: 80% training, 20% testing (Katrin: 100% testing)\n- All: 80% training, 20% testing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1011,"value":"viewof trainingFile = Inputs.select([\"All\", \"Katrin\", \"Andre\"], {\n label: \"Training Data\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1134,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":71,"value":"viewof sample = Inputs.select(Array.from(cspData.values()), {\n label: \"Sample ID\",\n format: (x) => x.id\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":612,"value":"viewof timelineSettings = Inputs.form({\n x: Inputs.range([0.3, 1], { step: 0.05, label: \"x-Range (s)\", value: 0.5 }),\n y: Inputs.range([0.5, 100], { step: 0.1, label: \"y-Scale\", value: 1 }),\n show: Inputs.checkbox([\"Signal\", \"Derivative\"], {\n label: \"Plot\",\n value: [\"Signal\"]\n })\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":611,"value":"viewof scatterSettings = Inputs.form({\n tol: Inputs.range([1, 20], {\n step: 1,\n label: \"Duration tolerance (ms)\",\n value: 10\n }),\n show: Inputs.radio([\"On/Off\", \"Duration\"], {\n label: \"Plot\",\n value: \"Duration\"\n })\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":492,"value":"\n \n \n \n

\n ${timeline(\"NN\")}\n

\n ${scatter(\"NN\")}\n

\n \n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":327,"value":"viewof plot_julkunen_threshold = Inputs.range([.000001, .0001], {step: .000001, label: \"Julkunen threshold\",value: .00001})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":367,"value":"\n \n \n \n

\n ${timeline(\"Julkunen\")}\n

\n ${scatter(\"Julkunen\")}\n

\n \n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":1027,"value":"Inputs.table(cspLabelsCombined, {\n columns: [\n \"id\",\n \"split\",\n \"csp_label\",\n \"NN_median\",\n \"NN_std\",\n \"NN_error\",\n \"Julkunen\",\n \"Julkunen_error\"\n ],\n header: {\n id: \"Sample\",\n split: \"Split\",\n csp_label: \"Label\",\n NN_median: \"NN\",\n NN_std: \"±\",\n NN_error: \"Err\",\n Julkunen_error: \"Err\"\n },\n format: {\n NN_median: durationFormat,\n csp_label: durationFormat,\n Julkunen: durationFormat,\n NN_error: (x) => (x * 1000).toFixed(1),\n Julkunen_error: (x) => (x * 1000).toFixed(1),\n NN_std: (x) => (x * 1000).toFixed(1),\n Error: (x) => (x * 1000).toFixed(1)\n },\n width: {\n id: 80,\n split: 50,\n NN_std: 30,\n NN_median: 160,\n Julkunen: 160,\n csp_label: 160,\n NN_error: 40,\n Julkunen_error: 40\n },\n sort: {\n NN_median: (x) => x[1] - x[0],\n csp_label: (x) => x[1] - x[0],\n Julkunen: (x) => x[1] - x[0]\n },\n multiple: false,\n required: false\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1182,"value":"## Summary","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1186,"value":"

Duration

${summary.duration}

Onset

${summary.on}

Offset

${summary.off}

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":1274,"value":"bar(\"NN\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1343,"value":"bar(\"Julkunen\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":132,"value":"---\n# Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":68,"value":"frequency = 3000","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":607,"value":"cspData = {\n const request = await fetch(\n `https://wirhabenzeit.github.io/csp/${trainingFile.toLowerCase()}_data.json`\n );\n var data = await request.json();\n var collator = new Intl.Collator(undefined, {\n numeric: true,\n sensitivity: \"base\"\n });\n data = data.sort((a, b) => collator.compare(a.id, b.id));\n return new Map(data.map((entry) => [entry.id, entry]));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":605,"value":"## Julkunen Prediction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":259,"value":"derivative = (signal) =>\n signal.slice(1, signal.length).map((x, i) => x - signal[i])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":263,"value":"julkunenPrediction = (\n threshold = 0.0225,\n freq = 3000,\n minLen = 0.03,\n maxTime = 0.3\n ) =>\n (signal) => {\n const diff = derivative(signal).map((x) => Math.abs(x * freq * 1e-3));\n const minInd = Math.round(minLen * freq);\n const windowsOfSmallDerivative = [];\n var [i, j] = [0, 0];\n while (j < Math.min(diff.length, maxTime * freq)) {\n if (Math.abs(diff[j]) >= threshold) {\n if (j - i > minInd) windowsOfSmallDerivative.push([i, j - 1]);\n i = j + 1;\n j = i;\n } else j += 1;\n }\n if (j - i > minInd) windowsOfSmallDerivative.push([i, j - 1]);\n if (windowsOfSmallDerivative.length == 0) return [0, 0];\n return windowsOfSmallDerivative\n .sort((a, b) => d3.descending(a[1] - a[0], b[1] - b[0]))[0]\n .map((x) => x / freq);\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":395,"value":"labelsJulkunen = new Map(\n Array.from(cspData, ([key, entry]) => [\n key,\n julkunenPrediction(plot_julkunen_threshold)(entry.signal)\n ])\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":602,"value":"## Neural Network Prediction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":601,"value":"modelDimension = 256","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":617,"value":"modelPrediction = {\n const ort =\n await require(\"https://cdn.jsdelivr.net/npm/onnxruntime-web@1.15.1/dist/ort.js\");\n ort.env.wasm.wasmPaths =\n \"https://cdn.jsdelivr.net/npm/onnxruntime-web@1.15.1/dist/\";\n const executionProviders = [\"wasm\"];\n const session = await ort.InferenceSession.create(\n `https://wirhabenzeit.github.io/csp/${trainingFile.toLowerCase()}_data_xresnet1d18plus_256.onnx`,\n { executionProviders }\n );\n return async (signals) => {\n const bs = signals.length;\n const length = signals[0].length;\n const results = await session.run({\n input: new ort.Tensor(\"float32\", signals.flat(), [bs, 1, length])\n });\n const arr = Array.from(results.output.data);\n return d3.range(bs).map((i) => arr.slice(2 * i, 2 * i + 2));\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":599,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":598,"value":"randomWarpedSignals = (signal, copies, twArgs) => {\n return d3.range(copies).map(() => {\n const tw = randomTimeWarp(twArgs);\n const mwHelper = randomTimeWarp({\n ...(\"pts\" in twArgs ? { pts: twArgs.pts } : {}),\n ...(\"mag\" in twArgs ? { mag: twArgs.mag } : {})\n });\n const mw = (t) => 1 + mwHelper(t) - t;\n const sig = Array.from(\n d3\n .range(modelDimension)\n .map(\n (i) =>\n 1000 *\n signal[Math.round(tw(i / (modelDimension - 1)) * frequency)] *\n mw(i / (modelDimension - 1))\n )\n );\n return { tw, sig };\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":597,"value":"augmentedModelPrediction = async (signal, copies, twArgs) => {\n const augSignals = randomWarpedSignals(signal, copies, twArgs);\n const rawResults = await modelPrediction(augSignals.map((x) => x.sig));\n const transformedResults = rawResults.map((vec, i) =>\n vec.map(augSignals[i].tw)\n );\n const funcs = {\n on: (x) => x[0],\n off: (x) => x[1],\n duration: (x) => x[1] - x[0]\n };\n const stats = {\n mean: d3.mean,\n median: d3.median,\n std: d3.deviation,\n min: d3.min,\n max: d3.max\n };\n return Object.fromEntries(\n Object.entries(funcs).map(([label, func]) => [\n label,\n Object.fromEntries(\n Object.entries(stats).map(([stat, statFunc]) => [\n stat,\n statFunc(transformedResults, func)\n ])\n )\n ])\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":596,"value":"labelsNN = {\n const labels = await Promise.all(\n Array.from(cspData, ([entry, x]) =>\n augmentedModelPrediction(x.signal, 6, {\n pts: 3,\n mag: 0.2,\n y0: 0,\n y1: 0.4\n })\n )\n );\n return new Map(Array.from(cspData, ([key, entry], i) => [key, labels[i]]));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":595,"value":"## Data with predicitons ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1174,"value":"cspLabelsCombined = Array.from(\n cspData.entries(),\n ([key, { csp_label, id, split }]) => ({\n id,\n split,\n csp_label,\n NN_median: [labelsNN.get(id).on.median, labelsNN.get(id).off.median],\n NN_std: labelsNN.get(id).duration.std,\n Julkunen: labelsJulkunen.get(id),\n NN_error: Math.abs(\n labelsNN.get(id).off.median -\n labelsNN.get(id).on.median -\n csp_label[1] +\n csp_label[0]\n ),\n Julkunen_error: Math.abs(\n labelsJulkunen.get(id)[1] -\n labelsJulkunen.get(id)[0] -\n csp_label[1] +\n csp_label[0]\n )\n })\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1219,"value":"$30","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1085,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1038,"value":"durationFormat = (x) =>\n `${(x[0] * 1000).toFixed(1)}-${(x[1] * 1000).toFixed(1)}ms (${(\n (x[1] - x[0]) *\n 1000\n ).toFixed(1)}ms)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":592,"value":"## Plots","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1419,"value":"function set(input, value) {\n input.value = value;\n input.dispatchEvent(new Event(\"input\", { bubbles: true }));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":590,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":589,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1351,"value":"bar = (name) => {\n return Plot.plot({\n title: name,\n width: 1200,\n marginLeft: 80,\n marginRight: 80,\n x: { domain: [0, 150] },\n marks: [\n Plot.barX([{ split: \"exclude\" }, { split: \"test\" }, { split: \"train\" }], {\n x1: 0,\n x2: scatterSettings.tol,\n opacity: 0.1,\n y: \"split\"\n }),\n Plot.tickX(cspTable, {\n x: name + \"_err\",\n y: \"split\",\n opacity: (e) => (e.id == sample.id ? 1 : 0.2),\n stroke: (e) => (e.id == sample.id ? \"red\" : \"blue\"),\n fy: \"type\",\n channels: { sample: \"id\" },\n tip: true\n })\n ]\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W34","stdlib":"1","titling":"auto"}} 2e:"$2d:body"