2c:null 2d:["$","title",null,{"children":"Leafs Shots Gaussian Smoothing | hockeyfan22 | Observable"}] 32:[{"id":"3e129d5caa76b5a888484d31e6a144b542d99e97dff7868067ac08924d4c6aa165a2bbed5df92e30857e985efe8daf7d18511754a29488718e7ad6644ee0a4d3","href":"https://static.observableusercontent.com/files/3e129d5caa76b5a888484d31e6a144b542d99e97dff7868067ac08924d4c6aa165a2bbed5df92e30857e985efe8daf7d18511754a29488718e7ad6644ee0a4d3","name":"stream.csv","type":"text/csv","lastModified":1608751127866.685,"size":1075262}] 30:{"id":"f59ee24db515ce09","slug":"leafs-shots-gaussian-smoothing","title":"Leafs Shots Gaussian Smoothing","thumbnail":"64d89291a28956ac4322879665e64812d6ea16251920e7254e74cdf299bd07f1","access_level":"public","create_time":"$D2020-12-23T19:10:16.724Z","update_time":"$D2020-12-30T01:06:09.337Z","view_time":null,"first_public_time":"$D2020-12-30T01:06:11.026Z","has_unpublished_edits":null,"fork":{"id":"49cee47a73a1a9f0","slug":"gaussian-smoothing","title":"Gaussian Smoothing","owner":{"id":"da2a32154638c457","login":"fil","name":"Fil"}},"stars":0,"starred":false,"owner":{"type":"team","id":"f6312d5e27b60ae4","login":"hockeyfan22","name":"","avatar_url":"https://avatars-next.observableusercontent.com/b196aa13898620d3d65f810ae0e355832041b6e0927adb9f43541dbb9752c0a3","tier":"starter_2024"},"version":378,"publish_version":378,"role":null,"authored":false,"authors":[{"type":"individual","id":"bb4ef8ba640bc1af","login":"hockeyfan22","name":"","avatar_url":"https://avatars-next.observableusercontent.com/b196aa13898620d3d65f810ae0e355832041b6e0927adb9f43541dbb9752c0a3","tier":null}],"members":[{"type":"individual","id":"bb4ef8ba640bc1af","login":"hockeyfan22","name":"","avatar_url":"https://avatars-next.observableusercontent.com/b196aa13898620d3d65f810ae0e355832041b6e0927adb9f43541dbb9752c0a3","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Leafs Shots Gaussian Smoothing","cells":[{"id":0,"value":"md`# Leafs Shots Gaussian Smoothing\n\n${N}-hours smoothing of electricity usage, using a simple Gaussian kernel.\n
Data: [Mike Bostock](/@mbostock/electric-usage-2019).\n\n_Note: in this version the time steps are uniform and there are no holes in the data. Please send suggestions._`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":122,"value":"viewof N = {\n const form = html``;\n form.i.oninput = () => (form.value = form.i.valueAsNumber);\n form.i.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":203,"value":"viewof compareWithMM = {\n const form = html``;\n form.i.oninput = () => (form.value = form.i.checked);\n form.i.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"chart = {\n const svg = d3.create(\"svg\").attr(\"viewBox\", [0, 0, width, height]);\n\n const color = d3\n .scaleLinear()\n .domain([3, 0, -1])\n .range(d3.schemeRdYlBu[3]),\n gradient = DOM.uid();\n svg\n .append(\"linearGradient\")\n .attr(\"id\", gradient.id)\n .attr(\"gradientUnits\", \"userSpaceOnUse\")\n .attr(\"x1\", 0)\n .attr(\"y1\", y(color.domain()[0]))\n .attr(\"x2\", 0)\n .attr(\"y2\", y(color.domain()[2]))\n .selectAll(\"stop\")\n .data(d3.range(5))\n .join(\"stop\")\n .attr(\"offset\", i => i / 4)\n .attr(\"stop-color\", i => color(3 - i));\n\n svg\n .append(\"path\")\n .attr(\"fill\", gradient)\n .attr(\"d\", area(values));\n\n svg.append(\"g\").call(xAxis);\n svg.append(\"g\").call(yAxis);\n\n if (compareWithMM)\n svg\n .append(\"path\")\n .attr(\"fill\", \"none\")\n .attr(\"stroke\", \"steelblue\")\n .attr(\"stroke-width\", .5)\n .attr(\"d\", line(mm));\n\n return svg.node();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":245,"value":"precision = 1e-6","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":185,"value":"function applyKernel(points, w) {\n const values = new Float64Array(points.length).fill(0),\n total = new Float64Array(points.length).fill(0);\n let p = 1;\n for (let d = 0; p > precision; d++) {\n p = w(d);\n for (let i = 0; i < points.length; i++) {\n if (i + d < points.length) {\n values[i + d] += p * points[i];\n total[i + d] += p;\n }\n if (d != 0 && i - d >= 0) {\n values[i - d] += p * points[i];\n total[i - d] += p;\n }\n }\n }\n for (let i = 0; i < values.length; i++) {\n values[i] /= total[i];\n }\n return values;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":187,"value":"function gaussianSmoothing(values, N) {\n const r = 2 / N;\n return applyKernel(values, d => Math.exp(-((r * d) ** 2)));\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"function movingAverage(values, N) {\n let i = 0;\n let sum = 0;\n const means = new Float64Array(values.length).fill(NaN);\n for (let n = Math.min(N - 1, values.length); i < n; ++i) {\n sum += values[i];\n }\n for (let n = values.length; i < n; ++i) {\n sum += values[i];\n means[i] = sum / N;\n sum -= values[i - N + 1];\n }\n return means;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"values = gaussianSmoothing(data.map(d => d.count), N)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":216,"value":"mm = movingAverage(data.map(d => d.count), N)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":294,"value":"md`----\n_Thanks to [Jeshurun Hembd](/@jjhembd) for fixing an egregious mistake._`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":351,"value":"data = aq\n .fromCSV(await FileAttachment('stream.csv').text())\n .filter(d => d.event_team == 'TOR')\n .objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":353,"value":"import { aq, op } from '@uwdata/arquero'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":115,"value":"area = d3\n .area()\n .defined(d => !isNaN(d))\n .x((d, i) => x(data[i].game_seconds))\n .y0(y(0))\n .y1(y)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"line = d3\n .line()\n .defined(d => !isNaN(d))\n .x(d => x(d.game_seconds))\n .y(d=> y(mm))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"x = d3\n .scaleLinear()\n .domain(d3.extent(data, d => d.game_seconds))\n .range([margin.left, width - margin.right])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"y = d3\n .scaleLinear()\n .domain(d3.extent(mm))\n .nice()\n .rangeRound([height - margin.bottom, margin.top])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"xAxis = g => g\n .attr(\"transform\", `translate(0,${height - margin.bottom})`)\n .call(d3.axisBottom(x).tickSizeOuter(0))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"yAxis = g => g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .call(d3.axisLeft(y))\n .call(g => g.select(\".domain\").remove())\n .call(g => g.selectAll(\".tick line\").clone()\n .attr(\"x2\", width)\n .attr(\"stroke-opacity\", 0.1))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"height = 250","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"margin = ({top: 20, right: 12, bottom: 30, left: 60})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"d3 = require(\"d3@5\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W32","stdlib":"1"}} 31:"$30:body"