25:["$","title",null,{"children":"PAr 106 SwimAnalysis : Visualisation données de courses | benjamindenise | Observable"}] 49:T75a,CycleCumul = {let ListeFinale = [] let Nageur = CycleparNageurs.select("courses.id","prenom","nageurs.nom",'competitions.nom','rounds.libelle') .reify().objects() .map(d=> [d['courses.id'],d.prenom,d['nageurs.nom'],d["competitions.nom"],d['rounds.libelle']]) for (let i = 0; i < Nageur.length; i++) { let X = Nageur[i]; let [id,Prenom,nom,competition,round] = X; let Tps = CycleparAnalyse.select("tpsFinCycles","courses.id","prenom","nageurs.nom") .filter(aq.escape(d => d['courses.id'] == id & d.prenom == Prenom & d['nageurs.nom'] == nom)) .reify().objects().map(d=> d.tpsFinCycles); let Dst = CycleparAnalyse.select("distanceDebut","courses.id","prenom","nageurs.nom") .filter(aq.escape(d => d['courses.id'] == id & d.prenom == Prenom & d['nageurs.nom'] == nom)) .reify().objects().map(d=> d.distanceDebut); let NbrCycle = CycleparAnalyse.select("NbrCycles","courses.id","prenom","nageurs.nom") .filter(aq.escape(d => d['courses.id'] == id & d.prenom == Prenom & d['nageurs.nom'] == nom)) .reify().objects().map(d=> d.NbrCycles); let N = 0 //On initie le cumulé let freq = 0 for (let j = 0; j [d['courses.id'],d.prenom,d['nageurs.nom'],d["competitions.nom"],d['rounds.libelle']]) for (let i = 0; i < Nageur.length; i++) { let X = Nageur[i]; let [id,Prenom,nom,competition,round] = X; let tempsCycle = CycleparAnalyse.select("tpsFinCycles","courses.id","prenom","nageurs.nom") .filter(aq.escape(d => d['courses.id'] == id & d.prenom == Prenom & d['nageurs.nom'] == nom)) .reify().objects().map(d=> d.tpsFinCycles); let MvtCumule = 0; let Tps = time_distance.select("temps","courses.id","prenom","nageurs.nom") .filter(aq.escape(d => d['courses.id'] == id & d.prenom == Prenom & d['nageurs.nom'] == nom)) .reify().objects().map(d=> d.temps); let NbrCycle = CycleparAnalyse.select("NbrCycles","courses.id","prenom","nageurs.nom") .filter(aq.escape(d => d['courses.id'] == id & d.prenom == Prenom & d['nageurs.nom'] == nom)) .reify().objects().map(d=> d.NbrCycles); let NCycle = 0 let CompteurCycle = 1; for (let j = 0; j d.distanceDebut == 0)) //on mets ce filtre juste pour avoir le nageur une seule fois .reify().objects() .map(d=> [d['courses.id'],d.prenom,d['nageurs.nom'],d["competitions.nom"],d['rounds.libelle']]) let Dist = DistCoulee; for (let i = 0; i < Nageur.length; i++) { let X = Nageur[i]; let [id,Prenom,nom,competition,round] = X; let temps = CouleeparNageurs.select("temps","courses.id","prenom","nageurs.nom") .filter(aq.escape(d => d['courses.id'] == id & d.prenom == Prenom & d['nageurs.nom'] == nom)) .reify().objects().map(d=> d.temps); let tpsPassage = CouleeparNageurs.select("tpsPassage","courses.id","prenom","nageurs.nom") .filter(aq.escape(d => d['courses.id'] == id & d.prenom == Prenom & d['nageurs.nom'] == nom)) .reify().objects().map(d=> d.tpsPassage); let distance = CouleeparNageurs.select("distance","courses.id","prenom","nageurs.nom") .filter(aq.escape(d => d['courses.id'] == id & d.prenom == Prenom & d['nageurs.nom'] == nom)) .reify().objects().map(d=> d.distance); let distanceDebut = CouleeparNageurs.select("distanceDebut","courses.id","prenom","nageurs.nom") .filter(aq.escape(d => d['courses.id'] == id & d.prenom == Prenom & d['nageurs.nom'] == nom)) .reify().objects().map(d=> d.distanceDebut) //On initie les variables pour ajouter dans la liste let t = [] let d = [] for (let i=0;i x, // given d in data, returns the (temporal) x-value y = ([, y]) => y, // given d in data, returns the (quantitative) y-value z = () => 1, // given d in data, returns the (categorical) z-value title, // given d in data, returns the title text defined, // for gaps in data curve = d3.curveLinear, // method of interpolation between points marginTop = 20, // top margin, in pixels marginRight = 30, // right margin, in pixels marginBottom = 30, // bottom margin, in pixels marginLeft = 40, // left margin, in pixels width = 640, // outer width, in pixels height = 400, // outer height, in pixels xType = d3.scaleLinear, // type of x-scale xDomain, // [xmin, xmax] xRange = [marginLeft, width - marginRight], // [left, right] xFormat, xLabel, yType = d3.scaleLinear, // type of y-scale yDomain, // [ymin, ymax] yRange = [height - marginBottom, marginTop], // [bottom, top] yFormat, // a format specifier string for the y-axis yLabel, // a label for the y-axis zDomain, // array of z-values color = "currentColor", // stroke color of line, as a constant or a function of *z* strokeLinecap, // stroke line cap of line strokeLinejoin, // stroke line join of line strokeWidth = 1.5, // stroke width of line strokeOpacity, // stroke opacity of line mixBlendMode = "multiply", // blend mode of lines voronoi // show a Voronoi overlay? (for debugging) } = {}) { // Compute values. const X = d3.map(data, x); const Y = d3.map(data, y); const Z = d3.map(data, z); const O = d3.map(data, d => d); if (defined === undefined) defined = (d, i) => !isNaN(X[i]) && !isNaN(Y[i]); const D = d3.map(data, defined); // Compute default domains, and unique the z-domain. if (xDomain === undefined) xDomain = [0, d3.max(X)]; if (yDomain === undefined) yDomain = [d3.min(Y)*0.95, d3.max(Y)]; if (zDomain === undefined) zDomain = Z; zDomain = new d3.InternSet(zDomain); // Omit any data not present in the z-domain. const I = d3.range(X.length).filter(i => zDomain.has(Z[i])); // Construct scales and axes. const xScale = xType(xDomain, xRange); const yScale = yType(yDomain, yRange); const xAxis = d3.axisBottom(xScale).ticks(width / 80).tickSizeOuter(0); const yAxis = d3.axisLeft(yScale).ticks(height / 60, yFormat); // Compute titles. const T = title === undefined ? Z : title === null ? null : d3.map(data, title); // Construct a line generator. const line = d3.line() .defined(i => D[i]) .curve(curve) .x(i => xScale(X[i])) .y(i => yScale(Y[i])); const svg = d3.create("svg") .attr("width", width) .attr("height", height) .attr("viewBox", [0, 0, width, height]) .attr("style", "max-width: 100%; height: auto; height: intrinsic;") .style("-webkit-tap-highlight-color", "transparent") .on("pointerenter", pointerentered) .on("pointermove", pointermoved) .on("pointerleave", pointerleft) .on("touchstart", event => event.preventDefault()); // An optional Voronoi display (for fun). if (voronoi) svg.append("path") .attr("fill", "none") .attr("stroke", "#ccc") .attr("d", d3.Delaunay .from(I, i => xScale(X[i]), i => yScale(Y[i])) .voronoi([0, 0, width, height]) .render()); svg.append("g") .attr("transform", `translate(0,${height - marginBottom})`) .call(xAxis) .call(g => g.append("text") .attr("x", width - (marginLeft + marginRight)) .attr("y", 25) .attr("fill", "currentColor") .attr("text-anchor", "start") .text(xLabel)); svg.append("g") .attr("transform", `translate(${marginLeft},0)`) .call(yAxis) .call(g => g.select(".domain").remove()) .call(voronoi ? () => {} : g => g.selectAll(".tick line").clone() .attr("x2", width - marginLeft - marginRight) .attr("stroke-opacity", 0.1)) .call(g => g.append("text") .attr("x", -marginLeft) .attr("y", 10) .attr("fill", "currentColor") .attr("text-anchor", "start") .text(yLabel)); svg.append("g") .selectAll("circle") .data(data) .join("circle") .attr("cx", i => xScale(X[i])) .attr("cy", i => yScale(Y[i])) .attr("fill", "currentColor") .attr("r", 2.5); const path = svg.append("g") .attr("fill", "none") .attr("stroke", typeof color === "string" ? color : null) .attr("stroke-linecap", strokeLinecap) .attr("stroke-linejoin", strokeLinejoin) .attr("stroke-width", strokeWidth) .attr("stroke-opacity", strokeOpacity) .selectAll("path") .data(d3.group(I, i => Z[i])) .join("path") .style("mix-blend-mode", mixBlendMode) .attr("stroke", typeof color === "function" ? ([z]) => color(z) : null) .attr("d", ([, I]) => line(I)); const dot = svg.append("g") .attr("display", "none"); dot.append("circle") .attr("r", 2.5); dot.append("text") .attr("font-family", "sans-serif") .attr("font-size", 10) .attr("text-anchor", "middle") .attr("y", -8); function pointermoved(event) { const [xm, ym] = d3.pointer(event); const i = d3.least(I, i => Math.hypot(xScale(X[i]) - xm, yScale(Y[i]) - ym)); // closest point path.style("stroke", ([z]) => Z[i] === z ? null : "#ddd").filter(([z]) => Z[i] === z).raise(); dot.attr("transform", `translate(${xScale(X[i])},${yScale(Y[i])})`); if (T) dot.select("text").text(T[i]); svg.property("value", O[i]).dispatch("input", {bubbles: true}); } function pointerentered() { path.style("mix-blend-mode", null).style("stroke", "#ddd"); dot.attr("display", null); } function pointerleft() { path.style("mix-blend-mode", "multiply").style("stroke", null); dot.attr("display", "none"); svg.node().value = null; svg.dispatch("input", {bubbles: true}); } return Object.assign(svg.node(), {value: null}); }4d:[] 29:{"id":"4ab4aeb32d243713","slug":null,"title":"PAr 106 SwimAnalysis : Visualisation données de courses","thumbnail":"a5ffd34358b2b02255280d0136eb9ca42aadec31ced748f6f8a1ec6d9a885b52","access_level":"unlisted","create_time":"$D2021-11-09T10:10:57.060Z","update_time":"$D2022-06-06T08:26:50.284Z","view_time":null,"first_public_time":null,"has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"a6eb804d69c0a4f0","login":"benjamindenise","name":"","avatar_url":"https://avatars-next.observableusercontent.com/70f930d0bf2bf83a117a3ff64c3523fa5c3fa376baeae7996ce0d937b79d1ca0","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":2753,"publish_version":2753,"role":null,"authored":false,"authors":[{"type":"individual","id":"89c92f9dd07be7a0","login":"benjamindenise","name":"","avatar_url":"https://avatars-next.observableusercontent.com/70f930d0bf2bf83a117a3ff64c3523fa5c3fa376baeae7996ce0d937b79d1ca0","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"fd2bfb99bf56c2e5","login":"antoinehnz","name":"","avatar_url":"https://avatars-next.observableusercontent.com/38f53652d662ff38cf8f3312c897b4a1826f20df3401f2ecd6dee15b03d9dbd9","tier":null,"role":"editor"},{"type":"individual","id":"89c92f9dd07be7a0","login":"benjamindenise","name":"","avatar_url":"https://avatars-next.observableusercontent.com/70f930d0bf2bf83a117a3ff64c3523fa5c3fa376baeae7996ce0d937b79d1ca0","tier":null,"role":"editor"},{"type":"individual","id":"560201c9c61a188e","login":"romsson","name":"Romain Vuillemot","avatar_url":"https://avatars-next.observableusercontent.com/3b127750f8c67597cf2e60d4296f87cde15f59cdb4d1853063922cce5489fb4b","tier":null,"role":"viewer"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"titling":"auto","title":"PAr 106 SwimAnalysis : Visualisation données de courses","cells":[{"id":0,"value":"# PAr 106 SwimAnalysis : Visualisation données de courses","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1156,"value":"FULLSCREEN = fullscreen({\n breakLayoutAtCell: 19,\n hideAfter: 21,\n hideBefore: 2,\n left: 50,\n right: 44,\n button: html`

Evolution de la vitesse en fonction de la distance

Evolution de la vitesse en fonction du temps

Classement des temps

Nombre de cycle en fonction du temps

Fréquence (nbr cycle/min) en fonction de la distance

Nombre de cycle par nageurs et par courses

Classement par nombre de cycles/course