Q-Q Plots
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| license: gpl-3.0 | |
| redirect: https://observablehq.com/@d3/q-q-plot |
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| (function(){function t(t,n){var r=n.length-1;return n=n.slice().sort(d3.ascending),d3.range(t).map(function(a){return n[~~(a*r/t)]})}function n(t){return t.x}function r(t){return t.y}d3.qq=function(){function a(n){n.each(function(n,r){var a,y,g=d3.select(this),f=t(s,l.call(this,n,r)),m=t(s,d.call(this,n,r)),x=o&&o.call(this,n,r)||[d3.min(f),d3.max(f)],h=o&&o.call(this,n,r)||[d3.min(m),d3.max(m)],p=d3.scale.linear().domain(x).range([0,e]),v=d3.scale.linear().domain(h).range([i,0]);this.__chart__?(a=this.__chart__.x,y=this.__chart__.y):(a=d3.scale.linear().domain([0,1/0]).range(p.range()),y=d3.scale.linear().domain([0,1/0]).range(v.range())),this.__chart__={x:p,y:v};var _=g.selectAll("line.diagonal").data([null]);_.enter().append("svg:line").attr("class","diagonal").attr("x1",p(h[0])).attr("y1",v(x[0])).attr("x2",p(h[1])).attr("y2",v(x[1])),_.transition().duration(c).attr("x1",p(h[0])).attr("y1",v(x[0])).attr("x2",p(h[1])).attr("y2",v(x[1]));var k=g.selectAll("circle").data(d3.range(s).map(function(t){return{x:f[t],y:m[t]}}));k.enter().append("svg:circle").attr("class","quantile").attr("r",4.5).attr("cx",function(t){return a(t.x)}).attr("cy",function(t){return y(t.y)}).style("opacity",1e-6).transition().duration(c).attr("cx",function(t){return p(t.x)}).attr("cy",function(t){return v(t.y)}).style("opacity",1),k.transition().duration(c).attr("cx",function(t){return p(t.x)}).attr("cy",function(t){return v(t.y)}).style("opacity",1),k.exit().transition().duration(c).attr("cx",function(t){return p(t.x)}).attr("cy",function(t){return v(t.y)}).style("opacity",1e-6).remove();var A=u||p.tickFormat(4),q=u||v.tickFormat(4),F=function(t){return"translate("+p(t)+","+i+")"},C=function(t){return"translate(0,"+v(t)+")"},w=g.selectAll("g.x.tick").data(p.ticks(4),function(t){return this.textContent||A(t)}),b=w.enter().append("svg:g").attr("class","x tick").attr("transform",function(t){return"translate("+a(t)+","+i+")"}).style("opacity",1e-6);b.append("svg:line").attr("y1",0).attr("y2",-6),b.append("svg:text").attr("text-anchor","middle").attr("dy","1em").text(A),b.transition().duration(c).attr("transform",F).style("opacity",1),w.transition().duration(c).attr("transform",F).style("opacity",1),w.exit().transition().duration(c).attr("transform",F).style("opacity",1e-6).remove();var j=g.selectAll("g.y.tick").data(v.ticks(4),function(t){return this.textContent||q(t)}),z=j.enter().append("svg:g").attr("class","y tick").attr("transform",function(t){return"translate(0,"+y(t)+")"}).style("opacity",1e-6);z.append("svg:line").attr("x1",0).attr("x2",6),z.append("svg:text").attr("text-anchor","end").attr("dx","-.5em").attr("dy",".3em").text(q),z.transition().duration(c).attr("transform",C).style("opacity",1),j.transition().duration(c).attr("transform",C).style("opacity",1),j.exit().transition().duration(c).attr("transform",C).style("opacity",1e-6).remove()})}var e=1,i=1,c=0,o=null,u=null,s=100,l=n,d=r;return a.width=function(t){return arguments.length?(e=t,a):e},a.height=function(t){return arguments.length?(i=t,a):i},a.duration=function(t){return arguments.length?(c=t,a):c},a.domain=function(t){return arguments.length?(o=null==t?t:d3.functor(t),a):o},a.count=function(t){return arguments.length?(s=t,a):s},a.x=function(t){return arguments.length?(l=t,a):l},a.y=function(t){return arguments.length?(d=t,a):d},a.tickFormat=function(t){return arguments.length?(u=t,a):u},a}})(); |
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| <!DOCTYPE html> | |
| <meta charset="utf-8"> | |
| <title>Q-Q Plots</title> | |
| <style> | |
| body { | |
| font: 10px sans-serif; | |
| width: 960px; | |
| height: 310px; | |
| } | |
| .qq .box, | |
| .qq .tick line, | |
| .qq .quantile, | |
| .qq .diagonal { | |
| stroke: #aaa; | |
| fill: none; | |
| } | |
| .qq .quantile { | |
| stroke: #000; | |
| } | |
| .qq g + g .y.tick { | |
| display: none; | |
| } | |
| </style> | |
| <body> | |
| <script src="//d3js.org/d3.v3.min.js"></script> | |
| <script src="d3.qq.min.js"></script> | |
| <script> | |
| var width = 270, | |
| height = 270, | |
| margin = {top: 20, right: 10, bottom: 20, left: 35}, | |
| n = 10000; // number of samples to generate | |
| var chart = d3.qq() | |
| .width(width) | |
| .height(height) | |
| .domain([-.1, 1.1]) | |
| .tickFormat(function(d) { return ~~(d * 100); }); | |
| var vis = d3.select("body").append("svg") | |
| .attr("width", (width + margin.right + margin.left) * 3) | |
| .attr("height", height + margin.top + margin.bottom) | |
| .append("g") | |
| .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); | |
| d3.json("turkers.json", function(error, turkers) { | |
| if (error) throw error; | |
| var tm = mean(turkers), | |
| td = Math.sqrt(variance(turkers)), | |
| dd = [ | |
| [0.10306430789206111, 0.0036139086950272735, 0.30498647327844536], | |
| [0.5924252668569606, 0.0462763685758622, 0.4340870312025223], | |
| [0.9847627827855167, 2.352350767874714e-4, 0.2609264955190324] | |
| ]; | |
| var g = vis.selectAll("g") | |
| .data([{ | |
| x: d3.range(n).map(Math.random), | |
| y: turkers, | |
| label: "Uniform Distribution" | |
| }, { | |
| x: d3.range(n).map(normal1(tm, td)), | |
| y: turkers, | |
| label: "Gaussian (Normal) Distribution" | |
| }, { | |
| x: d3.range(n).map(normal3(dd)), | |
| y: turkers, | |
| label: "Mixture of 3 Gaussians" | |
| }]) | |
| .enter().append("g") | |
| .attr("class", "qq") | |
| .attr("transform", function(d, i) { return "translate(" + (width + margin.right + margin.left) * i + ")"; }); | |
| g.append("rect") | |
| .attr("class", "box") | |
| .attr("width", width) | |
| .attr("height", height); | |
| g.call(chart); | |
| g.append("text") | |
| .attr("dy", "1.3em") | |
| .attr("dx", ".6em") | |
| .text(function(d) { return d.label; }); | |
| chart.duration(1000); | |
| window.transition = function() { | |
| g.datum(randomize).call(chart); | |
| }; | |
| }); | |
| function randomize(d) { | |
| d.y = d3.range(n).map(Math.random); | |
| return d; | |
| } | |
| // Sample from a normal distribution with mean 0, stddev 1. | |
| function normal() { | |
| var x = 0, y = 0, rds, c; | |
| do { | |
| x = Math.random() * 2 - 1; | |
| y = Math.random() * 2 - 1; | |
| rds = x * x + y * y; | |
| } while (rds == 0 || rds > 1); | |
| c = Math.sqrt(-2 * Math.log(rds) / rds); // Box-Muller transform | |
| return x * c; // throw away extra sample y * c | |
| } | |
| // Simple 1D Gaussian (normal) distribution | |
| function normal1(mean, deviation) { | |
| return function() { | |
| return mean + deviation * normal(); | |
| }; | |
| } | |
| // Gaussian Mixture Model (k=3) fit using E-M algorithm | |
| function normal3(dd) { | |
| return function() { | |
| var r = Math.random(), | |
| i = r < dd[0][2] ? 0 : r < dd[0][2] + dd[1][2] ? 1 : 2, | |
| d = dd[i]; | |
| return d[0] + Math.sqrt(d[1]) * normal(); | |
| } | |
| } | |
| // Welford's algorithm. | |
| function mean(x) { | |
| var n = x.length; | |
| if (n === 0) return NaN; | |
| var m = 0, | |
| i = -1; | |
| while (++i < n) m += (x[i] - m) / (i + 1); | |
| return m; | |
| } | |
| // Unbiased estimate of a sample's variance. | |
| // Also known as the sample variance, where the denominator is n - 1. | |
| function variance(x) { | |
| var n = x.length; | |
| if (n < 1) return NaN; | |
| if (n === 1) return 0; | |
| var m = mean(x), | |
| i = -1, | |
| s = 0; | |
| while (++i < n) { | |
| var v = x[i] - m; | |
| s += v * v; | |
| } | |
| return s / (n - 1); | |
| } | |
| </script> |
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Is this GPL or ISC licensed?
Here the Gist says GPL. But the link at https://gist.github.com/mbostock/4349187 says ISC.