Simple Gaussian

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <title>Three m's visuals</title>
  <script src="https://d3js.org/d3.v3.min.js"></script>
  <style>
    body {
      font: 10px sans-serif;
    }
    .axis path,
    .axis line {
      fill: none;
      stroke: #000;
      shape-rendering: crispEdges;
    }
    .line {
      fill: none;
      stroke: steelblue;
      stroke-width: 1.5px;
    }
  </style>
</head>
<body>

</body>
<script>
// setting up empty data array
var data = [];

getData(); // populate data

// line chart based on https://bl.ocks.org/mbostock/3883245
var margin = {
      top: 20,
      right: 20,
      bottom: 30,
      left: 50
    },
    width = 640 - margin.left - margin.right,
    height = 310 - margin.top - margin.bottom;

var x = d3.scale.linear()
    .range([0, width]);

var y = d3.scale.linear()
    .range([height, 0]);

var xAxis = d3.svg.axis()
    .scale(x)
    .orient("bottom");

var yAxis = d3.svg.axis()
    .scale(y)
    .orient("left");

var line = d3.svg.line()
    .x(function(d) {
      return x(d.q);
    })
    .y(function(d) {
      return y(d.p);
    });

var svg = d3.select("body").append("svg")
    .attr("width", width + margin.left + margin.right)
    .attr("height", height + margin.top + margin.bottom)
    .append("g")
    .attr("transform", "translate(" + margin.left + "," + margin.top + ")");

x.domain(d3.extent(data, function(d) {
  return d.q;
}));
y.domain(d3.extent(data, function(d) {
  return d.p;
}));

svg.append("g")
    .attr("class", "x axis")
    .attr("transform", "translate(0," + height + ")")
    .call(xAxis);

svg.append("g")
    .attr("class", "y axis")
    .call(yAxis);

svg.append("path")
    .datum(data)
    .attr("class", "line")
    .attr("d", line);

function getData() {

// loop to populate data array with
// probability - quantile pairs
  for (var i = 0; i < 100000; i++) {
    var q = normal(); // calc random draw from normal dist
    var p = gaussian(q); // calc prob of rand draw
    var el = {
          "q": q,
          "p": p
        };
  data.push(el);
}

// need to sort for plotting
// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/sort
data.sort(function(x, y) {
  return x.q - y.q;
});
}

// from https://bl.ocks.org/mbostock/4349187
// 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
}

// taken from Jason Davies science library
// https://github.com/jasondavies/science.js/
function gaussian(x) {
  var gaussianConstant = 1 / Math.sqrt(2 * Math.PI),
      mean = 0,
      sigma = 1;

x = (x - mean) / sigma;
  return gaussianConstant * Math.exp(-.5 * x * x) / sigma;
}
</script>
</html>

https://d3js.org/d3.v3.min.js