// set the dimensions and margins of the graph
var margin = {top: 60, right: 30, bottom: 20, left:110},
    width = 460 - margin.left - margin.right,
    height = 400 - margin.top - margin.bottom;

// append the svg object to the body of the page
var svg = d3.select("#my_dataviz")
  .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 + ")");

//read data
d3.csv("https://raw.githubusercontent.com/zonination/perceptions/master/probly.csv", function(data) {

// Get the different categories and count them
  var categories = data.columns
  var n = categories.length

// Add X axis
  var x = d3.scaleLinear()
    .domain([-10, 140])
    .range([ 0, width ]);
  svg.append("g")
    .attr("transform", "translate(0," + height + ")")
    .call(d3.axisBottom(x));

// Create a Y scale for densities
  var y = d3.scaleLinear()
    .domain([0, 0.4])
    .range([ height, 0]);

// Create the Y axis for names
  var yName = d3.scaleBand()
    .domain(categories)
    .range([0, height])
    .paddingInner(1)
  svg.append("g")
    .call(d3.axisLeft(yName));

// Compute kernel density estimation for each column:
  var kde = kernelDensityEstimator(kernelEpanechnikov(7), x.ticks(40)) // increase this 40 for more accurate density.
  var allDensity = []
  for (i = 0; i < n; i++) {
      key = categories[i]
      density = kde( data.map(function(d){  return d[key]; }) )
      allDensity.push({key: key, density: density})
  }

// Add areas
  svg.selectAll("areas")
    .data(allDensity)
    .enter()
    .append("path")
      .attr("transform", function(d){return("translate(0," + (yName(d.key)-height) +")" )})
      .datum(function(d){return(d.density)})
      .attr("fill", "#69b3a2")
      .attr("stroke", "#000")
      .attr("stroke-width", 1)
      .attr("d",  d3.line()
          .curve(d3.curveBasis)
          .x(function(d) { return x(d[0]); })
          .y(function(d) { return y(d[1]); })
      )

})

// This is what I need to compute kernel density estimation
function kernelDensityEstimator(kernel, X) {
  return function(V) {
    return X.map(function(x) {
      return [x, d3.mean(V, function(v) { return kernel(x - v); })];
    });
  };
}
function kernelEpanechnikov(k) {
  return function(v) {
    return Math.abs(v /= k) <= 1 ? 0.75 * (1 - v * v) / k : 0;
  };
}

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