chart = {
const svg = d3.create("svg").attr("viewBox", [0, 0, width, height]);
// from https://stackoverflow.com/questions/45381849/how-to-achieve-a-progressive-blur-using-svg-by-combining-a-filter-with-a-mask
const defs = svg.append("defs");
const reverseGradient = defs
.append("linearGradient")
.attr("id", "reverse-gradient");
reverseGradient
.append("stop")
.attr('offset', '20%')
.attr('stop-color', 'gray')
.attr('stop-opacity', '100%');
reverseGradient
.append("stop")
.attr('offset', '60%')
.attr('stop-color', 'gray')
.attr('stop-opacity', '0%');
const linearGradient = defs
.append("linearGradient")
.attr("id", "forecast-gradient");
linearGradient
.append("stop")
.attr('offset', '20%')
.attr('stop-color', 'gray')
.attr('stop-opacity', '0%');
linearGradient
.append("stop")
.attr('offset', '60%')
.attr('stop-color', 'gray')
.attr('stop-opacity', '100%');
defs
.append('rect')
.attr('id', 'gradient-rect')
.attr('width', '100%')
.attr('height', '100%')
.attr('fill', "url('#forecast-gradient')");
const filter = defs.append("filter").attr("id", 'forecast-filter');
filter
.append("feGaussianBlur")
.attr("in", "SourceGraphic")
.attr("stdDeviation", exStdDev)
.attr("color-interpolation-filters", "sRGB"); // added to fix lightening issue with Safari
// .attr("result", "blur"); // assign a name so it can be referenced by feColorMatrix
svg.append("g").call(xAxis);
svg.append("g").call(yAxis);
const g = svg
.append("g")
.attr("fill", "none")
.attr("stroke-linejoin", "round")
.attr("stroke-linecap", "round");
g.selectAll("path")
.data(dataGrouped)
.join("path")
.attr("stroke", 'url(#reverse-gradient)')
.attr("stroke-width", .5)
.attr("d", line)
.call(halo);
g.append('g')
.selectAll("path")
.data(dataGrouped)
.join("path")
.attr("stroke", 'url(#forecast-gradient)')
.attr("stroke-width", 3)
.attr("filter", "url('#forecast-filter')")
.attr("d", line)
.call(halo);
g.append("path")
.datum(dataActual)
.attr("stroke", "steelblue")
.attr("stroke-width", 1.5)
.attr("d", line);
// Whisker labels
g.selectAll("text")
.data(dataGrouped.map(d => d[d.length - 1]))
.join("text")
.attr("fill", "gray")
.attr("font-size", 10)
.attr("font-family", "sans-serif")
.attr("dx", 4)
.attr("dy", ".3em")
.attr("x", ({ about }) => x(about))
.attr("y", ({ value }) => y(value))
.text(({ on }) => dateFormat(on));
return svg.node();
}
data = d3.csvParse(
await FileAttachment("libor1mo.csv").text(),
({ on, about, value }) => ({
on: dateParse(on),
about: dateParse(about),
value: +value
})
)
dataGrouped = Array.from(d3.group(data, ({ on }) => +on).values())
dataActual = data.filter(({ on, about }) => +on === +about)
dateParse = d => d3.timeMonth.round(new Date(d))
dateFormat = d3.timeFormat("%b %Y")
valueFormat = d =>
Math.abs(d) < 1 ? d.toFixed(2) : Math.abs(d) > 100 ? d3.format(".2s")(d) : d
line = d3
.line()
.defined(d => !isNaN(d.value))
.x(d => x(d.about))
.y(d => y(d.value))
x = d3
.scaleUtc()
.domain(d3.extent([...data.map(d => d.on), ...data.map(d => d.about)]))
.range([margin.left, width - margin.right])
y = d3
.scaleLinear()
.domain([0, d3.max(data, d => d.value)])
.nice()
.range([height - margin.bottom, margin.top])
xAxis = g =>
g.attr("transform", `translate(0,${height - margin.bottom})`).call(
d3
.axisBottom(x)
.ticks(width / 80)
.tickSizeOuter(0)
)
yAxis = g =>
g
.attr("transform", `translate(${margin.left},0)`)
.call(
d3
.axisLeft(y)
.tickFormat(valueFormat)
.ticks(5)
)
.call(g => g.select(".domain").remove())
.call(g =>
g
.select(".tick:last-of-type text")
.clone()
.attr("x", 3)
.attr("text-anchor", "start")
.attr("font-weight", "bold")
.text("LIBOR 1 month forward curve")
)
function halo(text) {
text
.select(function() {
return this.parentNode.insertBefore(this.cloneNode(true), this);
})
.attr("fill", "none")
.attr("stroke", "white")
.attr("stroke-width", 4)
.attr("stroke-linejoin", "round");
}
margin = ({top: 20, right: 30, bottom: 30, left: 40})
height = 500
import { slider, checkbox, select, radio } from "@jashkenas/inputs"
d3 = require("d3@6")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
