Published
Edited
Apr 8, 2021
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### Identifying High Performers
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As an alternative way to explore a parametric design space, the parallel line coordinate graph below can be a useful tool. The left columns of the graph represent parametric inputs, including façade orientation, window-to-wall ratio, window U-value, shading, and thermal mass. The columns on the right display key output metrics including daytime comfort percentage, cooling degree hours (CDH), heating degree hours (HDH), and heating + cooling degree hours (HCDH). Each line on the graph, passing through each input and output columns, represents a simulated scenario. By limiting the field of results to those that provide passive thermal comfort for a minimum of 65% of the year, and selecting a façade orientation which is of interest to the user, we can identify all of the envelope combinations able to satisfy our comfort criteria, rather than limiting the design to a single optimal case.


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euiReverse = d => (unitSelection.unit === "IP") ? _.round(d, 2) : _.round(d / 3.412 / 0.092903);
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color = d => {
const lookup = {
"comfort": "#32a9aa",
// "expanded": "#8acfb9",
"expanded": "#bbe2e2",
"oa": "#b3cfdd",
"perceived": "#005688"
}
return lookup[d];
}
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color2 = {
const domain = d3.extent(mapped.map(d => d["Day Comfort"])).map((d, i) => i === 0 ? d * 1: d * 1)
// const scale = d3.scaleLinear().domain(domain).interpolate(d3.interpolateHcl).range(["#A6E1F6", "#005688"]);
const scale = d3.scaleSequential([.5, .9], d3.interpolateRdPu)
return scale;
}

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orientationColor = d3.scaleSequential([.5, .9], d3.interpolateRdPu)
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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.
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