Swarthmore Battery OptimizationHorton AWS SimulationsHorton AWS ResultsNYC TypologiesYale Berkeley UpdateNYC Electrification Results - Capacity ConstraintsBay Area CFE AppendixNYC Electrification Results - Updated MetricsCoorsTek 9th St. RedevelopmentCharacterization Experiments100 (ish) cities to simulateDisaggregation ExplorationsUtilitiesDisaggregation Graph V2Combinations TestCharacterization ExperimentsCombinations Test First Week ConstrainedCharacterization Graph V2Model Input CalibrationUT Model ResultsUT EEM All BuildingsMIT Model Input CalibrationMIT EEMsMIT Final Model ResultsMIT EEM All BuildingsMIT Monthly Baseline ResultsHorton Analysis ResultsHorton Solar + StorageGoogle Monthly Baseline ResultsGoogle EEMsDC Energy Data AggregationEmissions VisualizationEPW ReaderSunpath DiagramPyschrometric Chart
Google Input Calibration
md`# Google Input Calibration`
xlsx = require('https://bundle.run/xlsx@0.14.1')
// width = 1200
chart = {
const svg = d3.create("svg")
.attr("viewBox", [0, 0, width, height]);
svg.append("g")
.call(yAxis_ticks);
const g = svg.selectAll(".vars")
.data(counts)
.join("g")
.attr("class", "vars")
.attr("transform", d => `translate(0,${y(d.variable) + y.bandwidth()/2})`);
g.each(function(d, i) {
const x = d3.scaleLinear()
// .domain([0, 3]).nice()
.domain([0, 2]).nice()
.range([margin.left, width - margin.right]);
const xAxis = g => g
// .call(d3.axisBottom(x).tickValues([0, 1, 2, 3]).tickFormat(dd => x_lookup[d.variable] [dd]).tickPadding(12))
.call(d3.axisBottom(x).tickValues([0, 1, 2]).tickFormat(dd => x_lookup[d.variable][dd]).tickPadding(12))
.call(g => g.select(".domain").remove())
.call(g => g.selectAll(".tick line").remove())
.call(g => g.selectAll("text").attr("font-weight", "bold"))
.call(g => g.selectAll("text").attr("color", (dd, i) => highlight(d, i)))
.call(g => g.selectAll("text").style("mix-blend-mode", "multiply"));
d3.select(this).selectAll(".dots")
// .data([0, 1, 2, 3])
.data([0, 1, 2])
.join("circle")
.attr("class", "dots")
.style("mix-blend-mode", "multiply")
.attr("stroke", "#00695C")
.attr("stroke-width", 1)
.attr("fill", "#80CBC4")
// .attr("fill", chroma("#00a1b0").brighten(1.5).desaturate())
.attr("fill-opacity", .5)
.attr("cy", 0)
.attr("cx", (d, i) => x(i))
.attr("r", dd => scale(d[dd.toString(16)]))
d3.select(this).selectAll(".points")
// .data([0, 1, 2, 3])
.data([0, 1, 2])
.join("circle")
.attr("class", "points")
.style("mix-blend-mode", "multiply")
// .attr("stroke", "#00a1b0")
// .attr("stroke-width", 2)
.attr("fill", (dd, i) => highlight(d, i))
.attr("cy", 0)
.attr("cx", (d, i) => x(i))
.attr("r", 4)
d3.select(this).append("g").call(xAxis);
})
svg.append("g")
.call(xAxis);
svg.append("g")
.call(yAxis);
svg.selectAll("text").attr("font-size", 16).attr("font-family", "Open Sans")
return svg.node();
}
filename = binary.name.split(".")[0]
input = new Uint8Array(await Files.buffer(binary))
wb = xlsx.read(input, {type: 'array'})
// data must be structured with typical input fields as column headers, with one column calling out building name
// data = FileAttachment("UT Baseline Inputs@1.csv").csv({typed: true})
excel = wb.Sheets["Sheet1"]
data = xlsx.utils.sheet_to_json(excel)
margin = ({top: 80, right: 100, bottom: 50, left: 350})
height = 1400
d3 = require("d3@^6.1")
_ = require('lodash@4.17.15/lodash.js').catch(() => window["_"])
colors = ({"S": "#f79320", "E": "#fed202", "C":"#00a1b0"})
chroma = require('chroma-js')
counts = {
const count = (d, i) => {
const arr = data.map(dd => dd[d]);
return arr.filter(dd => dd === i).length;
}
const keymap = Object.keys(v_lookup).map(d => ({variable: d, "0": count(d, 0), "1": count(d, 1), "2": count(d, 2)}));
const reverse = d => {
if (d.variable === "HUMIDMIN" || d.variable === "HUMIDMAX") {
let [z, o, t] = [d["0"], d["1"], d["2"]];
return {...d, "0": t, "2": z};
}
else return d;
}
return keymap.map(d => reverse(d));
}
energy = d => {
let r = "Low";
switch(d) {
case 0:
r= "High";
break;
case 1:
r= "Mid";
break;
default:
r="Low";
}
return r;
}
y = d3.scaleBand()
.domain(Object.keys(v_lookup).reverse())
.range([height - margin.bottom, margin.top])
.padding(.1)
.paddingOuter(.5)
x = d3.scaleLinear()
// .domain([0, 3]).nice()
.domain([0, 2]).nice()
.range([margin.left, width - margin.right])
xAxis = g => g
.attr("transform", `translate(0,${margin.top})`)
// .call(d3.axisTop(x).tickValues([0, 1, 2, 3]).tickFormat((d, i) => energy(i)).tickPadding(10))
.call(d3.axisTop(x).tickValues([0, 1, 2]).tickFormat((d, i) => energy(i)).tickPadding(10))
.call(g => g.select(".domain").remove())
.call(g => g.select(".tick:first-of-type text").clone()
.attr("y", -50)
.attr("x", -18)
.attr("text-anchor", "start")
.attr("font-weight", "bold")
.html("Energy Use"))
yAxis = g => g
.attr("transform", `translate(${5},0)`)
.call(d3.axisRight(y).tickFormat(d => v_lookup[d]))
.call(g => g.select(".domain").remove())
.call(g => g.selectAll(".tick line").remove())
yAxis_ticks = g => g
.attr("transform", `translate(${margin.left},0)`)
.call(d3.axisRight(y))
.call(g => g.select(".domain").remove())
.call(g => g.selectAll("text").remove())
.call(g => g.selectAll(".tick line")
.attr("stroke-opacity", 0.1)
.attr("x2", width - margin.left - margin.right))
scale = d3.scaleLinear()
.domain([0, 100])
.range([0, y.bandwidth()/1.5])
v_lookup = ({
// "WWR": "Window Wall Ratio (%)",
// "FORMFACT": "Form Factor",
"WINU": "Window Assembly U (BTU/h-ft2-°F)",
"WINSHGC": "Window SHGC",
"WALLR": "Effective Wall R (h-ft2-°F/BTU)",
"ROOFR": "Effective Roof R (h-ft2-°F/BTU)",
"INFIL": "Infiltration (CFM/ft2)",
"EPD": "Equipment (W/ft2)",
"LPD": "Lighting (W/ft2)",
// "ACH": "Air Changes per Hour",
"VENTSCH": "Ventilation Schedule",
"HUMIDMAX": "Humidity Control Max RH",
"SAT": "Supply Temperature Reset (°F)",
"FANPOWER": "Fan Pressure Drop (in. H2O)",
// "OASYSTEM": "Outside Air System",
"ECON": "Economizer Operation",
"HRV": "Heat Recovery Ventilation",
"COOLCOP": "Cooling COP",
"HEATCOP": "Heating COP"
})
x_lookup = ({
"WWR": ["80%", "60%", "40%"],
"FORMFACT": [1.4, 1.2, 0.9],
"WINU": [0.9, 0.6, 0.3],
"WINSHGC": [0.4, 0.3, 0.2],
"WALLR": [5, 10, 15],
"ROOFR": [10, 15, 20],
"INFIL": [0.10, 0.06, 0.02],
"EPD": [9, 6, 3],
"LPD": [1.2, 0.9, 0.6],
"ACH": [2.25, 1.50, 0.75],
"VENTSCH": ["Always On", "Reduced", "Minimum"],
"HUMIDMAX": ["50%", "70%", "90%"],
"SAT": ["55", "60", "NA"],
"FANPOWER": [8, 6, 4],
"OASYSTEM": ["Recirculating", "Once Through", "NA"],
"ECON": ["No Economizer", "Drybulb", "NA"],
"HRV": ["0", "40%", "70%"],
"COOLCOP": [1, 3, 6],
"HEATCOP": [0.7, 1.0, 3.5]
})
serialize = {
const xmlns = "http://www.w3.org/2000/xmlns/";
const xlinkns = "http://www.w3.org/1999/xlink";
const svgns = "http://www.w3.org/2000/svg";
return function serialize(svg) {
svg = svg.cloneNode(true);
const fragment = window.location.href + "#";
const walker = document.createTreeWalker(svg, NodeFilter.SHOW_ELEMENT, null, false);
while (walker.nextNode()) {
for (const attr of walker.currentNode.attributes) {
if (attr.value.includes(fragment)) {
attr.value = attr.value.replace(fragment, "#");
}
}
}
svg.setAttributeNS(xmlns, "xmlns", svgns);
svg.setAttributeNS(xmlns, "xmlns:xlink", xlinkns);
const serializer = new window.XMLSerializer;
const string = serializer.serializeToString(svg);
return new Blob([string], {type: "image/svg+xml"});
};
}
highlight = (d, i) => {
const objs = Object.entries(d).filter(dd => dd[0] != "variable").sort((a, b) => b[1] - a[1]);
const index = +objs[0][0];
return index === i ? "#00695C": "#dfdfdf";
}
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.
