Some viz for ToulouseDataViz
viewof table = Inputs.table(
dataEx.filter((d) => filterDate(d)),
{ locale: "FR-fr" }
)
import { SummaryTable } from "@observablehq/summary-table"
SummaryTable(dataEx.filter((d) => filterDate(d)))
types = [
"consommation",
"fioul",
"charbon",
"gaz",
"nucléaire",
"eolien",
"solaire",
"hydraulique",
"pompage",
"bioénergies"
]
typesEx = [
"consommation",
"fioul",
"charbon",
"gaz",
"nucléaire",
"eolien",
"solaire",
"hydraulique",
"bioénergies"
]
dataFiltered = dataParTypeEx.filter((d) =>
categoriesFilteredSorted.includes(d[categoriesName])
)
getSelectedFile = {
return getSelectedFileMonthYear();
}
dataEx = typeDataEx(getSelectedFile)
dataParTypeEx = getDataPerTypeEx
getDataPerTypeEx = buildDatePerType(dataEx, false)
dataParTypeFilteredByDate = dataParTypeEx.filter((d) => filterDate(d))
dataParTypeFilteredByYear = buildDatePerType(
typeDataEx(p_annee_moy).filter((d) => d.année === yearselect)
)
dataParTypeMoisEx = buildDatePerType(
typeDataEx(getSelectedFileMonthYear()),
false
)
categoriesRolledUp = d3.rollups(
dataParTypeFilteredByYear,
(v) => d3.mean(v, (d) => d[metricName]),
(d) => d[categoriesName]
)
// filter and sort the categorical strings based on the selections from the checkboxes and radio buttons
categoriesFilteredSorted = categoriesRolledUp
.filter((d) => categoriesToShow.includes(d[0]))
.sort(sortOptions[selectedSortOption])
.map((d) => d[0])
consommationTotale = categoriesRolledUp.filter(
(d) => d[0] === "consommation"
)[0][1]
categoriesFilteredSortedPie = categoriesRolledUp
.filter((d) => d[0] !== "consommation")
.filter((d) => categoriesToShow.includes(d[0]))
.sort(sortOptions[selectedSortOption])
categoriesFilteredSortedPie2 = categoriesFilteredSortedPie.map((item) => {
return { name: item[0], value: item[1] };
})
myBarData = Object.keys(myBarDataReduce).map((item) => {
return { type: item, puissance: myBarDataReduce[item] };
})
// a few functions to choose from for sorting the categories
sortOptions = ({
alphabétique: (a, b) => (a[0] > b[0] ? 1 : -1),
décroissant: (a, b) => b[1] - a[1],
croissant: (a, b) => a[1] - b[1]
})
formatDate = d3.timeFormat("%Y-%m-%d")
function capitalize(string) {
return string[0].toUpperCase() + string.slice(1);
}
categoriesName = "type"
metricName = "puissance"
dateExtent1 = d3.extent(dataEx.map((d) => d.date))
slider1Dates = slider1.map((d) => d3.timeDay.offset(dateExtent1[0], d))
function filterDate(d) {
return (d.date >= slider1Dates[0]) & (d.date <= slider1Dates[1]);
}
function getSelectedFileMonthYear() {
let fileName = p_mois_moy;
if (sampling === "mois") {
fileName = p_mois_moy;
}
if (sampling === "annee") {
fileName = p_annee_moy;
}
return fileName;
}
//import { WSJPieSorting } from "@alainro/rte2012-2020";
// WSL recommended sorting for pie chart
function WSJPieSorting(array) {
let WSJproportion = [array[0]]
.concat(array.slice(2).reverse())
.concat(array[1]);
return WSJproportion;
}
function filterPieColor() {
let myPieColorPreFiltered = [];
for (let i = 0; i < myPieData2.length; i++) {
myPieColorPreFiltered.push(getColor(myPieData2[i].name));
}
return myPieColorPreFiltered;
}
myPieColors = filterPieColor()
function buildDatePerType(dataSource, addPompage) {
const dataParType = [];
dataSource.forEach((d) => {
const dataArray = addPompage ? types : typesEx;
dataArray.forEach((t) => {
dataParType.push({
date: d.date,
année: d.année,
mois: d.mois,
jour: d.jour,
type: t,
puissance: +d[t]
});
});
});
return dataParType;
}
import { colorOpacity } from "@mootari/color-opacity"
import { rangeSlider } from "@mootari/range-slider"
import { PieChart } from "@d3/pie-chart"
p_mois_moy = FileAttachment("puissance_mois_moy.csv").csv()
p_annee_moy = FileAttachment("puissance_annee_moy.csv").csv()
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