Unlisted
Edited
Dec 6, 2022
// List of Rosneft origin stations taken from here: https://www.rosneft.ru/upload/site1/document_file/rep11_ezho4kv2020.pdf
getmanovskaya = FileAttachment("crude-to-getmanovskaya.csv")
.csv()
.then(c => c
.filter(d => d["Код станции назначения РФ"] === "52620")
.map(d => ({
date: d3.utcParse("%m.%Y")(d["Месяц отправления"]),
producer: d["Грузоотправитель наим по ЕГРПО"],
volume: d["Объем перевозок тонн"] / 1e3
})))
kropotkin = d3.sort(
d3.flatRollup(
shell_exports
.filter(d =>
d.stat === "1" &&
d["exporter code"] === "TMPRU006615")
.map(d => ({
date: d3.utcParse("%d-%m-%Y")(d.Date),
producer: d["raw manufacturer name"] ? d["raw manufacturer name"].match(/ОРЕНБУРГНЕФТЬ|ИНГУШНЕФТЬ|СТАВРОПОЛЬНЕФТЕГАЗ|ДАГНЕФТЬ|РОСНЕФТЬ/)[0] : "No data",
volume: parseInt(d["Net Weight Kg"]) / 1e3 / 1e3
})),
v => d3.sum(v, d => d.volume),
k => d3.utcMonth.floor(k.date),
k => k.producer
)
.map(([date, producer, volume]) => ({ date, producer, volume })),
k => k.date,
k => k.producer
)
komsomolsk = d3.sort(
d3.flatRollup(
lukoil_exports
.filter(d =>
d.stat === "1" &&
d["place of discharge"] === "PS KOMSOMOLSKAYA")
.map(d => ({
date: d3.utcParse("%d-%m-%Y")(d.Date),
volume: parseInt(d["Net Weight Kg"]) / 1e3 / 1e3
})),
v => d3.sum(v, d => d.volume),
k => d3.utcDay.floor(k.date)
)
.map(([date, volume]) => ({ date, volume })),
k => k.date
)
cpc_rf_freq = d3.flatRollup(
cpc,
v => d3.sum(v.filter(d => d.producer !== "Kazakhstan transit"), d => d.volume) / d3.sum(v, d => d.volume) * 100,
k => k.date
).map(([date, freq]) => ({ date, freq }))
cpc = d3.sort([...cpc_2021, ...cpc_2022, ...cpc_estimates], k => k.date, k => -k.volume)
cpc_estimates = [
{date: new Date("2022-10-15"), producer: "Kazakhstan transit", volume: 3448.4},
{date: new Date("2022-10-15"), producer: "LUKOIL", volume: 364},
{date: new Date("2022-10-15"), producer: "Independent producers", volume: 24.5},
{date: new Date("2022-10-15"), producer: "ROSNEFT", volume: 3},
{date: new Date("2022-11-15"), producer: "Kazakhstan transit", volume: 3448.4},
{date: new Date("2022-11-15"), producer: "LUKOIL", volume: 660},
{date: new Date("2022-11-15"), producer: "Independent producers", volume: 24.5},
{date: new Date("2022-11-15"), producer: "ROSNEFT", volume: 3}
]
cpc_2022 = FileAttachment("INE4092022.XLSX")
.xlsx()
.then(w => w
.sheet(0, { headers: true, range: "A4:N68" })
.filter(d => d["Country/port"] === "CPC" && d["Producer**"] !== "GAZPROM NEFT")
.flatMap(d => {
var o = [];
const producer = d["Producer**"];
[
"Producer**", "Markets", "Country/port", "Transport inside Russia",
"Transport on Russia frontier", "Ytd"
].forEach(c => {
delete d[c];
});
Object.keys(d).forEach(c => {
o.push({
date: d3.utcParse("%d %B %Y")(`15 ${c} 2022`),
producer: producer,
volume: d[c]
});
});
return o;
})
)
cpc_2021 = FileAttachment("INE4122021.XLSX")
.xlsx()
.then(w => w
.sheet(0, { headers: true, range: "A4:R103" })
.filter(d => d["Country/port"] === "CPC")
.flatMap(d => {
var o = [];
const producer = d["Producer"];
[
"Producer", "Markets", "Country/port", "Transport inside Russia",
"Transport on Russia frontier", "Ytd"
].forEach(c => {
delete d[c];
});
Object.keys(d).forEach(c => {
o.push({
date: d3.utcParse("%d %B %Y")(`1 ${c} 2021`),
producer: producer,
volume: d[c]
});
});
return o;
})
)
shell_exports = xlsx.utils.sheet_to_json(
xlsx.read(await FileAttachment("Export_Genius_09_16_2022_10_07_58.xlsx").arrayBuffer(), { type: "array" })
.Sheets["Data"],
{ raw: false })
lukoil_exports = xlsx.utils.sheet_to_json(
xlsx.read(await FileAttachment("Export_Genius_09_16_2022_10_19_26.xlsx").arrayBuffer(), { type: "array" })
.Sheets["Data"],
{ raw: false })
xlsx = require("xlsx@0.17.0/dist/xlsx.core.min.js")
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.
