Public
Edited
Feb 8, 2024
weatherDT.view(10)
tempStatuses.view()
tempStatuses
.groupby('period', 'city')
.rollup({
t_avg: d => op.mean(d.t_avg),
})
.filter(
aq.escape((obs) => obs.period != 'junk')
)
.groupby('city')
.pivot('period', 't_avg')
.derive({
temp_diff: aq.escape((obs) => obs.now - obs.reference)
})
.orderby(aq.desc('temp_diff'))
.view()
extremeDaysChange.view()
extremeDaysChange.objects()
aq.from(seasonsData).view()
aq.from(seasonsData)
.groupby('yearGroup', 'city', 'calendarSeason', 'avgTempGroup2')
.count()
.groupby('yearGroup', 'city', 'calendarSeason')
.pivot('avgTempGroup2', 'count')
.objects()
seasonPercentsMonth.view()
seasonPercentsMonth.objects()
weatherDT.view()
refPeriodPrecip.view()
aq.from(extremePrecipDays)
.view()
weatherDT.view()
precipDaysType = weatherDT
.derive({
yearGroup: aq.escape((obs) => {
for (let i = 0; i < yearStarts.length; i++) {
if ((obs.year >= yearStarts[i]) && (obs.year < yearStarts[i] + yearsGroupLen))
return yearStarts[i]
}
})
})
.filter(
aq.escape((obs) => obs.precip_sum >= 1)
)
.filter(
aq.escape((obs) => obs.hours_precip_01mm >= 6)
)
.derive({
precip_type_by_sum_name: aq.escape((obs) => {
if (obs.precip_type_by_sum == 1)
return "дождь"
else if (obs.precip_type_by_sum == 2)
return "дождь и cнег"
else if (obs.precip_type_by_sum == 3)
return "cнег"
})
})
.groupby('year', 'city', 'yearGroup', 'precip_type_by_sum_name')
.count()
.groupby('yearGroup', 'city', 'precip_type_by_sum_name')
.rollup({
count: d => op.mean(d.count),
})
// .groupby('yearGroup', 'city')
// .pivot('precip_type_by_sum_name', 'count')
.objects()
total_precip_sum_by_seasons = weatherDT
.derive({
yearGroup: aq.escape((obs) => {
for (let i = 0; i < yearStarts.length; i++) {
if ((obs.year >= yearStarts[i]) && (obs.year < yearStarts[i] + yearsGroupLen))
return yearStarts[i]
}
}),
period: aq.escape((obs) => {
if ((obs.year >= 1950) & (obs.year < 1980))
return 'reference'
else if (obs.year >= 2014)
return 'now'
else
return 'junk'
})
})
.select('city', 'date', 'yearGroup', 'year', 'season', 'precip_sum', 'period')
.groupby(['city', 'yearGroup', 'year', 'season', 'period'])
.rollup({
precip_sum: d => op.sum(d.precip_sum),
})
.groupby(['city', 'season', 'period'])
.rollup({
avg_precip_sum: d => op.mean(d.precip_sum),
})
.view()
Plot.plot((() => {
const n = 4; // number of facet columns
const keys = Array.from(d3.union(precip_sum_by_seasons.map((d) => d.city)));
const index = new Map(keys.map((key, i) => [key, i]));
const fx = (key) => index.get(key) % n;
const fy = (key) => Math.floor(index.get(key) / n);
return {
height: 550,
width: 1200,
axis: null,
y: {insetTop: 20},
fx: {padding: 0.02},
marks: [
Plot.barY(precip_sum_by_seasons, {
x: "yearGroup",
y: "avg_precip_sum",
fx: "city",
fy: "season",
opacity: 0.6,
channels: {
avg_precip_sum: {
value: "avg_precip_sum",
label: "avg_precip_sum sum"
},
yearGroup: "yearGroup",
city: "city",
season: "season"
},
tip: {
format: {
x: false,
y: false,
fx: false,
fy: false,
yearGroup: (d) => (`${d}`)
}
}
}),
Plot.axisY({
tickSpacing: 20,
nice: true,
label: "мм"
}),
Plot.axisX({
tickSize: 0,
tickSpacing: 120,
label: null,
tickFormat: (d) => {if (getUniformlyDistributedElements(yearStarts, 5).includes(d))
return `${d}`
else
return ""}
}),
// подписи городов
// Plot.text(keys, {fx, fy, frameAnchor: "top-left", dx: 6, dy: 6})
]
};
})())
new_year_data.view()
snowyDatesDecades
snowyDatesPeriodsBase.filter(
aq.escape((obs) => obs.firstStableSnowDays < 300)
)
.groupby('period', 'city')
.rollup({
count: d => op.mean(d.firstStableSnowDays),
})
.derive({
avg_date: aq.escape((obs) => {
var fillYear = 2020
var new_date = new Date(new Date(fillYear, 6, 1).getTime() + parseInt(obs.count) * 60 * 60 * 24 * 1000)
if (obs.count > 300)
return null
if (new_date.getMonth() > 8)
return new Date(2020, new_date.getMonth(), new_date.getDate()).toLocaleString()
else
return new Date(2021, new_date.getMonth(), new_date.getDate()).toLocaleString()
})
})
.filter(
aq.escape((obs) => obs.period != 'junk')
)
// .filter(
// aq.escape((obs) => obs.city != 'Краснодар' & obs.city != 'Ростов-на-Дону')
// )
.groupby('city')
.pivot(['period'], ['count', 'avg_date'])
.objects()
snowyDatesPeriodsBase.filter(
aq.escape((obs) => obs.firstStableSnowDays >= 300)
)
.groupby('period', 'city')
.rollup({
bad_years: d => op.count(),
})
.filter(
aq.escape((obs) => obs.period != 'junk')
)
.view()
aq.from(snowSize).view()
snowSize
snowDT.view()
aq.from(snowDays).view()
function getSeason(date) {
let month = date.getMonth();
if (month >= 2 && month <= 4)
return 'весна'
else if (month >= 5 && month <= 7)
return 'лето';
else if (month >= 8 && month <= 10)
return 'осень';
else
return 'зима';
}
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.
