2569718930@qq.com
1115 lines
42 KiB
TypeScript
1115 lines
42 KiB
TypeScript
"use client";
|
|
|
|
import clsx from "clsx";
|
|
import { useEffect, useMemo, useState } from "react";
|
|
import Link from "next/link";
|
|
import { ExternalLink } from "lucide-react";
|
|
import {
|
|
CartesianGrid,
|
|
Legend,
|
|
Line,
|
|
LineChart as ReLineChart,
|
|
ReferenceLine,
|
|
ResponsiveContainer,
|
|
Tooltip,
|
|
XAxis,
|
|
YAxis,
|
|
} from "recharts";
|
|
import type { AmosData, AirportCurrentConditions, CityDetail, ScanOpportunityRow } from "@/lib/dashboard-types";
|
|
import { buildDebBaselinePath } from "@/lib/temperature-chart-paths";
|
|
import { Panel } from "@/components/dashboard/scan-terminal/Panel";
|
|
import { rowName, temp } from "@/components/dashboard/scan-terminal/utils";
|
|
|
|
const SETTLEMENT_RUNWAY_PAIRS: Record<string, Array<[string, string]>> = {
|
|
shanghai: [["17L", "35R"]],
|
|
beijing: [["01", "19"]],
|
|
guangzhou: [["02L", "20R"]],
|
|
chengdu: [["02L", "20R"]],
|
|
chongqing: [["02L", "20R"]],
|
|
wuhan: [["04", "22"]],
|
|
seoul: [["15R", "33L"]],
|
|
};
|
|
|
|
function normalizeRunwayLabel(value?: string | null) {
|
|
return String(value || "").trim().toUpperCase().replace(/\s+/g, "");
|
|
}
|
|
|
|
function normalizeCityKey(value?: string | null) {
|
|
return String(value || "").trim().toLowerCase().replace(/[\s_-]+/g, "");
|
|
}
|
|
|
|
function pairKey(pair: [string, string]) {
|
|
return pair.map(normalizeRunwayLabel).sort().join("/");
|
|
}
|
|
|
|
function buildRunwayPlates(
|
|
amos: AmosData | null | undefined,
|
|
row: ScanOpportunityRow | null,
|
|
settlementObs?: Array<{ ts: number; value: number }>,
|
|
) {
|
|
if (!amos) return [];
|
|
const runwayObs = amos.runway_obs || {};
|
|
const runwayPairs = runwayObs.runway_pairs || [];
|
|
const runwayTemps = runwayObs.temperatures || [];
|
|
const pointTemps = runwayObs.point_temperatures || [];
|
|
|
|
const cityKey = normalizeCityKey(row?.city);
|
|
const settlementPairs = SETTLEMENT_RUNWAY_PAIRS[cityKey] || [];
|
|
const settlementKeys = new Set(settlementPairs.map(pairKey));
|
|
|
|
const list: Array<{
|
|
rwy: string;
|
|
isSettlement: boolean;
|
|
tdzTemp: number | null;
|
|
midTemp: number | null;
|
|
endTemp: number | null;
|
|
maxTemp: number | null;
|
|
dailyHigh: number | null;
|
|
trend_15m: number | null;
|
|
}> = [];
|
|
|
|
runwayPairs.forEach((rawPair: any, index: number) => {
|
|
const pair = rawPair as [string, string];
|
|
if (!Array.isArray(pair) || pair.length < 2) return;
|
|
const isSettlement = settlementKeys.has(pairKey(pair));
|
|
|
|
const tdz = validNumber(pointTemps[index]?.tdz_temp);
|
|
const mid = validNumber(pointTemps[index]?.mid_temp);
|
|
const end = validNumber(pointTemps[index]?.end_temp);
|
|
|
|
const historyVals = Array.isArray(runwayTemps[index])
|
|
? (runwayTemps[index] as Array<number | null>).map(validNumber).filter((v): v is number => v !== null)
|
|
: [];
|
|
|
|
const tdzVal = tdz !== null ? [tdz] : [];
|
|
const midVal = mid !== null ? [mid] : [];
|
|
const endVal = end !== null ? [end] : [];
|
|
const allVals = [...historyVals, ...tdzVal, ...midVal, ...endVal];
|
|
|
|
const maxTemp = allVals.length ? Math.max(...allVals) : null;
|
|
const dailyHigh = historyVals.length ? Math.max(...historyVals) : maxTemp;
|
|
|
|
// Calculate 15-minute trend
|
|
const latest = historyVals.length > 0 ? historyVals[historyVals.length - 1] : (tdz ?? mid ?? end ?? null);
|
|
const val15 = historyVals.length > 15 ? historyVals[historyVals.length - 16] : (historyVals.length > 0 ? historyVals[0] : null);
|
|
let trend_15m = (latest !== null && val15 !== null) ? latest - val15 : null;
|
|
|
|
if (isSettlement && settlementObs && settlementObs.length >= 2) {
|
|
const latestObs = settlementObs[settlementObs.length - 1];
|
|
const targetTs = latestObs.ts - 15 * 60 * 1000;
|
|
let closestPoint = settlementObs[0];
|
|
let minDiff = Math.abs(closestPoint.ts - targetTs);
|
|
for (let i = 1; i < settlementObs.length; i++) {
|
|
const diff = Math.abs(settlementObs[i].ts - targetTs);
|
|
if (diff < minDiff) {
|
|
minDiff = diff;
|
|
closestPoint = settlementObs[i];
|
|
}
|
|
}
|
|
if (Math.abs(closestPoint.ts - targetTs) < 5 * 60 * 1000) {
|
|
trend_15m = latestObs.value - closestPoint.value;
|
|
}
|
|
}
|
|
|
|
list.push({
|
|
rwy: `${normalizeRunwayLabel(pair[0])}/${normalizeRunwayLabel(pair[1])}`,
|
|
isSettlement,
|
|
tdzTemp: tdz,
|
|
midTemp: mid,
|
|
endTemp: end,
|
|
maxTemp,
|
|
dailyHigh,
|
|
trend_15m,
|
|
});
|
|
});
|
|
|
|
return list;
|
|
}
|
|
|
|
type ObsPoint = { time?: string | null; temp?: number | null };
|
|
|
|
type EvidenceSeries = {
|
|
key: string;
|
|
label: string;
|
|
source: string;
|
|
color: string;
|
|
dashed?: boolean;
|
|
featured?: boolean;
|
|
smooth?: boolean;
|
|
curve?: "linear" | "monotone" | "stepAfter";
|
|
connectNulls?: boolean;
|
|
showDot?: boolean;
|
|
values: Array<number | null>;
|
|
};
|
|
|
|
type RunwayHistorySeries = {
|
|
key: string;
|
|
label: string;
|
|
rwy: string;
|
|
isSettlement: boolean;
|
|
color: string;
|
|
points: Array<{ ts: number; value: number }>;
|
|
};
|
|
|
|
// Sliding window: keep at most this many observation points (24h at 1-min ≈ 1440)
|
|
const MAX_OBS_POINTS = 1440;
|
|
const HOURLY_CACHE_TTL_MS = 30 * 60 * 1000;
|
|
const ROLLING_WINDOW_BEFORE_MS = 6 * 60 * 60 * 1000;
|
|
const ROLLING_WINDOW_AFTER_LIVE_MS = 45 * 60 * 1000;
|
|
const ROLLING_WINDOW_AFTER_FORECAST_MS = 6 * 60 * 60 * 1000;
|
|
const _hourlyCache = new Map<string, { ts: number; data: HourlyForecast }>();
|
|
const RUNWAY_LINE_COLORS = ["#00897b", "#d97706", "#7c3aed", "#0891b2", "#ea580c", "#64748b"];
|
|
|
|
function validNumber(value: unknown): number | null {
|
|
return typeof value === "number" && Number.isFinite(value) ? value : null;
|
|
}
|
|
|
|
function getCityLocalUtcTimestamp(
|
|
value: string | number | null | undefined,
|
|
tzOffsetSeconds: number,
|
|
referenceLocalDate?: string | null
|
|
): number | null {
|
|
if (value == null) return null;
|
|
|
|
if (typeof value === "number") {
|
|
const d = new Date(value + tzOffsetSeconds * 1000);
|
|
return Date.UTC(
|
|
d.getUTCFullYear(),
|
|
d.getUTCMonth(),
|
|
d.getUTCDate(),
|
|
d.getUTCHours(),
|
|
d.getUTCMinutes()
|
|
);
|
|
}
|
|
|
|
const raw = String(value).trim();
|
|
if (!raw) return null;
|
|
|
|
if (raw.includes("T") || raw.includes("Z") || raw.includes("-")) {
|
|
const d = new Date(raw);
|
|
if (!Number.isNaN(d.getTime())) {
|
|
const localMs = d.getTime() + tzOffsetSeconds * 1000;
|
|
const localDate = new Date(localMs);
|
|
return Date.UTC(
|
|
localDate.getUTCFullYear(),
|
|
localDate.getUTCMonth(),
|
|
localDate.getUTCDate(),
|
|
localDate.getUTCHours(),
|
|
localDate.getUTCMinutes()
|
|
);
|
|
}
|
|
}
|
|
|
|
const m = raw.match(/(\d{1,2}):(\d{2})/);
|
|
if (m) {
|
|
const h = +m[1];
|
|
const min = +m[2];
|
|
|
|
let year = new Date().getUTCFullYear();
|
|
let month = new Date().getUTCMonth();
|
|
let date = new Date().getUTCDate();
|
|
|
|
if (referenceLocalDate) {
|
|
const dateParts = referenceLocalDate.split("-");
|
|
if (dateParts.length === 3) {
|
|
year = parseInt(dateParts[0]);
|
|
month = parseInt(dateParts[1]) - 1;
|
|
date = parseInt(dateParts[2]);
|
|
}
|
|
}
|
|
|
|
return Date.UTC(year, month, date, h, min);
|
|
}
|
|
|
|
return null;
|
|
}
|
|
|
|
function formatTimestamp(ts: number): string {
|
|
const d = new Date(ts);
|
|
return `${String(d.getUTCHours()).padStart(2, "0")}:${String(d.getUTCMinutes()).padStart(2, "0")}`;
|
|
}
|
|
|
|
function normObs(points: ObsPoint[] | null | undefined, tzOffsetSeconds: number, limit = MAX_OBS_POINTS) {
|
|
return (points || [])
|
|
.filter((p) => validNumber(p.temp) !== null && p.time)
|
|
.map((p) => ({
|
|
ts: getCityLocalUtcTimestamp(p.time, tzOffsetSeconds)!,
|
|
value: Number(p.temp),
|
|
}))
|
|
.filter((p) => p.ts !== null)
|
|
.slice(-limit);
|
|
}
|
|
|
|
function seriesStats(values: Array<number | null>) {
|
|
const nums = values.filter((v): v is number => validNumber(v) !== null);
|
|
const latest = nums.length ? nums[nums.length - 1] : null;
|
|
const high = nums.length ? Math.max(...nums) : null;
|
|
const first15 = nums.length > 1 ? nums[Math.max(0, nums.length - 15)] : null;
|
|
const delta15 = latest !== null && first15 !== null ? latest - first15 : null;
|
|
return { latest, high, delta15 };
|
|
}
|
|
|
|
function isSettlementRunway(row: ScanOpportunityRow | null, rwy: string) {
|
|
const cityKey = normalizeCityKey(row?.city);
|
|
const settlementPairs = SETTLEMENT_RUNWAY_PAIRS[cityKey] || [];
|
|
if (!settlementPairs.length) return false;
|
|
const normalized = rwy
|
|
.split("/")
|
|
.map(normalizeRunwayLabel)
|
|
.filter(Boolean)
|
|
.sort()
|
|
.join("/");
|
|
return settlementPairs.some((pair) => pairKey(pair) === normalized);
|
|
}
|
|
|
|
function runwayLabelFromPair(rawPair: unknown, index: number) {
|
|
if (Array.isArray(rawPair) && rawPair.length >= 2) {
|
|
return `${normalizeRunwayLabel(rawPair[0])}/${normalizeRunwayLabel(rawPair[1])}`;
|
|
}
|
|
return `RWY ${index + 1}`;
|
|
}
|
|
|
|
type HourlyForecast = {
|
|
forecastTodayHigh?: number | null;
|
|
localTime?: string | null;
|
|
times: string[];
|
|
temps: Array<number | null>;
|
|
modelCurves?: Record<string, Array<number | null>>;
|
|
runwayPlateHistory?: Record<string, Array<Record<string, unknown>>>;
|
|
amos?: AmosData | null;
|
|
airportCurrent?: AirportCurrentConditions | null;
|
|
airportPrimary?: AirportCurrentConditions | null;
|
|
} | null;
|
|
|
|
function parseRunwayHistoryValue(point: Record<string, unknown>) {
|
|
return validNumber(point.max_temp_c) ?? validNumber(point.temp_c) ?? validNumber(point.temp) ?? validNumber(point.value);
|
|
}
|
|
|
|
function parseRunwayHistoryTime(
|
|
point: Record<string, unknown>,
|
|
tzOffset: number,
|
|
localDateStr: string,
|
|
) {
|
|
return getCityLocalUtcTimestamp(
|
|
(point.timestamp as string | number | null | undefined) ??
|
|
(point.time as string | number | null | undefined) ??
|
|
(point.observed_at as string | number | null | undefined),
|
|
tzOffset,
|
|
localDateStr,
|
|
);
|
|
}
|
|
|
|
function buildRunwayHistorySeries(
|
|
row: ScanOpportunityRow | null,
|
|
hourly: HourlyForecast,
|
|
tzOffset: number,
|
|
localDateStr: string,
|
|
): RunwayHistorySeries[] {
|
|
const directHistory =
|
|
hourly?.runwayPlateHistory ??
|
|
((hourly?.amos as any)?.runway_plate_history as Record<string, Array<Record<string, unknown>>> | undefined) ??
|
|
((row as any)?.runway_plate_history as Record<string, Array<Record<string, unknown>>> | undefined);
|
|
|
|
if (directHistory && typeof directHistory === "object") {
|
|
return Object.entries(directHistory)
|
|
.map(([rwy, rawPoints], index) => {
|
|
const normalizedRwy = String(rwy || `RWY ${index + 1}`).trim();
|
|
const points = (Array.isArray(rawPoints) ? rawPoints : [])
|
|
.map((point) => {
|
|
const ts = parseRunwayHistoryTime(point, tzOffset, localDateStr);
|
|
const value = parseRunwayHistoryValue(point);
|
|
return ts !== null && value !== null ? { ts, value } : null;
|
|
})
|
|
.filter((point): point is { ts: number; value: number } => point !== null)
|
|
.sort((a, b) => a.ts - b.ts)
|
|
.slice(-MAX_OBS_POINTS);
|
|
const isSettlement = isSettlementRunway(row, normalizedRwy);
|
|
return {
|
|
key: `runway_${index}`,
|
|
label: `${normalizedRwy}${isSettlement ? (row ? " 结算跑道" : " Settlement") : ""}`,
|
|
rwy: normalizedRwy,
|
|
isSettlement,
|
|
color: isSettlement ? "#009688" : RUNWAY_LINE_COLORS[index % RUNWAY_LINE_COLORS.length],
|
|
points,
|
|
};
|
|
})
|
|
.filter((series) => series.points.length > 1);
|
|
}
|
|
|
|
const amos = hourly?.amos;
|
|
const runwayObs = amos?.runway_obs;
|
|
const runwayPairs = runwayObs?.runway_pairs || [];
|
|
const runwayTemps = runwayObs?.temperatures || [];
|
|
const anchor =
|
|
getCityLocalUtcTimestamp(amos?.observation_time_local || amos?.observation_time || hourly?.localTime || row?.local_time, tzOffset, localDateStr) ??
|
|
getCityLocalUtcTimestamp(row?.local_time, tzOffset, localDateStr);
|
|
|
|
if (!anchor || !Array.isArray(runwayTemps)) return [];
|
|
|
|
return runwayTemps
|
|
.map((rawTemps, index) => {
|
|
if (!Array.isArray(rawTemps) || rawTemps.length <= 2) return null;
|
|
const rwy = runwayLabelFromPair(runwayPairs[index], index);
|
|
const isSettlement = isSettlementRunway(row, rwy);
|
|
const values = rawTemps
|
|
.map(validNumber)
|
|
.map((value, pointIndex) => {
|
|
if (value === null) return null;
|
|
const minutesFromEnd = rawTemps.length - 1 - pointIndex;
|
|
return {
|
|
ts: anchor - minutesFromEnd * 60 * 1000,
|
|
value,
|
|
};
|
|
})
|
|
.filter((point): point is { ts: number; value: number } => point !== null);
|
|
if (values.length <= 1) return null;
|
|
return {
|
|
key: `runway_${index}`,
|
|
label: `${rwy}${isSettlement ? " 结算跑道" : ""}`,
|
|
rwy,
|
|
isSettlement,
|
|
color: isSettlement ? "#009688" : RUNWAY_LINE_COLORS[index % RUNWAY_LINE_COLORS.length],
|
|
points: values.slice(-MAX_OBS_POINTS),
|
|
};
|
|
})
|
|
.filter((series): series is RunwayHistorySeries => series !== null);
|
|
}
|
|
|
|
// ── Build aligned data rows for the sliding-window chart ────────────────
|
|
|
|
function buildSlidingChartData(
|
|
row: ScanOpportunityRow | null,
|
|
hourly: HourlyForecast,
|
|
) {
|
|
const tzOffset = row?.tz_offset_seconds ?? 0;
|
|
const localDateStr = row?.local_date || new Date().toISOString().slice(0, 10);
|
|
|
|
const settlementObs = normObs(row?.settlement_today_obs || row?.metar_context?.settlement_today_obs, tzOffset);
|
|
const metarObs = normObs(row?.metar_today_obs || row?.metar_context?.today_obs || row?.metar_recent_obs || row?.metar_context?.recent_obs, tzOffset);
|
|
const runwayHistorySeries = buildRunwayHistorySeries(row, hourly, tzOffset, localDateStr);
|
|
|
|
// Collect all timestamps from observations + forecasts
|
|
const allTimes = new Set<number>();
|
|
|
|
const pushObs = (obs: ReturnType<typeof normObs>) => {
|
|
obs.forEach((o) => allTimes.add(o.ts));
|
|
};
|
|
pushObs(settlementObs);
|
|
pushObs(metarObs);
|
|
runwayHistorySeries.forEach((item) => {
|
|
item.points.forEach((point) => allTimes.add(point.ts));
|
|
});
|
|
|
|
// Forecast timestamps
|
|
const forecastTimes: number[] = [];
|
|
if (hourly?.times?.length && hourly?.temps?.length) {
|
|
hourly.times.forEach((t, i) => {
|
|
const ts = getCityLocalUtcTimestamp(t, tzOffset, localDateStr);
|
|
if (ts !== null && i < hourly.temps.length) {
|
|
allTimes.add(ts);
|
|
forecastTimes.push(ts);
|
|
}
|
|
});
|
|
}
|
|
|
|
// Sort timestamps
|
|
const sorted = [...allTimes].sort((a, b) => a - b);
|
|
if (!sorted.length) return { data: [], series: [] };
|
|
|
|
// Build a lookup: timestamp → index in the sorted array
|
|
const tsToIdx = new Map<number, number>();
|
|
sorted.forEach((ts, i) => tsToIdx.set(ts, i));
|
|
|
|
const n = sorted.length;
|
|
const na = (): Array<number | null> => Array.from({ length: n }, () => null);
|
|
|
|
const series: EvidenceSeries[] = [];
|
|
|
|
runwayHistorySeries.forEach((item) => {
|
|
const vals = na();
|
|
item.points.forEach((o) => {
|
|
const idx = tsToIdx.get(o.ts);
|
|
if (idx !== undefined) vals[idx] = o.value;
|
|
});
|
|
if (vals.some((v) => v !== null)) {
|
|
series.push({
|
|
key: item.key,
|
|
label: item.label,
|
|
source: "Runway",
|
|
color: item.color,
|
|
dashed: !item.isSettlement,
|
|
featured: item.isSettlement,
|
|
curve: "monotone",
|
|
connectNulls: true,
|
|
showDot: item.isSettlement,
|
|
values: vals,
|
|
});
|
|
}
|
|
});
|
|
|
|
// Settlement
|
|
const sVals = na();
|
|
settlementObs.forEach((o) => {
|
|
const idx = tsToIdx.get(o.ts);
|
|
if (idx !== undefined) sVals[idx] = o.value;
|
|
});
|
|
if (!runwayHistorySeries.length && sVals.some((v) => v !== null)) {
|
|
const cityKey = String(row?.city || "").toLowerCase().trim();
|
|
const runwaySensorCities = new Set([
|
|
'beijing', 'shanghai', 'guangzhou', 'shenzhen', 'qingdao',
|
|
'chengdu', 'chongqing', 'wuhan', // AMSC runway sensors
|
|
'seoul', 'busan', // AMOS runway sensors
|
|
]);
|
|
const isHKO = cityKey === 'hong kong' || cityKey === 'lau fau shan' || cityKey.includes('hongkong') || cityKey.includes('laufau');
|
|
const isTokyo = cityKey === 'tokyo';
|
|
const isSingapore = cityKey === 'singapore';
|
|
const isWeatherStation = !runwaySensorCities.has(cityKey)
|
|
&& !isHKO && !isTokyo && !isSingapore;
|
|
|
|
const runwayHeaderLabel = isHKO ? '参考站点 (1分钟)'
|
|
: isTokyo ? '机场气象站 (10分钟)'
|
|
: isSingapore ? '航站楼温度'
|
|
: isWeatherStation ? '气象站实测'
|
|
: '跑道实测 (1分钟)';
|
|
|
|
series.push({
|
|
key: "settlement",
|
|
label: runwayHeaderLabel,
|
|
source: row?.metar_context?.station || row?.airport || "Settlement",
|
|
color: "#009688",
|
|
featured: true,
|
|
curve: "monotone",
|
|
connectNulls: true,
|
|
values: sVals,
|
|
});
|
|
}
|
|
|
|
// METAR
|
|
const mVals = na();
|
|
metarObs.forEach((o) => {
|
|
const idx = tsToIdx.get(o.ts);
|
|
if (idx !== undefined) mVals[idx] = o.value;
|
|
});
|
|
if (mVals.some((v) => v !== null)) {
|
|
series.push({
|
|
key: "metar",
|
|
label: "METAR",
|
|
source: row?.airport || "METAR",
|
|
color: "#0ea5e9",
|
|
dashed: true,
|
|
curve: "stepAfter",
|
|
connectNulls: true,
|
|
showDot: true,
|
|
values: mVals,
|
|
});
|
|
}
|
|
|
|
// DEB forecast curve
|
|
if (hourly?.times?.length && hourly?.temps?.length) {
|
|
const debPath = buildDebBaselinePath(
|
|
hourly.times,
|
|
hourly.temps,
|
|
row?.deb_prediction,
|
|
hourly.localTime || row?.local_time,
|
|
hourly.forecastTodayHigh,
|
|
);
|
|
const debVals = na();
|
|
hourly.times.forEach((t, i) => {
|
|
const ts = getCityLocalUtcTimestamp(t, tzOffset, localDateStr);
|
|
const idx = ts !== null ? tsToIdx.get(ts) : undefined;
|
|
if (idx !== undefined && i < debPath.debTemps.length) {
|
|
debVals[idx] = validNumber(debPath.debTemps[i]);
|
|
}
|
|
});
|
|
if (debVals.some((v) => v !== null)) {
|
|
series.push({
|
|
key: "hourly_forecast",
|
|
label: "DEB Forecast",
|
|
source: "DEB Hourly",
|
|
color: "#f97316",
|
|
featured: true,
|
|
smooth: true,
|
|
curve: "monotone",
|
|
connectNulls: true,
|
|
values: debVals,
|
|
});
|
|
}
|
|
|
|
// Per-model hourly curves
|
|
if (hourly.modelCurves) {
|
|
const modelColors = ["#2563eb", "#7c3aed", "#059669", "#d97706", "#dc2626", "#0891b2"];
|
|
Object.keys(hourly.modelCurves).forEach((model, idx) => {
|
|
const modelTemps = hourly.modelCurves![model];
|
|
if (!modelTemps?.length) return;
|
|
const finiteModelTemps = modelTemps
|
|
.map(validNumber)
|
|
.filter((v): v is number => v !== null);
|
|
if (
|
|
finiteModelTemps.length < 2 ||
|
|
Math.max(...finiteModelTemps) - Math.min(...finiteModelTemps) < 0.05
|
|
) {
|
|
return;
|
|
}
|
|
const vals = na();
|
|
hourly.times.forEach((t, i) => {
|
|
const ts = getCityLocalUtcTimestamp(t, tzOffset, localDateStr);
|
|
const x = ts !== null ? tsToIdx.get(ts) : undefined;
|
|
if (x !== undefined && i < modelTemps.length) vals[x] = validNumber(modelTemps[i]);
|
|
});
|
|
if (vals.some((v) => v !== null)) {
|
|
series.push({
|
|
key: `model_curve_${model}`,
|
|
label: model,
|
|
source: "Multi-model hourly",
|
|
color: modelColors[idx % modelColors.length],
|
|
dashed: true,
|
|
smooth: true,
|
|
curve: "monotone",
|
|
connectNulls: true,
|
|
values: vals,
|
|
});
|
|
}
|
|
});
|
|
}
|
|
}
|
|
|
|
// Fallback: if no series, use current temp as a flat line
|
|
if (!series.length) {
|
|
const fallback = validNumber(row?.current_temp) ?? validNumber(row?.deb_prediction) ?? validNumber(row?.target_threshold);
|
|
if (fallback !== null) {
|
|
const vals = na().map(() => fallback);
|
|
series.push({
|
|
key: "current",
|
|
label: "Current",
|
|
source: "Live",
|
|
color: "#009688",
|
|
featured: true,
|
|
curve: "monotone",
|
|
connectNulls: true,
|
|
values: vals,
|
|
});
|
|
}
|
|
}
|
|
|
|
// Build data rows: one per timestamp
|
|
const data = sorted.map((ts, i) => {
|
|
const point: Record<string, string | number | null> = {
|
|
label: formatTimestamp(ts),
|
|
ts,
|
|
};
|
|
series.forEach((s) => { point[s.key] = s.values[i]; });
|
|
return point;
|
|
});
|
|
|
|
return { data, series };
|
|
}
|
|
|
|
function hasNumericValue(row: Record<string, string | number | null>, keys: string[]) {
|
|
return keys.some((key) => validNumber(row[key]) !== null);
|
|
}
|
|
|
|
function buildRollingWindowData(
|
|
data: Array<Record<string, string | number | null>>,
|
|
series: EvidenceSeries[],
|
|
row: ScanOpportunityRow | null,
|
|
hourly: HourlyForecast,
|
|
) {
|
|
if (data.length <= 1) return data;
|
|
|
|
const liveKeys = series
|
|
.filter((item) => item.key !== "hourly_forecast" && !item.key.startsWith("model_curve_"))
|
|
.map((item) => item.key);
|
|
const forecastKeys = series
|
|
.filter((item) => item.key === "hourly_forecast" || item.key.startsWith("model_curve_"))
|
|
.map((item) => item.key);
|
|
|
|
const timestampRows = data
|
|
.filter((point) => typeof point.ts === "number")
|
|
.sort((a, b) => Number(a.ts) - Number(b.ts));
|
|
if (!timestampRows.length) return data;
|
|
|
|
const latestLiveTs = [...timestampRows]
|
|
.reverse()
|
|
.find((point) => hasNumericValue(point, liveKeys))?.ts as number | undefined;
|
|
|
|
const tzOffset = row?.tz_offset_seconds ?? 0;
|
|
const localDateStr = row?.local_date || new Date().toISOString().slice(0, 10);
|
|
const currentLocalTs = getCityLocalUtcTimestamp(
|
|
hourly?.localTime || row?.local_time,
|
|
tzOffset,
|
|
localDateStr,
|
|
);
|
|
const maxDataTs = Number(timestampRows[timestampRows.length - 1].ts);
|
|
const anchor = latestLiveTs ?? currentLocalTs ?? maxDataTs;
|
|
const afterMs = latestLiveTs ? ROLLING_WINDOW_AFTER_LIVE_MS : ROLLING_WINDOW_AFTER_FORECAST_MS;
|
|
const start = anchor - ROLLING_WINDOW_BEFORE_MS;
|
|
const end = anchor + afterMs;
|
|
|
|
const visible = timestampRows.filter((point) => {
|
|
const ts = Number(point.ts);
|
|
if (ts < start || ts > end) return false;
|
|
return hasNumericValue(point, liveKeys) || hasNumericValue(point, forecastKeys);
|
|
});
|
|
|
|
return visible.length >= 2 ? visible : timestampRows.slice(-120);
|
|
}
|
|
|
|
// ── Model summary cards (daily high point predictions) ─────────────────
|
|
|
|
function buildModelSummaryCards(row: ScanOpportunityRow | null): EvidenceSeries[] {
|
|
return Object.entries(row?.model_cluster_sources || {})
|
|
.map(([label, value]) => [label, validNumber(value)] as const)
|
|
.filter((entry): entry is readonly [string, number] => entry[1] !== null)
|
|
.slice(0, 4)
|
|
.map(([label, value], index) => ({
|
|
key: `model_summary_${index}`,
|
|
label,
|
|
source: "Multi-model daily high",
|
|
color: ["#2563eb", "#14b8a6", "#7c3aed", "#64748b"][index] || "#64748b",
|
|
dashed: true,
|
|
values: [value],
|
|
}));
|
|
}
|
|
|
|
// ── Market temperature ticks for Y-axis ─────────────────────────────────
|
|
|
|
function parseTemperatureOptionsFromText(value?: string | null) {
|
|
const raw = String(value || "");
|
|
const matches = raw.match(/-?\d+(?:\.\d+)?/g) || [];
|
|
return matches.map(Number).filter((v) => Number.isFinite(v) && v > -80 && v < 80);
|
|
}
|
|
|
|
function buildMarketTemperatureOptions(row: ScanOpportunityRow | null) {
|
|
const buckets = row?.distribution_full?.length
|
|
? row.distribution_full
|
|
: row?.distribution_preview;
|
|
const values = new Set<number>();
|
|
(buckets || []).forEach((b) => {
|
|
const v = validNumber(b.value);
|
|
if (v !== null) values.add(v);
|
|
parseTemperatureOptionsFromText(b.label).forEach((x) => values.add(x));
|
|
});
|
|
[row?.target_lower, row?.target_upper, row?.target_value, row?.target_threshold]
|
|
.forEach((v) => { if (validNumber(v) !== null) values.add(validNumber(v)!); });
|
|
parseTemperatureOptionsFromText(row?.target_label).forEach((x) => values.add(x));
|
|
|
|
const sorted = [...values].sort((a, b) => a - b);
|
|
if (sorted.length) return sorted;
|
|
const t = validNumber(row?.target_threshold) ?? validNumber(row?.target_value);
|
|
if (t === null) return null;
|
|
return [t - 2, t - 1, t, t + 1, t + 2];
|
|
}
|
|
|
|
function buildChartDomain(
|
|
ticks: number[] | null,
|
|
series: EvidenceSeries[],
|
|
visibleData?: Array<Record<string, string | number | null>>,
|
|
): [number, number] | ["auto", "auto"] {
|
|
const vals = visibleData?.length
|
|
? visibleData.flatMap((point) => series.map((s) => point[s.key])).filter((v): v is number => validNumber(v) !== null)
|
|
: series.flatMap((s) => s.values).filter((v): v is number => validNumber(v) !== null);
|
|
const all = [...(ticks || []), ...vals];
|
|
if (!all.length) return ["auto", "auto"];
|
|
const min = Math.min(...all);
|
|
const max = Math.max(...all);
|
|
const span = Math.max(1, max - min);
|
|
const pad = Math.max(0.5, span * 0.08);
|
|
return [Number((min - pad).toFixed(1)), Number((max + pad).toFixed(1))];
|
|
}
|
|
|
|
// ── Main component ─────────────────────────────────────────────────────
|
|
|
|
export function LiveTemperatureThresholdChart({
|
|
isEn,
|
|
row,
|
|
allRows = [],
|
|
}: {
|
|
isEn: boolean;
|
|
row: ScanOpportunityRow | null;
|
|
allRows?: ScanOpportunityRow[];
|
|
}) {
|
|
const [hourly, setHourly] = useState<HourlyForecast>(null);
|
|
const city = String(row?.city || "").toLowerCase().trim();
|
|
|
|
useEffect(() => {
|
|
if (!city) return;
|
|
const cached = _hourlyCache.get(city);
|
|
if (cached && Date.now() - cached.ts < HOURLY_CACHE_TTL_MS) {
|
|
setHourly(cached.data);
|
|
return;
|
|
}
|
|
let cancelled = false;
|
|
fetch(`/api/city/${encodeURIComponent(city)}/detail?depth=full&force_refresh=false`, {
|
|
cache: "no-store",
|
|
headers: { Accept: "application/json" },
|
|
})
|
|
.then(async (res) => {
|
|
if (!res.ok) return null;
|
|
return res.json() as Promise<CityDetail>;
|
|
})
|
|
.then((json) => {
|
|
const hourlySource = (json as any)?.hourly ?? (json as any)?.timeseries?.hourly;
|
|
if (cancelled || !json || !hourlySource) return;
|
|
const data: HourlyForecast = {
|
|
forecastTodayHigh: json.forecast?.today_high ?? null,
|
|
localTime: json.local_time || null,
|
|
times: hourlySource.times || [],
|
|
temps: hourlySource.temps || [],
|
|
modelCurves: (json.models_hourly ?? (json as any)?.timeseries?.models_hourly)?.curves || undefined,
|
|
runwayPlateHistory: (json as any)?.runway_plate_history || (json.amos as any)?.runway_plate_history || undefined,
|
|
amos: json.amos || null,
|
|
airportCurrent: json.airport_current || null,
|
|
airportPrimary: json.airport_primary || null,
|
|
};
|
|
_hourlyCache.set(city, { ts: Date.now(), data });
|
|
setHourly(data);
|
|
})
|
|
.catch(() => {});
|
|
return () => { cancelled = true; };
|
|
}, [city]);
|
|
|
|
const { data, series } = useMemo(() => buildSlidingChartData(row, hourly), [row, hourly]);
|
|
const visibleData = useMemo(
|
|
() => buildRollingWindowData(data, series, row, hourly),
|
|
[data, series, row, hourly],
|
|
);
|
|
|
|
const tzOffset = row?.tz_offset_seconds ?? 0;
|
|
const settlementObs = useMemo(() => {
|
|
return normObs(row?.settlement_today_obs || row?.metar_context?.settlement_today_obs, tzOffset);
|
|
}, [row, tzOffset]);
|
|
|
|
const runwayPlates = useMemo(() => buildRunwayPlates(hourly?.amos, row, settlementObs), [hourly?.amos, row, settlementObs]);
|
|
const hasRunwayData = runwayPlates.length > 0;
|
|
const settlementPlate = useMemo(() => runwayPlates.find((p) => p.isSettlement), [runwayPlates]);
|
|
|
|
const cityKey = String(row?.city || "").toLowerCase().trim();
|
|
const runwaySensorCities = new Set([
|
|
'beijing', 'shanghai', 'guangzhou', 'shenzhen', 'qingdao',
|
|
'chengdu', 'chongqing', 'wuhan', // AMSC runway sensors
|
|
'seoul', 'busan', // AMOS runway sensors
|
|
]);
|
|
const isHKO = cityKey === 'hong kong' || cityKey === 'lau fau shan' || cityKey.includes('hongkong') || cityKey.includes('laufau');
|
|
const isTokyo = cityKey === 'tokyo';
|
|
const isSingapore = cityKey === 'singapore';
|
|
const isWeatherStation = !runwaySensorCities.has(cityKey)
|
|
&& !isHKO && !isTokyo && !isSingapore;
|
|
|
|
const runwayHeaderLabel = isHKO ? '参考站点 (1分钟)'
|
|
: isTokyo ? '机场气象站 (10分钟)'
|
|
: isSingapore ? '航站楼温度'
|
|
: isWeatherStation ? '气象站实测'
|
|
: '跑道实测 (1分钟)';
|
|
|
|
const metarHeaderLabel = isHKO ? '天文台实测 (10分钟)'
|
|
: 'METAR 结算 (30分钟)';
|
|
|
|
const runwayHighLabel = isHKO ? '参考站点'
|
|
: isTokyo ? '机场气象站'
|
|
: isSingapore ? '航站楼'
|
|
: isWeatherStation ? '气象站'
|
|
: '跑道实测';
|
|
|
|
const metarHighLabel = isHKO ? '天文台'
|
|
: 'METAR 官方';
|
|
|
|
const currentRunwayTemp = validNumber(hourly?.amos?.temp_c) ?? validNumber(row?.current_temp) ?? settlementPlate?.maxTemp ?? null;
|
|
const observedHighMetar = validNumber(row?.metar_context?.airport_max_so_far ?? row?.metar_context?.max_temp ?? row?.current_max_so_far) ?? null;
|
|
const observedHighRunway = validNumber(row?.current_max_so_far) ?? settlementPlate?.maxTemp ?? currentRunwayTemp ?? null;
|
|
const wundergroundDailyHigh = validNumber(hourly?.airportCurrent?.max_so_far ?? hourly?.airportPrimary?.max_so_far) ?? null;
|
|
|
|
const modelValues = Object.values(row?.model_cluster_sources || {})
|
|
.map(validNumber)
|
|
.filter((v): v is number => v !== null);
|
|
const modelMin = modelValues.length ? Math.min(...modelValues) : (row?.cluster_core_low ?? null);
|
|
const modelMax = modelValues.length ? Math.max(...modelValues) : (row?.cluster_core_high ?? null);
|
|
const debVal = validNumber(row?.deb_prediction) ?? null;
|
|
|
|
const spread = (modelMax !== null && modelMin !== null) ? modelMax - modelMin : null;
|
|
const spreadLabel = spread === null ? "" : (spread <= 2.0 ? "低分歧" : (spread <= 4.0 ? "中等分歧" : "高分歧"));
|
|
const spreadLabelEn = spread === null ? "" : (spread <= 2.0 ? "Low" : (spread <= 4.0 ? "Medium" : "High"));
|
|
|
|
const formattedUpdateTime = useMemo(() => {
|
|
if (row?.local_date && row?.local_time) {
|
|
return `${row.local_date} ${row.local_time.slice(0, 8)}`;
|
|
}
|
|
const d = new Date();
|
|
return d.toISOString().replace('T', ' ').slice(0, 19);
|
|
}, [row]);
|
|
|
|
const cityThresholds = useMemo(() => {
|
|
if (!row || !allRows || !allRows.length) return [];
|
|
const cityKey = String(row.city || "").toLowerCase().trim();
|
|
const sameCityRows = allRows.filter(
|
|
(r) => String(r.city || "").toLowerCase().trim() === cityKey
|
|
);
|
|
|
|
const seen = new Set<number>();
|
|
const list: { threshold: number; label: string; isBreached: boolean; kind: "gte" | "lte" }[] = [];
|
|
sameCityRows.forEach((r) => {
|
|
const t = Number(r.target_threshold ?? r.target_value ?? r.target_lower ?? r.target_upper);
|
|
if (!Number.isFinite(t) || seen.has(t)) return;
|
|
seen.add(t);
|
|
|
|
const maxTemp = Number(r.current_max_so_far ?? r.current_temp ?? 0);
|
|
const q = String(r.market_question || r.target_label || "").toLowerCase();
|
|
const kind: "gte" | "lte" = q.includes("below") || q.includes("under") || q.includes("lte") ? "lte" : "gte";
|
|
const isBreached = kind === "lte" ? maxTemp > t : maxTemp >= t;
|
|
|
|
list.push({
|
|
threshold: t,
|
|
label: r.target_label || `${t}°C`,
|
|
isBreached,
|
|
kind,
|
|
});
|
|
});
|
|
|
|
return list.sort((a, b) => a.threshold - b.threshold);
|
|
}, [row, allRows]);
|
|
|
|
const marketTicks = useMemo(() => buildMarketTemperatureOptions(row), [row]);
|
|
const chartDomain = useMemo(
|
|
() => buildChartDomain(marketTicks, series, visibleData),
|
|
[marketTicks, series, visibleData],
|
|
);
|
|
|
|
return (
|
|
<Panel title={isEn ? "Live Temperature Trend & Option Threshold Lines" : "实时气温走势与期权阈值线"}>
|
|
<div className="flex h-full min-h-[420px] flex-col">
|
|
{/* Stats bar */}
|
|
<div className="shrink-0 border-b border-slate-200 bg-white px-4 py-3">
|
|
{/* Top Row: Large temperatures */}
|
|
<div className="flex justify-between items-center gap-6 mb-3">
|
|
<div className="flex items-center gap-12">
|
|
<div className="flex flex-col">
|
|
<span className="text-[11px] font-semibold text-slate-500 uppercase tracking-wider">
|
|
{isEn ? "Runway Live (1m)" : `${runwayHeaderLabel}`}
|
|
</span>
|
|
<span className="text-2xl font-bold font-mono text-[#009688] mt-1">
|
|
{temp(currentRunwayTemp)}
|
|
</span>
|
|
</div>
|
|
<div className="flex flex-col">
|
|
<span className="text-[11px] font-semibold text-slate-500 uppercase tracking-wider">
|
|
{isEn ? "METAR Settlement (30m) · Daily High" : `${metarHeaderLabel} · 当日最高`}
|
|
</span>
|
|
<span className="text-2xl font-bold font-mono text-blue-600 mt-1">
|
|
{temp(observedHighMetar)}
|
|
</span>
|
|
</div>
|
|
</div>
|
|
|
|
<div className="hidden sm:flex flex-col items-end text-right">
|
|
<span className="text-[10px] text-slate-400 uppercase font-semibold">
|
|
{isEn ? "Daily Peak" : "当日最高气温"}
|
|
</span>
|
|
<div className="mt-1 flex items-center gap-2 text-xs font-mono text-slate-600">
|
|
<span>{isEn ? "Runway" : runwayHighLabel}: <strong className="text-[#009688]">{temp(observedHighRunway)}</strong></span>
|
|
<span>|</span>
|
|
<span>{isEn ? "METAR" : metarHighLabel}: <strong className="text-blue-600">{temp(observedHighMetar)}</strong></span>
|
|
{wundergroundDailyHigh !== null && (
|
|
<>
|
|
<span>|</span>
|
|
<span>WU: <strong className="text-purple-600">{temp(wundergroundDailyHigh)}</strong></span>
|
|
</>
|
|
)}
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
{/* Bottom Row: Model Range Panel */}
|
|
<div className="grid grid-cols-4 gap-4 border-t border-slate-100 pt-3 text-xs font-mono text-slate-700 bg-slate-50/50 -mx-4 px-4 rounded-b-md">
|
|
<div className="flex flex-col gap-0.5">
|
|
<span className="text-[10px] text-slate-400 uppercase font-semibold">
|
|
{isEn ? "Model Range" : "模型区间"}
|
|
</span>
|
|
<strong className="text-slate-800 font-bold">
|
|
{modelMin !== null && modelMax !== null ? `${temp(modelMin)} - ${temp(modelMax)}` : "--"}
|
|
</strong>
|
|
</div>
|
|
<div className="flex flex-col gap-0.5">
|
|
<span className="text-[10px] text-slate-400 uppercase font-semibold">
|
|
DEB
|
|
</span>
|
|
<strong className="text-blue-600 font-bold">
|
|
{temp(debVal)}
|
|
</strong>
|
|
</div>
|
|
<div className="flex flex-col gap-0.5">
|
|
<span className="text-[10px] text-slate-400 uppercase font-semibold">
|
|
{isEn ? "Spread" : "分歧"}
|
|
</span>
|
|
<strong className={clsx("font-bold", spreadLabel === "高分歧" ? "text-amber-600" : "text-slate-600")}>
|
|
{spread !== null ? `${spread.toFixed(1)}°C` : "--"}
|
|
{spreadLabel && ` · ${isEn ? spreadLabelEn : spreadLabel}`}
|
|
</strong>
|
|
</div>
|
|
<div className="flex flex-col gap-0.5">
|
|
<span className="text-[10px] text-slate-400 uppercase font-semibold">
|
|
{isEn ? "Updated" : "更新时间"}
|
|
</span>
|
|
<strong className="text-slate-800 font-bold">
|
|
{formattedUpdateTime}
|
|
</strong>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
{/* Runway observations */}
|
|
{runwayPlates.length > 0 && (
|
|
<div className="shrink-0 border-b border-slate-200 bg-[#f8fafc] px-3 py-2">
|
|
<div className="flex items-center justify-between text-[11px] font-black text-slate-700 mb-1.5 uppercase">
|
|
<span>{isEn ? "Runway Observations" : "跑道观测"}</span>
|
|
{runwayPlates.some((p) => p.trend_15m !== null && p.trend_15m > 0 && !p.isSettlement) && (
|
|
<span className="text-[10px] bg-amber-50 text-amber-700 border border-amber-200 px-1.5 py-0.5 rounded font-sans">
|
|
{isEn ? "Non-settlement Runway Warming Alert" : "非结算跑道升温提醒"}
|
|
</span>
|
|
)}
|
|
</div>
|
|
<div className="grid gap-1">
|
|
{runwayPlates.map((plate) => (
|
|
<div
|
|
key={plate.rwy}
|
|
className={clsx(
|
|
"grid grid-cols-7 gap-2 items-center border rounded px-2.5 py-1 text-[11px] font-mono",
|
|
plate.isSettlement
|
|
? "border-emerald-200 bg-emerald-50/50 text-emerald-950 font-bold"
|
|
: "border-slate-200 bg-white text-slate-600"
|
|
)}
|
|
>
|
|
<div className="flex items-center gap-1.5 font-sans font-bold text-slate-800">
|
|
{plate.isSettlement && <span className="h-1.5 w-1.5 rounded-full bg-emerald-600 animate-pulse" />}
|
|
<span>{plate.rwy}</span>
|
|
{plate.isSettlement && (
|
|
<span className="text-[9px] bg-teal-200 text-teal-800 px-1 rounded font-normal">
|
|
{isEn ? "Settlement" : "结算"}
|
|
</span>
|
|
)}
|
|
</div>
|
|
<div>TDZ: <strong>{plate.tdzTemp !== null ? `${plate.tdzTemp.toFixed(1)}°C` : "--"}</strong></div>
|
|
<div>MID: <strong>{plate.midTemp !== null ? `${plate.midTemp.toFixed(1)}°C` : "--"}</strong></div>
|
|
<div>END: <strong>{plate.endTemp !== null ? `${plate.endTemp.toFixed(1)}°C` : "--"}</strong></div>
|
|
<div>max: <strong>{plate.maxTemp !== null ? `${plate.maxTemp.toFixed(1)}°C` : "--"}</strong></div>
|
|
<div>high: <strong>{plate.dailyHigh !== null ? `${plate.dailyHigh.toFixed(1)}°C` : "--"}</strong></div>
|
|
<div className={clsx(plate.trend_15m !== null && plate.trend_15m > 0 ? "text-orange-600 font-bold" : "text-slate-500")}>
|
|
15m: <strong>{plate.trend_15m !== null ? `${plate.trend_15m >= 0 ? "+" : ""}${plate.trend_15m.toFixed(1)}°C` : "--"}</strong>
|
|
</div>
|
|
</div>
|
|
))}
|
|
</div>
|
|
</div>
|
|
)}
|
|
|
|
{/* Multi-model list (only when runway data is on chart) */}
|
|
{hasRunwayData && series.some((s) => s.key.startsWith("model_curve_")) && (
|
|
<div className="shrink-0 border-b border-slate-200 bg-white px-4 py-2">
|
|
<div className="flex flex-wrap gap-x-6 gap-y-1 text-[11px]">
|
|
<span className="font-black text-slate-500 uppercase mr-2">
|
|
{isEn ? "Models:" : "多模型:"}
|
|
</span>
|
|
{series
|
|
.filter((s) => s.key.startsWith("model_curve_"))
|
|
.map((s) => {
|
|
const stats = seriesStats(s.values);
|
|
return (
|
|
<span key={s.key} className="inline-flex items-center gap-1.5 font-mono">
|
|
<span className="h-2 w-2 rounded-full shrink-0" style={{ backgroundColor: s.color }} />
|
|
<span className="text-slate-700 font-bold">{s.label}</span>
|
|
<span className="text-slate-500">{temp(stats.latest)}</span>
|
|
</span>
|
|
);
|
|
})}
|
|
</div>
|
|
</div>
|
|
)}
|
|
|
|
{/* Chart */}
|
|
<div className="relative min-h-0 flex-1 p-2">
|
|
<div className="absolute left-3 top-3 z-10 rounded border border-slate-200 bg-white px-2 py-1 text-[10px] font-black text-slate-800 shadow-sm">
|
|
{rowName(row)}
|
|
{row?.market_url ? (
|
|
<Link href={row.market_url} target="_blank" className="ml-1 text-blue-600 hover:underline">
|
|
<ExternalLink size={10} className="inline" />
|
|
</Link>
|
|
) : null}
|
|
</div>
|
|
<ResponsiveContainer width="100%" height="100%">
|
|
<ReLineChart data={visibleData} margin={{ top: 16, right: 28, left: 8, bottom: 8 }}>
|
|
<CartesianGrid stroke="#dbe6ef" strokeDasharray="2 2" />
|
|
<XAxis
|
|
dataKey="label"
|
|
tick={{ fontSize: 10, fill: "#64748b" }}
|
|
tickLine={false}
|
|
axisLine={{ stroke: "#cbd5e1" }}
|
|
interval={Math.max(1, Math.floor(visibleData.length / 8))}
|
|
/>
|
|
<YAxis
|
|
tick={{ fontSize: 10, fill: "#64748b" }}
|
|
tickFormatter={(v) => `${Number(v).toFixed(1)}°`}
|
|
axisLine={{ stroke: "#cbd5e1" }}
|
|
tickLine={false}
|
|
domain={chartDomain}
|
|
ticks={marketTicks ?? undefined}
|
|
/>
|
|
{cityThresholds.map((t, idx) => {
|
|
const isSelected = row && (Number(row.target_threshold ?? row.target_value) === t.threshold);
|
|
const labelText = isEn
|
|
? `${t.kind === "gte" ? "≥" : "≤"} ${t.threshold.toFixed(1)}° [${t.isBreached ? "Excluded" : "Active"}]`
|
|
: `${t.kind === "gte" ? "≥" : "≤"} ${t.threshold.toFixed(1)}° [${t.isBreached ? "已排除" : "活跃"}]`;
|
|
|
|
return (
|
|
<ReferenceLine
|
|
key={idx}
|
|
y={t.threshold}
|
|
stroke={isSelected ? "#3b82f6" : t.isBreached ? "#ef4444" : "#f97316"}
|
|
strokeDasharray={isSelected ? undefined : "4 4"}
|
|
strokeWidth={isSelected ? 2 : 1}
|
|
label={{
|
|
value: labelText,
|
|
fill: isSelected ? "#3b82f6" : t.isBreached ? "#ef4444" : "#f97316",
|
|
fontSize: 9,
|
|
position: isSelected ? "left" : "insideBottomRight",
|
|
}}
|
|
/>
|
|
);
|
|
})}
|
|
<Tooltip
|
|
contentStyle={{
|
|
border: "1px solid #cbd5e1",
|
|
borderRadius: 4,
|
|
fontSize: 11,
|
|
boxShadow: "0 8px 24px rgba(15,23,42,.12)",
|
|
}}
|
|
formatter={(value: unknown) => `${Number(value).toFixed(2)}°`}
|
|
/>
|
|
<Legend
|
|
verticalAlign="bottom"
|
|
height={series.length > 5 ? 56 : 36}
|
|
iconType="plainline"
|
|
wrapperStyle={{ fontSize: 11 }}
|
|
/>
|
|
{series
|
|
.filter((item) => !hasRunwayData || !item.key.startsWith("model_curve_"))
|
|
.map((item) => (
|
|
<Line
|
|
key={item.key}
|
|
type={item.curve || (item.smooth ? "monotone" : "linear")}
|
|
dataKey={item.key}
|
|
name={item.label}
|
|
stroke={item.color}
|
|
strokeWidth={item.featured ? 2 : 1}
|
|
strokeDasharray={item.dashed ? "4 3" : undefined}
|
|
dot={item.showDot ? { r: 2.5, fill: item.color } : false}
|
|
activeDot={{ r: item.featured ? 5 : 4 }}
|
|
connectNulls={item.connectNulls ?? true}
|
|
isAnimationActive={false}
|
|
/>
|
|
))}
|
|
</ReLineChart>
|
|
</ResponsiveContainer>
|
|
</div>
|
|
</div>
|
|
</Panel>
|
|
);
|
|
}
|