2569718930@qq.com
1643 lines
53 KiB
TypeScript
1643 lines
53 KiB
TypeScript
"use client";
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import type { ChartConfiguration } from "chart.js";
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import clsx from "clsx";
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import { startTransition, useMemo } from "react";
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import { useChart } from "@/hooks/useChart";
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import { useCityData, useDashboardStore } from "@/hooks/useDashboardStore";
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import { useI18n } from "@/hooks/useI18n";
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import {
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CityDetail,
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MarketScan,
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MarketTopBucket,
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ProbabilityBucket,
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} from "@/lib/dashboard-types";
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import {
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getHeroMetaItems,
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getModelView,
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getProbabilityView,
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getRiskBadgeLabel,
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getTemperatureChartData,
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getWeatherSummary,
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} from "@/lib/dashboard-utils";
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import {
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normalizeObservationSourceCode,
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normalizeObservationSourceLabel,
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} from "@/lib/source-labels";
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function EmptyState({ text }: { text: string }) {
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return (
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<div style={{ color: "var(--text-muted)", fontSize: "13px" }}>{text}</div>
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);
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}
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function toPercent(value?: number | null) {
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if (value == null) return null;
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const numeric = Number(value);
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if (!Number.isFinite(numeric)) return null;
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return `${(numeric * 100).toFixed(1)}%`;
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}
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function toPriceCents(value?: number | null) {
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if (value == null) return null;
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const numeric = Number(value);
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if (!Number.isFinite(numeric)) return null;
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const normalized = numeric > 1 ? numeric / 100 : numeric;
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const cents = normalized * 100;
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const rounded = Math.round(cents * 10) / 10;
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const text = Number.isInteger(rounded)
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? String(rounded.toFixed(0))
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: String(rounded);
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return `${text}c`;
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}
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function parseTempFromText(value: unknown) {
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const text = String(value || "");
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const match = text.match(/(-?\d+(?:\.\d+)?)/);
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if (!match) return null;
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const numeric = Number(match[1]);
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return Number.isFinite(numeric) ? numeric : null;
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}
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function getBucketTemp(bucket: ProbabilityBucket) {
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if (bucket.value != null) {
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const byValue = Number(bucket.value);
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if (Number.isFinite(byValue)) return byValue;
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}
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return parseTempFromText(bucket.label || bucket.bucket || bucket.range);
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}
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function getMarketYesPrice(scan?: MarketScan | null) {
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if (scan?.market_price != null) {
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const preferred = Number(scan.market_price);
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if (Number.isFinite(preferred)) return preferred;
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}
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if (scan?.yes_token?.implied_probability != null) {
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const implied = Number(scan.yes_token.implied_probability);
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if (Number.isFinite(implied)) return implied;
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}
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return null;
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}
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function isFahrenheitSymbol(symbol?: string | null) {
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return String(symbol || "")
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.toUpperCase()
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.includes("F");
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}
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function displayTempToMarketCelsius(
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value: number | null,
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detail: Pick<CityDetail, "temp_symbol">,
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) {
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if (value == null || !Number.isFinite(value)) return null;
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if (isFahrenheitSymbol(detail.temp_symbol)) {
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return ((value - 32) * 5) / 9;
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}
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return value;
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}
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function formatBucketDisplayLabel(
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bucket: ProbabilityBucket,
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detail: Pick<CityDetail, "temp_symbol">,
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) {
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let bucketLabel = bucket.label || `${bucket.value}${detail.temp_symbol}`;
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if (!bucketLabel) return "";
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let str = String(bucketLabel).toUpperCase().replace(/\s+/g, "");
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const symbol = detail.temp_symbol || "°C";
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if (isFahrenheitSymbol(symbol)) {
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str = str.replace(/℃/g, "°F").replace(/°C/g, "°F");
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} else {
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str = str.replace(/℃/g, "°C").replace(/°F/g, "°C");
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}
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str = str.replace(/°?C($|\+|-)/g, "°C$1");
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str = str.replace(/°?F($|\+|-)/g, "°F$1");
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if (!/[°℃][CF]/.test(str) && /[0-9]/.test(str)) {
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str += symbol;
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}
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return str;
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}
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function getMarketBucketUnit(bucket?: MarketTopBucket | null) {
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return String(bucket?.unit || "").toUpperCase();
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}
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function isMarketBucketAbove(bucket?: MarketTopBucket | null) {
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const text =
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`${bucket?.label || ""} ${bucket?.slug || ""} ${bucket?.question || ""}`
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.toLowerCase()
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.replace(/\s+/g, "");
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return (
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text.includes("+") ||
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text.includes("orhigher") ||
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text.includes("or-higher")
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);
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}
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function isMarketBucketBelow(bucket?: MarketTopBucket | null) {
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const text =
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`${bucket?.label || ""} ${bucket?.slug || ""} ${bucket?.question || ""}`
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.toLowerCase()
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.replace(/\s+/g, "");
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return (
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text.includes("<=") || text.includes("orlower") || text.includes("or-lower")
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);
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}
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function findMarketBucketForDisplayTemp(
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buckets: MarketTopBucket[],
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displayTemp: number | null,
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detail: Pick<CityDetail, "temp_symbol">,
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) {
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if (displayTemp == null || !Number.isFinite(displayTemp)) return null;
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let best: MarketTopBucket | null = null;
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let bestDelta = Number.POSITIVE_INFINITY;
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for (const bucket of buckets) {
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const bucketUnit = String(bucket.unit || "").toUpperCase();
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const compareTemp =
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bucketUnit === "F"
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? displayTemp
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: displayTempToMarketCelsius(displayTemp, detail);
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if (compareTemp == null) continue;
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const lower = bucket.lower != null ? Number(bucket.lower) : null;
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const upper = bucket.upper != null ? Number(bucket.upper) : null;
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if (
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lower != null &&
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upper != null &&
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Number.isFinite(lower) &&
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Number.isFinite(upper) &&
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compareTemp >= lower - 0.01 &&
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compareTemp <= upper + 0.01
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) {
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return bucket;
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}
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const rawTemp = bucket.temp ?? bucket.value ?? null;
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if (rawTemp == null) continue;
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const candidateTemp = Number(rawTemp);
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if (!Number.isFinite(candidateTemp)) continue;
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const delta = Math.abs(candidateTemp - compareTemp);
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if (delta < bestDelta) {
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best = bucket;
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bestDelta = delta;
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}
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}
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const tolerance = isFahrenheitSymbol(detail.temp_symbol) ? 0.56 : 0.26;
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return best && bestDelta <= tolerance ? best : null;
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}
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function marketBucketContainsDisplayTemp(
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bucket: MarketTopBucket | null,
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displayTemp: number | null,
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detail: Pick<CityDetail, "temp_symbol">,
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) {
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if (!bucket || displayTemp == null || !Number.isFinite(displayTemp))
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return false;
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const bucketUnit = getMarketBucketUnit(bucket);
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const compareTemp =
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bucketUnit === "F"
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? displayTemp
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: displayTempToMarketCelsius(displayTemp, detail);
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if (compareTemp == null) return false;
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const lower = bucket.lower != null ? Number(bucket.lower) : null;
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const upper = bucket.upper != null ? Number(bucket.upper) : null;
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if (lower != null && !Number.isFinite(lower)) return false;
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if (upper != null && !Number.isFinite(upper)) return false;
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if (lower != null && upper != null) {
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return compareTemp >= lower - 0.01 && compareTemp <= upper + 0.01;
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}
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if (lower != null && isMarketBucketAbove(bucket)) {
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return compareTemp >= lower - 0.01;
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}
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if (lower != null && isMarketBucketBelow(bucket)) {
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return compareTemp <= lower + 0.01;
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}
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const reference = bucket.temp ?? bucket.value ?? lower;
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const numeric = reference != null ? Number(reference) : null;
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if (numeric == null || !Number.isFinite(numeric)) return false;
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const tolerance = bucketUnit === "F" ? 0.56 : 0.26;
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return Math.abs(compareTemp - numeric) <= tolerance;
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}
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function getAggregatedModelProbabilityForMarketBucket(
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probabilities: ProbabilityBucket[],
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bucket: MarketTopBucket | null,
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detail: Pick<CityDetail, "temp_symbol">,
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) {
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if (!bucket) return null;
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let total = 0;
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let matched = 0;
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for (const probabilityBucket of probabilities) {
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const temp = getBucketTemp(probabilityBucket);
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if (!marketBucketContainsDisplayTemp(bucket, temp, detail)) continue;
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const probability = Number(probabilityBucket.probability);
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if (!Number.isFinite(probability)) continue;
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total += probability;
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matched += 1;
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}
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return matched > 0 ? Math.max(0, Math.min(1, total)) : null;
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}
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type ProbabilityDisplayRow = {
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key: string;
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label: string;
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probability: number;
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marketBucket?: MarketTopBucket | null;
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};
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function formatMarketBucketDisplayLabel(
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bucket: MarketTopBucket,
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detail: Pick<CityDetail, "temp_symbol">,
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) {
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const label = String(bucket.label || "").trim();
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if (label) {
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const unit = getMarketBucketUnit(bucket);
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let normalized = label.toUpperCase().replace(/\s+/g, "");
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if (unit === "F" || isFahrenheitSymbol(detail.temp_symbol)) {
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normalized = normalized
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.replace(/ORHIGHER/g, "+")
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.replace(/ORLOWER/g, "-")
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.replace(/℃/g, "°F")
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.replace(/°C/g, "°F")
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.replace(/(?<=\d)F/g, "°F");
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} else {
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normalized = normalized
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.replace(/ORHIGHER/g, "+")
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.replace(/ORLOWER/g, "-")
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.replace(/℃/g, "°C")
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.replace(/°F/g, "°C")
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.replace(/(?<=\d)C/g, "°C");
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}
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return normalized.replace(/\+/g, "+");
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}
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const unit =
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getMarketBucketUnit(bucket) === "F" ||
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isFahrenheitSymbol(detail.temp_symbol)
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? "°F"
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: "°C";
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const lower = bucket.lower != null ? Number(bucket.lower) : null;
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const upper = bucket.upper != null ? Number(bucket.upper) : null;
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if (
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lower != null &&
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upper != null &&
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Number.isFinite(lower) &&
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Number.isFinite(upper)
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) {
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return `${lower}-${upper}${unit}`;
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}
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const value = bucket.value ?? bucket.temp ?? lower;
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const numeric = value != null ? Number(value) : null;
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if (numeric != null && Number.isFinite(numeric)) {
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return isMarketBucketAbove(bucket)
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? `${numeric}${unit}+`
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: `${numeric}${unit}`;
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}
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return "--";
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}
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type ModelMetadata = NonNullable<
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NonNullable<CityDetail["source_forecasts"]>["open_meteo_multi_model"]
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>["model_metadata"];
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function getModelGroupMeta(
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name: string,
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metadata: ModelMetadata,
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locale: string,
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) {
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const meta = metadata?.[name] || {};
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const tier = String(meta.tier || "").toLowerCase();
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const upperName = String(name || "").toUpperCase();
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if (tier.includes("aifs") || upperName.includes("AIFS")) {
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return {
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key: "aifs",
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label: locale === "en-US" ? "AIFS model" : "AIFS 模型",
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order: 1,
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tone: "blue",
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};
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}
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if (
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tier.includes("europe") ||
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upperName.includes("ICON-EU") ||
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upperName.includes("ICON-D2")
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) {
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return {
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key: "europe",
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label: locale === "en-US" ? "Europe high-resolution" : "欧洲高分辨率",
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order: 2,
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tone: "cyan",
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};
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}
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if (
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tier.includes("north_america") ||
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upperName === "RDPS" ||
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upperName === "HRDPS"
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) {
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return {
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key: "north-america",
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label:
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locale === "en-US" ? "North America high-resolution" : "北美高分辨率",
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order: 3,
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tone: "amber",
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};
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}
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return {
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key: "global",
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label: locale === "en-US" ? "Global baseline" : "全球基准",
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order: 0,
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tone: "neutral",
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};
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}
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function formatModelMetaLine(
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name: string,
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metadata: ModelMetadata,
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locale: string,
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) {
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const meta = metadata?.[name] || {};
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const provider = String(meta.provider || "").trim();
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const model = String(meta.model || "").trim();
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const horizon = String(meta.horizon || "").trim();
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const resolution = Number(meta.resolution_km);
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const parts = [
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provider,
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model && model !== name ? model : "",
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Number.isFinite(resolution)
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? `${resolution}${locale === "en-US" ? " km" : " 公里"}`
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: "",
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horizon,
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].filter(Boolean);
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return parts.join(" · ");
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}
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function normalizeModelNameForVote(name: string) {
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return String(name || "")
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.trim()
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.toLowerCase()
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.replace(/[\s_/-]/g, "");
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}
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function getModelVoteFamily(name: string) {
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const normalized = normalizeModelNameForVote(name);
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if (["icon", "iconeu", "icond2"].includes(normalized)) return "dwd_icon";
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if (["gem", "gdps", "rdps", "hrdps"].includes(normalized)) return "eccc_gem";
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if (["ecmwfaifs", "aifs"].includes(normalized)) return "ecmwf_aifs";
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if (normalized === "ecmwf") return "ecmwf_ifs";
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return normalized || name;
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}
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function getModelVotePriority(name: string) {
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const normalized = normalizeModelNameForVote(name);
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return (
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{
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icond2: 40,
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iconeu: 30,
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icon: 20,
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hrdps: 40,
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rdps: 35,
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gdps: 30,
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gem: 20,
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ecmwfaifs: 30,
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ecmwf: 30,
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gfs: 30,
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jma: 30,
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mgm: 45,
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nws: 45,
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openmeteo: 15,
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}[normalized] || 10
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);
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}
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function getRoundedModelVoteDistribution(
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detail: CityDetail,
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targetDate?: string | null,
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) {
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const view = getModelView(detail, targetDate);
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const representatives = new Map<
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string,
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{ name: string; priority: number; value: number }
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>();
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Object.entries(view.models || {}).forEach(([name, rawValue]) => {
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const normalized = normalizeModelNameForVote(name);
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if (normalized === "lgbm" || normalized.includes("meteoblue")) return;
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const value = Number(rawValue);
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if (!Number.isFinite(value)) return;
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const family = getModelVoteFamily(name);
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const priority = getModelVotePriority(name);
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const current = representatives.get(family);
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if (!current || priority > current.priority) {
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representatives.set(family, { name, priority, value });
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}
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});
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const bucketMap = new Map<number, { count: number; models: string[] }>();
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representatives.forEach(({ name, value }) => {
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const rounded = Math.round(value);
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const row = bucketMap.get(rounded) || { count: 0, models: [] };
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row.count += 1;
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row.models.push(name);
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bucketMap.set(rounded, row);
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});
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|
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const total = representatives.size;
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const rows = Array.from(bucketMap.entries())
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.map(([value, row]) => ({
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count: row.count,
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models: row.models,
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percent: total > 0 ? row.count / total : 0,
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value,
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}))
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.sort((a, b) => b.count - a.count || b.value - a.value);
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|
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return {
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rows,
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total,
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};
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}
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|
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function normalizeMarketProbability(value?: number | null) {
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if (value == null) return null;
|
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const numeric = Number(value);
|
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if (!Number.isFinite(numeric)) return null;
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if (numeric > 1) return Math.max(0, Math.min(1, numeric / 100));
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return Math.max(0, Math.min(1, numeric));
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}
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|
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function normalizeSignedProbability(value?: number | null) {
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|
if (value == null) return null;
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|
const numeric = Number(value);
|
|
if (!Number.isFinite(numeric)) return null;
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if (Math.abs(numeric) > 1) return numeric / 100;
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return numeric;
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}
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|
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function formatSignedPercent(value?: number | null, digits = 1) {
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const normalized = normalizeSignedProbability(value);
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if (normalized == null) return "--";
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const percent = normalized * 100;
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const sign = percent > 0 ? "+" : "";
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return `${sign}${percent.toFixed(digits)}%`;
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}
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|
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function getMarketTopBuckets(scan?: MarketScan | null) {
|
|
const buckets = Array.isArray(scan?.top_buckets) ? scan.top_buckets : [];
|
|
if (!buckets.length) return [];
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|
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return buckets
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.map((item) => ({
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...item,
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probability: normalizeMarketProbability(item.probability),
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}))
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.filter(
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(item): item is MarketTopBucket & { probability: number } =>
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item.probability != null,
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);
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}
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|
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function getMarketAllBuckets(scan?: MarketScan | null) {
|
|
const buckets = Array.isArray(scan?.all_buckets)
|
|
? scan.all_buckets
|
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: Array.isArray(scan?.top_buckets)
|
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? scan.top_buckets
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: [];
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if (!buckets.length) return [];
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|
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return buckets
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.map((item) => ({
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...item,
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probability: normalizeMarketProbability(item.probability),
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}))
|
|
.filter(
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(item): item is MarketTopBucket & { probability: number } =>
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|
item.probability != null,
|
|
);
|
|
}
|
|
|
|
function getMarketTopBucketKey(bucket: MarketTopBucket) {
|
|
if (bucket?.value != null) {
|
|
const valueNum = Number(bucket.value);
|
|
if (Number.isFinite(valueNum)) return `v:${valueNum.toFixed(2)}`;
|
|
}
|
|
|
|
if (bucket?.temp != null) {
|
|
const tempNum = Number(bucket.temp);
|
|
if (Number.isFinite(tempNum)) return `t:${tempNum.toFixed(2)}`;
|
|
}
|
|
|
|
const parsed = parseTempFromText(bucket?.label);
|
|
if (parsed != null) return `l:${parsed.toFixed(2)}`;
|
|
|
|
return `s:${String(bucket?.slug || bucket?.question || bucket?.label || "")}`;
|
|
}
|
|
|
|
function hasLgbmModel(detail: CityDetail, targetDate?: string | null) {
|
|
const view = getModelView(detail, targetDate);
|
|
return Object.keys(view.models || {}).some((name) =>
|
|
normalizeModelNameForVote(name).includes("lgbm"),
|
|
);
|
|
}
|
|
|
|
function formatProbabilityEngineLabel(
|
|
detail: CityDetail,
|
|
targetDate: string | null | undefined,
|
|
locale: string,
|
|
) {
|
|
const view = getProbabilityView(detail, targetDate);
|
|
if (hasLgbmModel(detail, targetDate)) {
|
|
return locale === "en-US" ? "LGBM-calibrated probability" : "LGBM 校准概率";
|
|
}
|
|
const engine = String(view.engine || "")
|
|
.trim()
|
|
.toLowerCase();
|
|
const calibrationMode = String(view.calibrationMode || "")
|
|
.trim()
|
|
.toLowerCase();
|
|
if (engine === "emos" || calibrationMode.includes("emos")) {
|
|
return locale === "en-US" ? "EMOS-calibrated probability" : "EMOS 校准概率";
|
|
}
|
|
return locale === "en-US" ? "Model probability" : "模型概率";
|
|
}
|
|
|
|
export function HeroSummary() {
|
|
const { data } = useCityData();
|
|
const { locale } = useI18n();
|
|
if (!data) return null;
|
|
|
|
const { weatherIcon, weatherText } = getWeatherSummary(data, locale);
|
|
const metaItems = getHeroMetaItems(data, locale);
|
|
const current = data.current || {};
|
|
const settlementSourceCode = normalizeObservationSourceCode(
|
|
current.settlement_source || "metar",
|
|
);
|
|
const settlementIcao = String(current.station_code || data.risk?.icao || "")
|
|
.trim()
|
|
.toUpperCase();
|
|
const settlementSource =
|
|
settlementSourceCode === "metar" && settlementIcao
|
|
? `${settlementIcao} METAR`
|
|
: normalizeObservationSourceLabel(
|
|
current.settlement_source_label || current.settlement_source,
|
|
"METAR",
|
|
).toUpperCase();
|
|
const isMax =
|
|
current.max_so_far != null &&
|
|
current.temp != null &&
|
|
current.max_so_far <= current.temp;
|
|
const currentObsText =
|
|
current.temp != null
|
|
? `${current.temp}${data.temp_symbol} @${current.obs_time || "--"}`
|
|
: data.metar_status?.stale_for_today
|
|
? locale === "en-US"
|
|
? "No same-day METAR"
|
|
: "今日暂无 METAR"
|
|
: "--";
|
|
|
|
return (
|
|
<section className="hero-section">
|
|
<div className="hero-weather">
|
|
<span>
|
|
{weatherIcon} {weatherText}
|
|
</span>
|
|
</div>
|
|
<div className="hero-temp">
|
|
<span className="hero-value">
|
|
{current.temp != null ? current.temp.toFixed(1) : "--"}
|
|
</span>
|
|
<span className="hero-unit">{data.temp_symbol || "°C"}</span>
|
|
</div>
|
|
<div className="hero-max-time">
|
|
{isMax && current.max_temp_time
|
|
? locale === "en-US"
|
|
? `Today's peak temperature appeared at local time ${current.max_temp_time}`
|
|
: `该城市今日最高温出现在当地时间 ${current.max_temp_time}`
|
|
: ""}
|
|
</div>
|
|
<div className="hero-details">
|
|
<div className="hero-item">
|
|
<span className="label">
|
|
{locale === "en-US" ? "Current Obs" : "当前实测"}
|
|
</span>
|
|
<span className="value">{currentObsText}</span>
|
|
</div>
|
|
<div className="hero-item">
|
|
<span className="label">
|
|
{locale === "en-US"
|
|
? `${settlementSource} Anchor`
|
|
: `${settlementSource} 锚点`}
|
|
</span>
|
|
<span className="value highlight">
|
|
{current.wu_settlement != null
|
|
? `${current.wu_settlement}${data.temp_symbol}`
|
|
: "--"}
|
|
</span>
|
|
</div>
|
|
<div className="hero-item">
|
|
<span className="label">
|
|
{locale === "en-US" ? "DEB Forecast" : "DEB 预测"}
|
|
</span>
|
|
<span className="value">
|
|
{data.deb?.prediction != null
|
|
? `${data.deb.prediction}${data.temp_symbol}`
|
|
: "--"}
|
|
</span>
|
|
</div>
|
|
</div>
|
|
<div className="hero-sub">
|
|
{metaItems.map((item) => (
|
|
<span key={item}>{item}</span>
|
|
))}
|
|
</div>
|
|
</section>
|
|
);
|
|
}
|
|
|
|
export function TemperatureChart() {
|
|
const { data } = useCityData();
|
|
const { locale, t } = useI18n();
|
|
const chartData = useMemo(
|
|
() => (data ? getTemperatureChartData(data, locale) : null),
|
|
[data, locale],
|
|
);
|
|
|
|
const canvasRef = useChart(() => {
|
|
if (!data || !chartData) {
|
|
return {
|
|
data: { datasets: [], labels: [] },
|
|
type: "line",
|
|
} satisfies ChartConfiguration<"line">;
|
|
}
|
|
|
|
const datasets: NonNullable<
|
|
ChartConfiguration<"line">["data"]
|
|
>["datasets"] = [];
|
|
|
|
if (chartData.datasets.hasMgmHourly) {
|
|
datasets.push({
|
|
backgroundColor: "rgba(234, 179, 8, 0.05)",
|
|
borderColor: "rgba(234, 179, 8, 0.8)",
|
|
borderWidth: 2,
|
|
data: chartData.datasets.mgmHourlyPoints,
|
|
fill: false,
|
|
label: locale === "en-US" ? "MGM Forecast" : "MGM 预报",
|
|
pointHoverRadius: 6,
|
|
pointRadius: 3,
|
|
spanGaps: true,
|
|
tension: 0.3,
|
|
});
|
|
} else {
|
|
datasets.push({
|
|
backgroundColor: "rgba(77, 163, 255, 0.06)",
|
|
borderColor: "rgba(77, 163, 255, 0.66)",
|
|
borderWidth: 1.5,
|
|
data: chartData.datasets.debPast,
|
|
fill: true,
|
|
label: locale === "en-US" ? "DEB Forecast" : "DEB 预报",
|
|
pointHoverRadius: 3,
|
|
pointRadius: 0,
|
|
tension: 0.3,
|
|
});
|
|
datasets.push({
|
|
borderColor: "rgba(77, 163, 255, 0.36)",
|
|
borderDash: [5, 3],
|
|
borderWidth: 1.5,
|
|
data: chartData.datasets.debFuture,
|
|
fill: false,
|
|
label: locale === "en-US" ? "DEB Forecast" : "DEB 预报",
|
|
pointRadius: 0,
|
|
tension: 0.3,
|
|
});
|
|
}
|
|
|
|
datasets.push({
|
|
backgroundColor: "#4DA3FF",
|
|
borderColor: "#4DA3FF",
|
|
borderWidth: 0,
|
|
data: chartData.datasets.metarPoints,
|
|
fill: false,
|
|
label:
|
|
chartData.observationLabel ||
|
|
(locale === "en-US" ? "METAR Observation" : "METAR 实况"),
|
|
order: 0,
|
|
pointHoverRadius: 7,
|
|
pointRadius: 5,
|
|
});
|
|
|
|
if (chartData.datasets.mgmPoints.some((value) => value != null)) {
|
|
datasets.push({
|
|
backgroundColor: "#facc15",
|
|
borderColor: "#facc15",
|
|
borderWidth: 0,
|
|
data: chartData.datasets.mgmPoints,
|
|
fill: false,
|
|
label: locale === "en-US" ? "MGM Observation" : "MGM 实测",
|
|
order: -1,
|
|
pointHoverRadius: 9,
|
|
pointRadius: 7,
|
|
showLine: false,
|
|
});
|
|
}
|
|
|
|
if (
|
|
!chartData.datasets.hasMgmHourly &&
|
|
Math.abs(chartData.datasets.offset) > 0.3
|
|
) {
|
|
datasets.push({
|
|
borderColor: "rgba(77, 163, 255, 0.22)",
|
|
borderDash: [2, 4],
|
|
borderWidth: 1,
|
|
data: chartData.datasets.temps,
|
|
fill: false,
|
|
label: locale === "en-US" ? "OM Raw" : "OM 原始",
|
|
pointRadius: 0,
|
|
tension: 0.3,
|
|
});
|
|
}
|
|
|
|
return {
|
|
data: {
|
|
datasets,
|
|
labels: chartData.times,
|
|
},
|
|
options: {
|
|
interaction: { intersect: false, mode: "index" },
|
|
maintainAspectRatio: false,
|
|
plugins: {
|
|
legend: { display: false },
|
|
tooltip: {
|
|
backgroundColor: "rgba(15, 23, 42, 0.9)",
|
|
borderColor: "rgba(77, 163, 255, 0.28)",
|
|
borderWidth: 1,
|
|
},
|
|
},
|
|
responsive: true,
|
|
scales: {
|
|
x: {
|
|
grid: { color: "rgba(255,255,255,0.04)" },
|
|
ticks: {
|
|
callback: (_value, index) =>
|
|
typeof index === "number" && index % 3 === 0
|
|
? chartData.times[index]
|
|
: "",
|
|
color: "#6B7A90",
|
|
maxRotation: 0,
|
|
},
|
|
},
|
|
y: {
|
|
grid: { color: "rgba(255,255,255,0.04)" },
|
|
max: chartData.max,
|
|
min: chartData.min,
|
|
ticks: {
|
|
callback: (value) =>
|
|
`${Number(value).toFixed(chartData.yTickStep < 1 ? 1 : 0)}${data.temp_symbol || "°C"}`,
|
|
color: "#6B7A90",
|
|
stepSize: chartData.yTickStep,
|
|
},
|
|
},
|
|
},
|
|
},
|
|
type: "line",
|
|
} satisfies ChartConfiguration<"line">;
|
|
}, [data, chartData, locale]);
|
|
|
|
return (
|
|
<section className="chart-section">
|
|
<h3>{t("section.todayTempTrend")}</h3>
|
|
<div className="chart-wrapper">
|
|
<canvas ref={canvasRef} />
|
|
</div>
|
|
<div className="chart-legend">
|
|
{chartData?.legendText || t("section.chartEmpty")}
|
|
</div>
|
|
</section>
|
|
);
|
|
}
|
|
|
|
export function ProbabilityDistribution({
|
|
detail,
|
|
hideTitle = false,
|
|
targetDate,
|
|
marketScan,
|
|
}: {
|
|
detail: CityDetail;
|
|
hideTitle?: boolean;
|
|
targetDate?: string | null;
|
|
marketScan?: MarketScan | null;
|
|
}) {
|
|
const { locale, t } = useI18n();
|
|
const view = getProbabilityView(detail, targetDate);
|
|
const modelView = getModelView(detail, targetDate);
|
|
const marketYesPrice = getMarketYesPrice(marketScan);
|
|
const marketYesText = toPercent(marketYesPrice);
|
|
const isToday = !targetDate || targetDate === detail.local_date;
|
|
const probabilityEngineLabel = formatProbabilityEngineLabel(
|
|
detail,
|
|
targetDate,
|
|
locale,
|
|
);
|
|
const hasLgbmProbability = hasLgbmModel(detail, targetDate);
|
|
const modelVoteView = useMemo(
|
|
() => getRoundedModelVoteDistribution(detail, targetDate),
|
|
[detail, targetDate],
|
|
);
|
|
const modelVoteHint = modelVoteView.rows
|
|
.slice(0, 2)
|
|
.map(
|
|
(row) =>
|
|
`${row.value}${detail.temp_symbol} ${row.count}/${modelVoteView.total}`,
|
|
)
|
|
.join(" · ");
|
|
const marketTopBuckets = isToday ? getMarketTopBuckets(marketScan) : [];
|
|
const marketAllBuckets = isToday ? getMarketAllBuckets(marketScan) : [];
|
|
const sortedMarketTopBuckets = useMemo(() => {
|
|
const sorted = [...marketTopBuckets].sort(
|
|
(a, b) => Number(b.probability || 0) - Number(a.probability || 0),
|
|
);
|
|
const deduped: Array<MarketTopBucket & { probability: number }> = [];
|
|
const seenKeys = new Set<string>();
|
|
for (const row of sorted) {
|
|
const key = getMarketTopBucketKey(row);
|
|
if (seenKeys.has(key)) continue;
|
|
seenKeys.add(key);
|
|
deduped.push(row);
|
|
if (deduped.length >= 4) break;
|
|
}
|
|
return deduped;
|
|
}, [marketTopBuckets]);
|
|
const useMarketTopBuckets =
|
|
marketScan?.available && sortedMarketTopBuckets.length >= 2;
|
|
const topMarketBucketText = toPercent(sortedMarketTopBuckets[0]?.probability);
|
|
const topProbability = [...(view.probabilities || [])].sort(
|
|
(a, b) => Number(b.probability || 0) - Number(a.probability || 0),
|
|
)[0];
|
|
const topProbabilityText = toPercent(topProbability?.probability);
|
|
const topProbabilityLabel = topProbability
|
|
? formatBucketDisplayLabel(topProbability, detail)
|
|
: null;
|
|
const topProbabilityTemp = topProbability
|
|
? getBucketTemp(topProbability)
|
|
: null;
|
|
const probabilitiesForMarketContracts =
|
|
view.probabilitiesAll?.length > 0
|
|
? view.probabilitiesAll
|
|
: view.probabilities || [];
|
|
const marketContractRows = useMemo<ProbabilityDisplayRow[]>(() => {
|
|
if (!isToday || !marketScan?.available || marketAllBuckets.length === 0) {
|
|
return [];
|
|
}
|
|
|
|
const rows: ProbabilityDisplayRow[] = [];
|
|
const seenKeys = new Set<string>();
|
|
for (const marketBucket of marketAllBuckets) {
|
|
const probability = getAggregatedModelProbabilityForMarketBucket(
|
|
probabilitiesForMarketContracts,
|
|
marketBucket,
|
|
detail,
|
|
);
|
|
|
|
const key =
|
|
marketBucket.slug ||
|
|
marketBucket.label ||
|
|
`${marketBucket.lower ?? marketBucket.value ?? marketBucket.temp}-${marketBucket.upper ?? ""}`;
|
|
if (seenKeys.has(key)) continue;
|
|
seenKeys.add(key);
|
|
rows.push({
|
|
key,
|
|
label: formatMarketBucketDisplayLabel(marketBucket, detail),
|
|
probability: probability ?? 0,
|
|
marketBucket,
|
|
});
|
|
}
|
|
return rows;
|
|
}, [
|
|
detail,
|
|
isToday,
|
|
marketAllBuckets,
|
|
marketScan?.available,
|
|
probabilitiesForMarketContracts,
|
|
]);
|
|
const modelProbabilityRows = useMemo<ProbabilityDisplayRow[]>(
|
|
() =>
|
|
(view.probabilities || []).slice(0, 6).map((bucket, index) => {
|
|
const bucketTemp = getBucketTemp(bucket);
|
|
return {
|
|
key: `${bucket.label || bucket.value || index}`,
|
|
label: formatBucketDisplayLabel(bucket, detail),
|
|
probability: Number(bucket.probability || 0),
|
|
marketBucket: findMarketBucketForDisplayTemp(
|
|
marketAllBuckets,
|
|
bucketTemp,
|
|
detail,
|
|
),
|
|
};
|
|
}),
|
|
[detail, marketAllBuckets, view.probabilities],
|
|
);
|
|
const probabilityRows =
|
|
marketContractRows.length > 0
|
|
? marketContractRows.slice(0, 8)
|
|
: modelProbabilityRows;
|
|
const topContractRow =
|
|
marketContractRows.length > 0
|
|
? marketContractRows.reduce((best, row) =>
|
|
row.probability > best.probability ? row : best,
|
|
)
|
|
: null;
|
|
const displayTopLabel = topContractRow?.label || topProbabilityLabel || null;
|
|
const displayTopProbability =
|
|
topContractRow?.probability ??
|
|
(topProbability?.probability != null
|
|
? Number(topProbability.probability)
|
|
: null);
|
|
const displayTopProbabilityText = toPercent(displayTopProbability);
|
|
const displayUsesMarketBuckets = marketContractRows.length > 0;
|
|
const linkedMarketBucket = useMemo(() => {
|
|
if (topContractRow?.marketBucket) return topContractRow.marketBucket;
|
|
if (topProbabilityTemp == null) return null;
|
|
return findMarketBucketForDisplayTemp(
|
|
marketAllBuckets,
|
|
topProbabilityTemp,
|
|
detail,
|
|
);
|
|
}, [detail, marketAllBuckets, topContractRow, topProbabilityTemp]);
|
|
const priceAnalysis = marketScan?.price_analysis;
|
|
const yesPriceView = priceAnalysis?.yes;
|
|
const noPriceView = priceAnalysis?.no;
|
|
const linkedMarketAsk =
|
|
linkedMarketBucket?.yes_buy ??
|
|
linkedMarketBucket?.market_price ??
|
|
yesPriceView?.ask ??
|
|
null;
|
|
const linkedNoAsk = linkedMarketBucket?.no_buy ?? noPriceView?.ask ?? null;
|
|
const linkedContractLabel =
|
|
topContractRow?.label ||
|
|
(linkedMarketBucket
|
|
? formatMarketBucketDisplayLabel(linkedMarketBucket, detail)
|
|
: null) ||
|
|
topProbabilityLabel ||
|
|
null;
|
|
const aggregatedMarketProbability =
|
|
getAggregatedModelProbabilityForMarketBucket(
|
|
probabilitiesForMarketContracts,
|
|
linkedMarketBucket,
|
|
detail,
|
|
);
|
|
const linkedMarketProbability =
|
|
topContractRow?.probability ??
|
|
aggregatedMarketProbability ??
|
|
(topProbability?.probability != null
|
|
? Number(topProbability.probability)
|
|
: null);
|
|
const linkedMarketProbabilityText = toPercent(linkedMarketProbability);
|
|
const linkedMarketEdge =
|
|
linkedMarketProbability != null && linkedMarketAsk != null
|
|
? linkedMarketProbability - Number(linkedMarketAsk)
|
|
: null;
|
|
const linkedNoEdge =
|
|
linkedMarketProbability != null && linkedNoAsk != null
|
|
? 1 - linkedMarketProbability - Number(linkedNoAsk)
|
|
: null;
|
|
const linkedBestSide =
|
|
linkedMarketBucket && linkedNoEdge != null && linkedMarketEdge != null
|
|
? linkedNoEdge > linkedMarketEdge
|
|
? "no"
|
|
: "yes"
|
|
: null;
|
|
const linkedBestAsk = linkedBestSide === "no" ? linkedNoAsk : linkedMarketAsk;
|
|
const linkedBestEdge =
|
|
linkedBestSide === "no" ? linkedNoEdge : linkedMarketEdge;
|
|
const preferredPriceView = linkedMarketBucket
|
|
? {
|
|
ask: linkedBestAsk,
|
|
edge: linkedBestEdge,
|
|
}
|
|
: priceAnalysis?.best_side === "no"
|
|
? noPriceView
|
|
: yesPriceView;
|
|
const preferredSideLabel = linkedMarketBucket
|
|
? linkedBestSide === "no"
|
|
? "NO"
|
|
: "YES"
|
|
: priceAnalysis?.best_side === "no"
|
|
? locale === "en-US"
|
|
? "NO"
|
|
: "NO"
|
|
: locale === "en-US"
|
|
? "YES"
|
|
: "YES";
|
|
const yesDisplayPrice = linkedMarketBucket
|
|
? linkedMarketAsk
|
|
: yesPriceView?.ask;
|
|
const noDisplayPrice = linkedMarketBucket ? linkedNoAsk : noPriceView?.ask;
|
|
const yesDisplayEdge = linkedMarketBucket
|
|
? linkedMarketEdge
|
|
: yesPriceView?.edge;
|
|
const noDisplayEdge = linkedMarketBucket ? linkedNoEdge : noPriceView?.edge;
|
|
const hasPriceAnalysis =
|
|
isToday &&
|
|
(Boolean(priceAnalysis?.available) ||
|
|
Boolean(marketScan) ||
|
|
Boolean(topProbability));
|
|
const lockEdge = normalizeSignedProbability(priceAnalysis?.lock?.edge);
|
|
const lockAvailable = Boolean(
|
|
priceAnalysis?.lock?.available && lockEdge != null,
|
|
);
|
|
const quoteSource =
|
|
linkedMarketBucket?.quote_source ||
|
|
marketScan?.yes_token?.quote_source ||
|
|
marketScan?.no_token?.quote_source ||
|
|
null;
|
|
const quoteAgeMs =
|
|
linkedMarketBucket?.quote_age_ms ??
|
|
marketScan?.yes_token?.quote_age_ms ??
|
|
marketScan?.no_token?.quote_age_ms;
|
|
const quoteSourceLabel =
|
|
quoteSource === "polymarket_ws"
|
|
? locale === "en-US"
|
|
? `WS live${quoteAgeMs != null ? ` · ${Math.max(0, Math.round(Number(quoteAgeMs) / 1000))}s` : ""}`
|
|
: `WS 实时${quoteAgeMs != null ? ` · ${Math.max(0, Math.round(Number(quoteAgeMs) / 1000))}秒` : ""}`
|
|
: locale === "en-US"
|
|
? "CLOB fallback"
|
|
: "CLOB 兜底";
|
|
const actionableEdge = normalizeSignedProbability(preferredPriceView?.edge);
|
|
const linkedContractOverpriced =
|
|
Boolean(linkedMarketBucket) &&
|
|
linkedBestSide === "no" &&
|
|
linkedMarketProbability != null &&
|
|
linkedMarketAsk != null &&
|
|
linkedMarketEdge != null &&
|
|
linkedMarketEdge < 0 &&
|
|
linkedNoEdge != null &&
|
|
linkedNoEdge > 0;
|
|
const linkedContractOverpay =
|
|
linkedContractOverpriced &&
|
|
linkedMarketProbability != null &&
|
|
linkedMarketAsk != null
|
|
? Number(linkedMarketAsk) - linkedMarketProbability
|
|
: null;
|
|
const actionText = !marketScan
|
|
? locale === "en-US"
|
|
? "Waiting"
|
|
: "等待"
|
|
: !marketScan.available
|
|
? locale === "en-US"
|
|
? "No market"
|
|
: "无盘口"
|
|
: actionableEdge == null
|
|
? locale === "en-US"
|
|
? "No quote"
|
|
: "无报价"
|
|
: actionableEdge >= 0.02
|
|
? linkedContractOverpriced
|
|
? locale === "en-US"
|
|
? "Overpriced"
|
|
: "市场偏贵"
|
|
: locale === "en-US"
|
|
? `Watch ${preferredSideLabel}`
|
|
: `可关注 ${preferredSideLabel}`
|
|
: actionableEdge > 0
|
|
? linkedContractOverpriced
|
|
? locale === "en-US"
|
|
? "Slightly overpriced"
|
|
: "略偏贵"
|
|
: locale === "en-US"
|
|
? `Small ${preferredSideLabel}`
|
|
: `${preferredSideLabel} 优势较小`
|
|
: locale === "en-US"
|
|
? "No clear edge"
|
|
: "暂无优势";
|
|
const actionNote =
|
|
linkedContractOverpriced && linkedContractOverpay != null
|
|
? locale === "en-US"
|
|
? `YES above model by ${formatSignedPercent(linkedContractOverpay)}`
|
|
: `YES 高于模型 ${formatSignedPercent(linkedContractOverpay)}`
|
|
: actionableEdge != null && actionableEdge >= 0.02
|
|
? locale === "en-US"
|
|
? `${formatSignedPercent(actionableEdge)} vs ask`
|
|
: `相对买价 ${formatSignedPercent(actionableEdge)}`
|
|
: locale === "en-US"
|
|
? `${preferredSideLabel} ${formatSignedPercent(actionableEdge)}`
|
|
: `${preferredSideLabel} ${formatSignedPercent(actionableEdge)}`;
|
|
|
|
return (
|
|
<section className="prob-section">
|
|
{!hideTitle && <h3>{t("section.probability")}</h3>}
|
|
<div className="prob-bars">
|
|
<div className="prob-calibration-head">
|
|
<div>
|
|
<span className="prob-source-chip">{probabilityEngineLabel}</span>
|
|
<strong>
|
|
{displayTopLabel && displayTopProbabilityText
|
|
? locale === "en-US"
|
|
? displayUsesMarketBuckets
|
|
? `${displayTopLabel} is the top displayed contract bucket at ${displayTopProbabilityText}`
|
|
: `${displayTopLabel} is the top single bucket at ${displayTopProbabilityText}`
|
|
: displayUsesMarketBuckets
|
|
? `${displayTopLabel} 为当前显示分布最高，${displayTopProbabilityText}`
|
|
: `${displayTopLabel} 单点最高，${displayTopProbabilityText}`
|
|
: locale === "en-US"
|
|
? "Awaiting calibrated buckets"
|
|
: "等待校准概率桶"}
|
|
</strong>
|
|
</div>
|
|
<p>
|
|
{hasLgbmProbability
|
|
? locale === "en-US"
|
|
? "LGBM is the learned intraday adjustment; raw model points below are only diagnostic."
|
|
: "LGBM 作为日内学习校准项；下方原始模型落点仅用于诊断。"
|
|
: locale === "en-US"
|
|
? "Using the calibrated probability distribution; raw model points below are not probabilities."
|
|
: "使用校准后的概率分布；下方原始模型落点不是概率。"}
|
|
</p>
|
|
</div>
|
|
{marketScan?.available && (topMarketBucketText || marketYesText) && (
|
|
<div
|
|
style={{
|
|
color: "var(--text-secondary)",
|
|
fontSize: "11px",
|
|
marginBottom: "6px",
|
|
}}
|
|
>
|
|
{useMarketTopBuckets
|
|
? locale === "en-US"
|
|
? `Market reference only: top traded bucket ${topMarketBucketText}`
|
|
: `市场仅作参考：最高交易温度桶 ${topMarketBucketText}`
|
|
: locale === "en-US"
|
|
? `Market reference only: this bucket ${marketYesText}`
|
|
: `市场仅作参考：该温度桶 ${marketYesText}`}
|
|
</div>
|
|
)}
|
|
<div className="prob-distribution-panel">
|
|
<div className="prob-distribution-head">
|
|
<span>
|
|
{locale === "en-US"
|
|
? "EMOS probability distribution"
|
|
: "EMOS 概率分布"}
|
|
</span>
|
|
<em>
|
|
{marketContractRows.length > 0
|
|
? locale === "en-US"
|
|
? "market buckets are aggregated from single-degree EMOS buckets"
|
|
: "市场合约桶由单点 EMOS 概率聚合"
|
|
: locale === "en-US"
|
|
? "calibrated temperature buckets"
|
|
: "校准后的温度桶"}
|
|
</em>
|
|
</div>
|
|
{probabilityRows.length === 0 ? (
|
|
<EmptyState text={t("section.noProb")} />
|
|
) : (
|
|
probabilityRows.map((row, index) => {
|
|
const probability = Math.round(
|
|
Number(row.probability || 0) * 100,
|
|
);
|
|
|
|
return (
|
|
<div key={`${row.key || index}`} className="prob-row">
|
|
<div className="prob-label">{row.label}</div>
|
|
<div className="prob-bar-track">
|
|
<div
|
|
className={clsx("prob-bar-fill", `rank-${index}`)}
|
|
style={{ width: `${Math.max(probability, 8)}%` }}
|
|
>
|
|
{probability}%
|
|
</div>
|
|
</div>
|
|
</div>
|
|
);
|
|
})
|
|
)}
|
|
</div>
|
|
{hasPriceAnalysis && (
|
|
<div className="prob-price-card">
|
|
<div className="prob-price-head">
|
|
<span>
|
|
{locale === "en-US" ? "Win-rate reference" : "胜率参考"}
|
|
</span>
|
|
<strong>
|
|
{!marketScan
|
|
? locale === "en-US"
|
|
? "Waiting for market context"
|
|
: "等待市场参照"
|
|
: !marketScan.available
|
|
? locale === "en-US"
|
|
? "No matched active market"
|
|
: "未匹配到活跃盘口"
|
|
: locale === "en-US"
|
|
? `${linkedContractLabel || topProbabilityLabel || "Temperature bucket"} · model ${linkedMarketProbabilityText || topProbabilityText || "--"}`
|
|
: `${linkedContractLabel || topProbabilityLabel || "温度桶"} · 模型 ${linkedMarketProbabilityText || topProbabilityText || "--"}`}
|
|
</strong>
|
|
</div>
|
|
<div className="prob-price-grid">
|
|
<div>
|
|
<span>
|
|
{locale === "en-US" ? "Bucket" : "温度桶"}
|
|
</span>
|
|
<strong>
|
|
{linkedContractLabel || topProbabilityLabel || "--"}
|
|
</strong>
|
|
<em>
|
|
{linkedMarketProbabilityText || topProbabilityText || "--"}
|
|
</em>
|
|
</div>
|
|
<div>
|
|
<span>{locale === "en-US" ? "DEB" : "DEB"}</span>
|
|
<strong>
|
|
{modelView.deb != null && Number.isFinite(Number(modelView.deb))
|
|
? `${Number(modelView.deb).toFixed(1)}${detail.temp_symbol}`
|
|
: "--"}
|
|
</strong>
|
|
<em>{locale === "en-US" ? "final fused forecast" : "最终融合预测"}</em>
|
|
</div>
|
|
<div>
|
|
<span>{locale === "en-US" ? "Model support" : "模型支持"}</span>
|
|
<strong>{modelVoteHint || "--"}</strong>
|
|
<em>{locale === "en-US" ? "raw model agreement" : "原始模型一致性"}</em>
|
|
</div>
|
|
<div>
|
|
<span>{locale === "en-US" ? "Market role" : "盘口角色"}</span>
|
|
<strong>{locale === "en-US" ? "Reference only" : "仅作参考"}</strong>
|
|
<em>{quoteSourceLabel}</em>
|
|
</div>
|
|
</div>
|
|
<p>
|
|
{locale === "en-US"
|
|
? "This card follows the same rule as AI forecast: DEB first, model agreement second, METAR conflict check before settlement."
|
|
: "该卡片与 AI 预测口径一致：先看 DEB，再看模型支持，最后检查 METAR 是否冲突。"}
|
|
</p>
|
|
</div>
|
|
)}
|
|
{modelVoteHint && (
|
|
<div className="prob-model-hint">
|
|
<span>
|
|
{locale === "en-US" ? "Raw model points" : "原始模型落点"}
|
|
</span>
|
|
<strong>{modelVoteHint}</strong>
|
|
<em>
|
|
{locale === "en-US"
|
|
? "diagnostic only; EMOS and contract rows use calibrated probabilities"
|
|
: "仅作诊断；EMOS 与合约行使用校准概率"}
|
|
</em>
|
|
</div>
|
|
)}
|
|
</div>
|
|
</section>
|
|
);
|
|
}
|
|
|
|
export function ModelForecast({
|
|
detail,
|
|
hideTitle = false,
|
|
targetDate,
|
|
}: {
|
|
detail: CityDetail;
|
|
hideTitle?: boolean;
|
|
targetDate?: string | null;
|
|
}) {
|
|
const { locale, t } = useI18n();
|
|
const view = getModelView(detail, targetDate);
|
|
const modelsMap = { ...view.models };
|
|
const modelMetadata =
|
|
detail.source_forecasts?.open_meteo_multi_model?.model_metadata || {};
|
|
|
|
const modelEntries = Object.entries(modelsMap).filter(
|
|
([, value]) =>
|
|
value !== null && value !== undefined && Number.isFinite(Number(value)),
|
|
);
|
|
const hasSingleModelOnly = modelEntries.length === 1;
|
|
|
|
// 如果没有任何数值，给出提示
|
|
if (modelEntries.length === 0) {
|
|
return (
|
|
<section className="models-section">
|
|
{!hideTitle && <h3>{t("section.models")}</h3>}
|
|
<div className="model-bars">
|
|
<EmptyState text={t("section.noModels")} />
|
|
</div>
|
|
</section>
|
|
);
|
|
}
|
|
|
|
const numericValues = modelEntries.map(([, value]) => Number(value));
|
|
const comparisonValues =
|
|
view.deb != null ? [...numericValues, Number(view.deb)] : numericValues;
|
|
const minValue = comparisonValues.length
|
|
? Math.min(...comparisonValues) - 1
|
|
: 0;
|
|
const maxValue = comparisonValues.length
|
|
? Math.max(...comparisonValues) + 1
|
|
: 1;
|
|
const range = Math.max(maxValue - minValue, 1);
|
|
const sortedEntries = modelEntries.sort(
|
|
(a, b) => Number(b[1] || 0) - Number(a[1] || 0),
|
|
);
|
|
const groupedEntries = sortedEntries
|
|
.reduce(
|
|
(acc, [name, value]) => {
|
|
const group = getModelGroupMeta(name, modelMetadata, locale);
|
|
const existing = acc.find((item) => item.key === group.key);
|
|
const entry = {
|
|
metaLine: formatModelMetaLine(name, modelMetadata, locale),
|
|
name,
|
|
value: Number(value),
|
|
};
|
|
if (existing) {
|
|
existing.entries.push(entry);
|
|
} else {
|
|
acc.push({ ...group, entries: [entry] });
|
|
}
|
|
return acc;
|
|
},
|
|
[] as Array<{
|
|
entries: Array<{ metaLine: string; name: string; value: number }>;
|
|
key: string;
|
|
label: string;
|
|
order: number;
|
|
tone: string;
|
|
}>,
|
|
)
|
|
.sort((a, b) => a.order - b.order);
|
|
const spread =
|
|
numericValues.length >= 2
|
|
? Math.max(...numericValues) - Math.min(...numericValues)
|
|
: null;
|
|
const metadataSource =
|
|
detail.source_forecasts?.open_meteo_multi_model?.provider === "open-meteo"
|
|
? "Open-Meteo"
|
|
: null;
|
|
|
|
return (
|
|
<section className="models-section">
|
|
{!hideTitle && <h3>{t("section.models")}</h3>}
|
|
<div className="model-bars">
|
|
<div className="model-stack-summary">
|
|
<span>
|
|
{locale === "en-US" ? "Available models" : "可用模型"} ·{" "}
|
|
<strong>{modelEntries.length}</strong>
|
|
</span>
|
|
<span>
|
|
{locale === "en-US" ? "Spread" : "分歧"} ·{" "}
|
|
<strong>
|
|
{spread != null
|
|
? `${spread.toFixed(1)}${detail.temp_symbol}`
|
|
: "--"}
|
|
</strong>
|
|
</span>
|
|
{metadataSource && (
|
|
<span>
|
|
{locale === "en-US" ? "API" : "接口"} ·{" "}
|
|
<strong>{metadataSource}</strong>
|
|
</span>
|
|
)}
|
|
</div>
|
|
{hasSingleModelOnly && (
|
|
<div
|
|
style={{
|
|
color: "var(--text-secondary)",
|
|
fontSize: "11px",
|
|
marginBottom: "8px",
|
|
}}
|
|
>
|
|
{locale === "en-US"
|
|
? "Single-model fallback: waiting for the rest of the model cluster."
|
|
: "当前处于单模型回退，其他模型结果还没回传。"}
|
|
</div>
|
|
)}
|
|
{groupedEntries.map((group) => (
|
|
<div
|
|
key={group.key}
|
|
className={clsx("model-group", `model-group-${group.tone}`)}
|
|
>
|
|
<div className="model-group-heading">
|
|
<span>{group.label}</span>
|
|
<em>{group.entries.length}</em>
|
|
</div>
|
|
{group.entries.map(({ metaLine, name, value }) => {
|
|
const width = ((value - minValue) / range) * 100;
|
|
const debLine =
|
|
view.deb != null
|
|
? ((Number(view.deb) - minValue) / range) * 100
|
|
: null;
|
|
|
|
return (
|
|
<div key={name} className="model-row model-row-rich">
|
|
<div className="model-name" title={metaLine || name}>
|
|
<strong>{name}</strong>
|
|
{metaLine && <span>{metaLine}</span>}
|
|
</div>
|
|
<div className="model-bar-track">
|
|
<div
|
|
className="model-bar-fill"
|
|
style={{ width: `${width}%` }}
|
|
/>
|
|
<span className="model-bar-value">
|
|
{value}
|
|
{detail.temp_symbol}
|
|
</span>
|
|
{debLine != null && (
|
|
<div
|
|
className="model-deb-line"
|
|
style={{ left: `${debLine}%` }}
|
|
/>
|
|
)}
|
|
</div>
|
|
</div>
|
|
);
|
|
})}
|
|
</div>
|
|
))}
|
|
{view.deb != null && (
|
|
<div
|
|
className="model-row"
|
|
style={{
|
|
borderTop: "1px solid rgba(255,255,255,0.06)",
|
|
marginTop: "6px",
|
|
paddingTop: "6px",
|
|
}}
|
|
>
|
|
<div
|
|
className="model-name"
|
|
style={{ color: "var(--accent-cyan)", fontWeight: 700 }}
|
|
>
|
|
DEB
|
|
</div>
|
|
<div className="model-bar-track">
|
|
<div
|
|
className="model-bar-fill deb"
|
|
style={{
|
|
width: `${((Number(view.deb) - minValue) / range) * 100}%`,
|
|
}}
|
|
/>
|
|
<span className="model-bar-value deb">
|
|
{Number(view.deb)}
|
|
{detail.temp_symbol}
|
|
</span>
|
|
</div>
|
|
</div>
|
|
)}
|
|
</div>
|
|
</section>
|
|
);
|
|
}
|
|
|
|
export function ForecastTable() {
|
|
const store = useDashboardStore();
|
|
const { data } = useCityData();
|
|
const { locale, t } = useI18n();
|
|
const daily = useMemo(() => {
|
|
if (!data) return [];
|
|
const rawDaily = Array.isArray(data.forecast?.daily)
|
|
? data.forecast?.daily || []
|
|
: [];
|
|
const seen = new Set<string>();
|
|
return rawDaily.filter((day) => {
|
|
const date = String(day?.date || "").trim();
|
|
if (!date || seen.has(date)) return false;
|
|
seen.add(date);
|
|
return true;
|
|
});
|
|
}, [data]);
|
|
if (!data) return null;
|
|
const isSparseDaily = daily.length <= 1;
|
|
const isForecastCompleting =
|
|
store.loadingState.cityDetail &&
|
|
(data.detail_depth !== "full" || isSparseDaily);
|
|
const resolveForecastTemp = (
|
|
date: string,
|
|
fallback: number | null | undefined,
|
|
) => {
|
|
const debPrediction = data.multi_model_daily?.[date]?.deb?.prediction;
|
|
return debPrediction ?? fallback ?? null;
|
|
};
|
|
return (
|
|
<section className="forecast-section">
|
|
<h3>{t("forecast.title")}</h3>
|
|
{isSparseDaily && (
|
|
<div className="forecast-inline-note">
|
|
{isForecastCompleting
|
|
? locale === "en-US"
|
|
? "Multi-day forecast is syncing. Only the current-day card has arrived."
|
|
: "多日预报同步中，当前只到达当日卡片。"
|
|
: locale === "en-US"
|
|
? "Only the current-day forecast is available right now."
|
|
: "当前只收到当日预报，其他日期结果暂未回传。"}
|
|
</div>
|
|
)}
|
|
<div className="forecast-table">
|
|
{daily.length === 0 ? (
|
|
<EmptyState text={t("forecast.empty")} />
|
|
) : (
|
|
daily
|
|
.map((day, index) => {
|
|
const isToday = data.local_date
|
|
? day.date === data.local_date
|
|
: index === 0;
|
|
const isSelected =
|
|
(isToday &&
|
|
store.forecastModalMode === "today" &&
|
|
Boolean(store.futureModalDate)) ||
|
|
(store.forecastModalMode !== "today" &&
|
|
store.futureModalDate === day.date) ||
|
|
store.selectedForecastDate === day.date;
|
|
return (
|
|
<button
|
|
key={day.date}
|
|
type="button"
|
|
className={clsx(
|
|
"forecast-day",
|
|
isToday && "today",
|
|
isSelected && "selected",
|
|
)}
|
|
onClick={() => {
|
|
startTransition(() => {
|
|
if (isToday) {
|
|
store.openTodayModal();
|
|
return;
|
|
}
|
|
store.openFutureModal(day.date);
|
|
});
|
|
}}
|
|
>
|
|
<div className="f-date">
|
|
{isToday
|
|
? t("forecast.today")
|
|
: day.date.substring(5).replace("-", "/")}
|
|
</div>
|
|
<div className="f-temp">
|
|
{resolveForecastTemp(day.date, day.max_temp)}
|
|
{data.temp_symbol}
|
|
</div>
|
|
</button>
|
|
);
|
|
})
|
|
.concat(
|
|
isForecastCompleting
|
|
? Array.from({ length: Math.max(0, 5 - daily.length) }).map(
|
|
(_, index) => (
|
|
<button
|
|
key={`forecast-sync-${index}`}
|
|
type="button"
|
|
className="forecast-day forecast-day-sync"
|
|
disabled
|
|
>
|
|
<div className="f-date">
|
|
{locale === "en-US" ? "Syncing" : "同步中"}
|
|
</div>
|
|
<div className="f-temp">--</div>
|
|
</button>
|
|
),
|
|
)
|
|
: [],
|
|
)
|
|
)}
|
|
</div>
|
|
</section>
|
|
);
|
|
}
|
|
|
|
export function RiskInfo() {
|
|
const { data } = useCityData();
|
|
const { t } = useI18n();
|
|
if (!data) return null;
|
|
const risk = data.risk || {};
|
|
|
|
return (
|
|
<section className="risk-section">
|
|
<h3>{t("section.risk")}</h3>
|
|
<div className="risk-info">
|
|
{!risk.airport ? (
|
|
<span style={{ color: "var(--text-muted)" }}>
|
|
{t("section.noRiskProfile")}
|
|
</span>
|
|
) : (
|
|
<>
|
|
<div className="risk-row">
|
|
<span className="risk-label">{t("section.airport")}</span>
|
|
<span>
|
|
{risk.airport} ({risk.icao})
|
|
</span>
|
|
</div>
|
|
<div className="risk-row">
|
|
<span className="risk-label">{t("section.distance")}</span>
|
|
<span>{risk.distance_km}km</span>
|
|
</div>
|
|
{risk.warning && (
|
|
<div className="risk-row">
|
|
<span className="risk-label">{t("section.note")}</span>
|
|
<span>{risk.warning}</span>
|
|
</div>
|
|
)}
|
|
</>
|
|
)}
|
|
</div>
|
|
</section>
|
|
);
|
|
}
|