2569718930@qq.com
1354 lines
44 KiB
TypeScript
1354 lines
44 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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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 || "").toUpperCase().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 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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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: 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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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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return {
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rows,
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total,
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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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function normalizeSignedProbability(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 (Math.abs(numeric) > 1) return numeric / 100;
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return numeric;
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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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function getMarketTopBuckets(scan?: MarketScan | null) {
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const buckets = Array.isArray(scan?.top_buckets) ? scan.top_buckets : [];
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if (!buckets.length) return [];
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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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function getMarketAllBuckets(scan?: MarketScan | null) {
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const buckets = Array.isArray(scan?.all_buckets)
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? 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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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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function getMarketTopBucketKey(bucket: MarketTopBucket) {
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if (bucket?.value != null) {
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const valueNum = Number(bucket.value);
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if (Number.isFinite(valueNum)) return `v:${valueNum.toFixed(2)}`;
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}
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if (bucket?.temp != null) {
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const tempNum = Number(bucket.temp);
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if (Number.isFinite(tempNum)) return `t:${tempNum.toFixed(2)}`;
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}
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const parsed = parseTempFromText(bucket?.label);
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if (parsed != null) return `l:${parsed.toFixed(2)}`;
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return `s:${String(bucket?.slug || bucket?.question || bucket?.label || "")}`;
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}
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function hasLgbmModel(detail: CityDetail, targetDate?: string | null) {
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const view = getModelView(detail, targetDate);
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return Object.keys(view.models || {}).some((name) =>
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normalizeModelNameForVote(name).includes("lgbm"),
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);
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}
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function formatProbabilityEngineLabel(
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detail: CityDetail,
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targetDate: string | null | undefined,
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locale: string,
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) {
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const view = getProbabilityView(detail, targetDate);
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if (hasLgbmModel(detail, targetDate)) {
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return locale === "en-US"
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? "LGBM-calibrated probability"
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: "LGBM 校准概率";
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}
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const engine = String(view.engine || "").trim().toLowerCase();
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const calibrationMode = String(view.calibrationMode || "")
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.trim()
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.toLowerCase();
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if (engine === "emos" || calibrationMode.includes("emos")) {
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return locale === "en-US" ? "EMOS-calibrated probability" : "EMOS 校准概率";
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}
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return locale === "en-US" ? "Model probability" : "模型概率";
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}
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export function HeroSummary() {
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const { data } = useCityData();
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const { locale } = useI18n();
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if (!data) return null;
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const { weatherIcon, weatherText } = getWeatherSummary(data, locale);
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const metaItems = getHeroMetaItems(data, locale);
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const current = data.current || {};
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const settlementSourceCode = String(current.settlement_source || "metar")
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.trim()
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.toLowerCase();
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const settlementIcao = String(
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current.station_code || data.risk?.icao || "",
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)
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.trim()
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.toUpperCase();
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const settlementSource =
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settlementSourceCode === "wunderground"
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? settlementIcao
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? `${settlementIcao} METAR`
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: "METAR"
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: String(current.settlement_source_label || current.settlement_source || "METAR")
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.trim()
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.toUpperCase();
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const isMax =
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current.max_so_far != null &&
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current.temp != null &&
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current.max_so_far <= current.temp;
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const currentObsText =
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current.temp != null
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? `${current.temp}${data.temp_symbol} @${current.obs_time || "--"}`
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: data.metar_status?.stale_for_today
|
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? locale === "en-US"
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? "No same-day METAR"
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: "今日暂无 METAR"
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: "--";
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return (
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<section className="hero-section">
|
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<div className="hero-weather">
|
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<span>
|
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{weatherIcon} {weatherText}
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</span>
|
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</div>
|
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<div className="hero-temp">
|
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<span className="hero-value">
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{current.temp != null ? current.temp.toFixed(1) : "--"}
|
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</span>
|
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<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 ${current.max_temp_time}`
|
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: ""}
|
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</div>
|
||
<div className="hero-details">
|
||
<div className="hero-item">
|
||
<span className="label">
|
||
{locale === "en-US" ? "Current Obs" : "当前实测"}
|
||
</span>
|
||
<span className="value">
|
||
{currentObsText}
|
||
</span>
|
||
</div>
|
||
<div className="hero-item">
|
||
<span className="label">
|
||
{locale === "en-US"
|
||
? `${settlementSource} Anchor`
|
||
: `${settlementSource} 锚点`}
|
||
</span>
|
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<span className="value highlight">
|
||
{current.wu_settlement != null
|
||
? `${current.wu_settlement}${data.temp_symbol}`
|
||
: "--"}
|
||
</span>
|
||
</div>
|
||
<div className="hero-item">
|
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<span className="label">
|
||
{locale === "en-US" ? "DEB Forecast" : "DEB 预测"}
|
||
</span>
|
||
<span className="value">
|
||
{data.deb?.prediction != null
|
||
? `${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(52, 211, 153, 0.05)",
|
||
borderColor: "rgba(52, 211, 153, 0.6)",
|
||
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(52, 211, 153, 0.35)",
|
||
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: "#22d3ee",
|
||
borderColor: "#22d3ee",
|
||
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(99, 102, 241, 0.2)",
|
||
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(52, 211, 153, 0.3)",
|
||
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: "#64748b",
|
||
maxRotation: 0,
|
||
},
|
||
},
|
||
y: {
|
||
grid: { color: "rgba(255,255,255,0.04)" },
|
||
max: chartData.max,
|
||
min: chartData.min,
|
||
ticks: {
|
||
callback: (value) => `${value}${data.temp_symbol || "°C"}`,
|
||
color: "#64748b",
|
||
},
|
||
},
|
||
},
|
||
},
|
||
type: "line",
|
||
} satisfies ChartConfiguration<"line">;
|
||
}, [data, chartData, locale]);
|
||
|
||
return (
|
||
<section className="chart-section">
|
||
<h3>{t("section.todayTempTrend")}</h3>
|
||
<div className="chart-wrapper">
|
||
<canvas ref={canvasRef} />
|
||
</div>
|
||
<div className="chart-legend">
|
||
{chartData?.legendText || t("section.chartEmpty")}
|
||
</div>
|
||
</section>
|
||
);
|
||
}
|
||
|
||
export function ProbabilityDistribution({
|
||
detail,
|
||
hideTitle = false,
|
||
targetDate,
|
||
marketScan,
|
||
}: {
|
||
detail: CityDetail;
|
||
hideTitle?: boolean;
|
||
targetDate?: string | null;
|
||
marketScan?: MarketScan | null;
|
||
}) {
|
||
const { locale, t } = useI18n();
|
||
const view = getProbabilityView(detail, targetDate);
|
||
const marketYesPrice = getMarketYesPrice(marketScan);
|
||
const marketYesText = toPercent(marketYesPrice);
|
||
const isToday = !targetDate || targetDate === detail.local_date;
|
||
const probabilityEngineLabel = formatProbabilityEngineLabel(
|
||
detail,
|
||
targetDate,
|
||
locale,
|
||
);
|
||
const hasLgbmProbability = hasLgbmModel(detail, targetDate);
|
||
const modelVoteView = useMemo(
|
||
() => getRoundedModelVoteDistribution(detail, targetDate),
|
||
[detail, targetDate],
|
||
);
|
||
const modelVoteHint = modelVoteView.rows
|
||
.slice(0, 2)
|
||
.map(
|
||
(row) =>
|
||
`${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
|
||
? topProbability.label || `${topProbability.value}${detail.temp_symbol}`
|
||
: null;
|
||
const topProbabilityTemp = topProbability ? getBucketTemp(topProbability) : null;
|
||
const linkedMarketBucket = useMemo(() => {
|
||
if (topProbabilityTemp == null) return null;
|
||
return findMarketBucketForDisplayTemp(
|
||
marketAllBuckets,
|
||
topProbabilityTemp,
|
||
detail,
|
||
);
|
||
}, [detail, marketAllBuckets, 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 linkedMarketProbability =
|
||
topProbability?.probability != null ? Number(topProbability.probability) : null;
|
||
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 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
|
||
? locale === "en-US"
|
||
? `Watch ${preferredSideLabel}`
|
||
: `可关注 ${preferredSideLabel}`
|
||
: actionableEdge > 0
|
||
? locale === "en-US"
|
||
? `Small ${preferredSideLabel}`
|
||
: `${preferredSideLabel} 优势较小`
|
||
: locale === "en-US"
|
||
? "No clear edge"
|
||
: "暂无优势";
|
||
const actionNote =
|
||
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} ${formatSignedPercent(actionableEdge)}`;
|
||
|
||
return (
|
||
<section className="prob-section">
|
||
{!hideTitle && <h3>{t("section.probability")}</h3>}
|
||
<div className="prob-bars">
|
||
<div className="prob-calibration-head">
|
||
<div>
|
||
<span className="prob-source-chip">{probabilityEngineLabel}</span>
|
||
<strong>
|
||
{topProbability && topProbabilityText
|
||
? locale === "en-US"
|
||
? `${topProbabilityLabel} leads at ${topProbabilityText}`
|
||
: `${topProbabilityLabel} 当前最高，${topProbabilityText}`
|
||
: 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; model consensus below remains an explanation layer."
|
||
: "LGBM 作为日内学习校准项；下方模型共识只保留为解释层。"
|
||
: locale === "en-US"
|
||
? "Using the calibrated model distribution; model consensus below is for explanation only."
|
||
: "使用校准后的模型分布；下方模型共识仅用于解释。"}
|
||
</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 ${marketYesText}`}
|
||
</div>
|
||
)}
|
||
{hasPriceAnalysis && (
|
||
<div className="prob-price-card">
|
||
<div className="prob-price-head">
|
||
<span>
|
||
{locale === "en-US" ? "Probability x Price" : "概率 x 价格联动"}
|
||
</span>
|
||
<strong>
|
||
{!marketScan
|
||
? locale === "en-US"
|
||
? "Waiting for market layer"
|
||
: "等待市场层"
|
||
: !marketScan.available
|
||
? locale === "en-US"
|
||
? "No matched active market"
|
||
: "未匹配到活跃盘口"
|
||
: linkedMarketBucket
|
||
? locale === "en-US"
|
||
? `${actionText}: ${topProbabilityLabel || "top bucket"}`
|
||
: `${actionText}：${topProbabilityLabel || "最高概率桶"}`
|
||
: preferredPriceView?.edge != null
|
||
? locale === "en-US"
|
||
? `${actionText} · edge ${formatSignedPercent(preferredPriceView.edge)}`
|
||
: `${actionText} · 优势 ${formatSignedPercent(preferredPriceView.edge)}`
|
||
: locale === "en-US"
|
||
? "Waiting for executable quote"
|
||
: "等待可执行报价"}
|
||
</strong>
|
||
</div>
|
||
<div className="prob-price-grid">
|
||
<div>
|
||
<span>{locale === "en-US" ? "Model bucket" : "概率主桶"}</span>
|
||
<strong>{topProbabilityLabel || "--"}</strong>
|
||
<em>{topProbabilityText || "--"}</em>
|
||
</div>
|
||
<div>
|
||
<span>{locale === "en-US" ? "Candidate" : "可关注"}</span>
|
||
<strong>{actionText}</strong>
|
||
<em>{actionNote}</em>
|
||
</div>
|
||
<div>
|
||
<span>{locale === "en-US" ? "YES price" : "YES 价格"}</span>
|
||
<strong>{toPriceCents(yesDisplayPrice) || "--"}</strong>
|
||
<em>
|
||
{locale === "en-US"
|
||
? `edge ${formatSignedPercent(yesDisplayEdge)}`
|
||
: `优势 ${formatSignedPercent(yesDisplayEdge)}`}
|
||
</em>
|
||
</div>
|
||
<div>
|
||
<span>{locale === "en-US" ? "NO price" : "NO 价格"}</span>
|
||
<strong>{toPriceCents(noDisplayPrice) || "--"}</strong>
|
||
<em>
|
||
{locale === "en-US"
|
||
? `edge ${formatSignedPercent(noDisplayEdge)}`
|
||
: `优势 ${formatSignedPercent(noDisplayEdge)}`}
|
||
</em>
|
||
</div>
|
||
</div>
|
||
<p>
|
||
{locale === "en-US"
|
||
? `Read-only comparison between model probability and executable ask; it does not place orders. Source: ${quoteSourceLabel}${lockAvailable ? ` · lock ${formatSignedPercent(lockEdge)}` : ""}.`
|
||
: `只比较模型概率与可执行买价；系统不会下单。来源：${quoteSourceLabel}${lockAvailable ? ` · 锁价 ${formatSignedPercent(lockEdge)}` : ""}。`}
|
||
</p>
|
||
</div>
|
||
)}
|
||
{modelVoteHint && (
|
||
<div className="prob-model-hint">
|
||
<span>
|
||
{locale === "en-US" ? "Model consensus" : "模型共识参考"}
|
||
</span>
|
||
<strong>{modelVoteHint}</strong>
|
||
<em>
|
||
{locale === "en-US"
|
||
? "explains clustering, not calibrated probability"
|
||
: "解释模型聚集，不等同于校准概率"}
|
||
</em>
|
||
</div>
|
||
)}
|
||
{view.probabilities.length === 0 ? (
|
||
<EmptyState text={t("section.noProb")} />
|
||
) : (
|
||
view.probabilities.slice(0, 6).map((bucket, index) => {
|
||
const probability = Math.round(
|
||
Number(bucket.probability || 0) * 100,
|
||
);
|
||
const bucketTemp = getBucketTemp(bucket);
|
||
const rowMarketBucket = findMarketBucketForDisplayTemp(
|
||
marketAllBuckets,
|
||
bucketTemp,
|
||
detail,
|
||
);
|
||
const rowMarketPrice =
|
||
rowMarketBucket?.market_price ?? rowMarketBucket?.yes_buy ?? null;
|
||
const yesPriceText = toPriceCents(rowMarketPrice);
|
||
const marketTagFinal = rowMarketBucket
|
||
? locale === "en-US"
|
||
? `Market ref: ${yesPriceText || "--"}`
|
||
: `市场参考: ${yesPriceText || "--"}`
|
||
: null;
|
||
let bucketLabel =
|
||
bucket.label || `${bucket.value}${detail.temp_symbol}`;
|
||
if (bucketLabel) {
|
||
let str = String(bucketLabel).toUpperCase().replace(/\s+/g, "");
|
||
const symbol = detail.temp_symbol || "°C";
|
||
str = str.replace(/°?C($|\+|-)/g, "°C$1");
|
||
str = str.replace(/°?F($|\+|-)/g, "°F$1");
|
||
if (!/[°℃][CF]/.test(str) && /[0-9]/.test(str)) {
|
||
str += symbol;
|
||
}
|
||
bucketLabel = str;
|
||
}
|
||
|
||
return (
|
||
<div
|
||
key={`${bucket.label || bucket.value || index}`}
|
||
className="prob-row"
|
||
>
|
||
<div className="prob-label">{bucketLabel}</div>
|
||
<div className="prob-bar-track">
|
||
<div
|
||
className={clsx("prob-bar-fill", `rank-${index}`)}
|
||
style={{ width: `${Math.max(probability, 8)}%` }}
|
||
>
|
||
{probability}%
|
||
</div>
|
||
</div>
|
||
{marketTagFinal && (
|
||
<div
|
||
className={clsx(
|
||
"prob-market-inline",
|
||
rowMarketBucket ? "yes" : "no",
|
||
)}
|
||
>
|
||
{marketTagFinal}
|
||
</div>
|
||
)}
|
||
</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();
|
||
if (!data) return null;
|
||
|
||
const daily = data.forecast?.daily || [];
|
||
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 = 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>
|
||
);
|
||
}
|