feat: Implement core PolyWeather application with city weather query service, trend analysis, data collection, and dashboard UI.
This commit is contained in:
2569718930@qq.com
@@ -13,10 +13,6 @@ TELEGRAM_ALERT_CITIES=ankara,london,paris,seoul,toronto,buenos aires,wellington,
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# AI
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GROQ_API_KEY=your_groq_api_key_here
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# Meteoblue
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METEOBLUE_API_KEY=your_meteoblue_api_key_here
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METEOBLUE_CACHE_TTL_SEC=7200
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# Open-Meteo (forecast data changes ~hourly, no need to refresh more often)
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OPEN_METEO_CACHE_TTL_SEC=7200
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OPEN_METEO_ENSEMBLE_CACHE_TTL_SEC=7200
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@@ -76,4 +72,3 @@ POLYMARKET_WALLET_ACTIVITY_NOTIFY_CLOSED=false
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POLYMARKET_WALLET_ACTIVITY_BOOTSTRAP_ALERT=false
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POLYMARKET_WALLET_ACTIVITY_AVG_PRICE_SHOW_MIN=0.01
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POLYMARKET_WALLET_ACTIVITY_AVG_PRICE_SHOW_MAX=0.99
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@@ -1,6 +1,5 @@
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# Weather API Configuration
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weather:
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meteoblue_api_key: null # Set via METEOBLUE_API_KEY env var
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timeout: 30
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# Target Cities
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@@ -211,7 +211,6 @@
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- **Open-Meteo**
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- **weather.gov**（美国城市）
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- **Meteoblue**（部分城市）
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- **多模型集成**: ECMWF / GFS / ICON / GEM / JMA
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---
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@@ -22,7 +22,7 @@ const GUIDE_CARDS = {
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title: "Ankara 专属增强",
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},
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{
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body: "点击多日预报后的模态框，主要用于分析下一个交易日。6-48 小时趋势以 weather.gov 和 Open-Meteo 为主，部分城市补充 Meteoblue；0-2 小时临近判断优先看 METAR 与周边站。",
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body: "点击多日预报后的模态框，主要用于分析下一个交易日。6-48 小时趋势以 weather.gov 和 Open-Meteo 为主；0-2 小时临近判断优先看 METAR 与周边站。",
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title: "未来日期分析",
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},
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{
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@@ -48,7 +48,7 @@ const GUIDE_CARDS = {
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title: "Ankara-specific Enhancement",
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},
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{
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body: "The multi-day modal focuses on next-session analysis. 6-48h trend mainly relies on weather.gov and Open-Meteo, with Meteoblue in selected cities; 0-2h nowcast prioritizes METAR and nearby stations.",
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body: "The multi-day modal focuses on next-session analysis. 6-48h trend mainly relies on weather.gov and Open-Meteo; 0-2h nowcast prioritizes METAR and nearby stations.",
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title: "Future-date Analysis",
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},
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{
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@@ -552,18 +552,8 @@ export function ModelForecast({
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}) {
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const { locale, t } = useI18n();
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const view = getModelView(detail, targetDate);
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const fallbackMeteoblue = detail.source_forecasts?.meteoblue?.today_high;
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const modelsMap = { ...view.models };
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// 强行插入 Meteoblue，防止 helper 函数或日期对齐导致其被剔除
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if (
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modelsMap["Meteoblue"] == null &&
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fallbackMeteoblue != null &&
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(!targetDate || targetDate === detail.local_date)
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) {
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modelsMap["Meteoblue"] = fallbackMeteoblue;
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}
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const modelEntries = Object.entries(modelsMap).filter(
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([, value]) =>
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value !== null && value !== undefined && Number.isFinite(Number(value)),
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@@ -591,12 +581,6 @@ export function ModelForecast({
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? Math.max(...comparisonValues) + 1
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: 1;
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const range = Math.max(maxValue - minValue, 1);
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const getModelDisplayName = (name: string) => {
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if (String(name).trim().toLowerCase() === "meteoblue") {
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return "Meteoblue";
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}
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return name;
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};
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return (
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<section className="models-section">
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@@ -615,7 +599,7 @@ export function ModelForecast({
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return (
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<div key={name} className="model-row">
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<div className="model-name" title={name}>
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{getModelDisplayName(name)}
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{name}
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</div>
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<div className="model-bar-track">
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<div
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@@ -168,12 +168,6 @@ export interface SourceForecasts {
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weather_gov?: {
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forecast_periods?: WeatherGovPeriod[];
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};
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meteoblue?: {
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today_high?: number | null;
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daily_highs?: Array<number | null>;
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unit?: string | null;
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url?: string | null;
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};
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}
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export interface DailyModelForecast {
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@@ -330,67 +330,18 @@ export function getProbabilityView(detail: CityDetail, targetDate?: string | nul
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}
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export function getModelView(detail: CityDetail, targetDate?: string | null) {
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const toFiniteNumber = (value: unknown): number | null => {
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if (value === null || value === undefined || value === "") return null;
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const numeric = Number(value);
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return Number.isFinite(numeric) ? numeric : null;
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};
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const pickMeteoblueForDate = (dateStr: string) => {
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const meteoblue = detail.source_forecasts?.meteoblue;
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if (!meteoblue) return null;
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const dates = detail.forecast?.daily?.map((item) => item.date) || [];
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const index = dates.findIndex((date) => date === dateStr);
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const todayHigh = toFiniteNumber(meteoblue.today_high);
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// Today must always use Meteoblue daily max (today_high) first.
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if (dateStr === detail.local_date && todayHigh != null) {
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return todayHigh;
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}
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const dailyHighs = Array.isArray(meteoblue.daily_highs)
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? meteoblue.daily_highs
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: [];
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if (index >= 0) {
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const byDailyHigh = toFiniteNumber(dailyHighs[index]);
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if (byDailyHigh != null) return byDailyHigh;
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}
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if (index === 0 && todayHigh != null) return todayHigh;
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return null;
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};
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const withMeteoblue = (
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models: Record<string, number | null>,
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dateStr: string,
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) => {
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const nextModels = { ...models };
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const existing = toFiniteNumber(nextModels.Meteoblue);
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if (existing == null) {
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const meteoblueVal = pickMeteoblueForDate(dateStr);
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if (meteoblueVal != null) {
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nextModels.Meteoblue = meteoblueVal;
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}
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} else {
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nextModels.Meteoblue = existing;
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}
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return nextModels;
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};
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const date = targetDate || detail.local_date;
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const daily = detail.multi_model_daily?.[date];
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if (daily) {
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return {
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deb: daily.deb?.prediction ?? null,
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models: withMeteoblue(daily.models || {}, date),
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models: daily.models || {},
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};
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}
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return {
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deb: detail.deb?.prediction ?? null,
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models: withMeteoblue(detail.multi_model || {}, date),
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models: detail.multi_model || {},
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};
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}
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@@ -712,10 +663,6 @@ export function computeFrontTrendSignal(
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? isEnglish(locale)
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? "Temperature declines with pressure rebound and/or northerly shift. Next 6-48h leans cold-front suppression."
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: "温度下滑、气压回升或风向转北，未来 6-48 小时更像冷锋或冷平流压制。"
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: detail.name !== "ankara" && Boolean(detail.source_forecasts?.meteoblue)
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? isEnglish(locale)
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? "Structured trend layer mainly uses weather.gov, Open-Meteo and Meteoblue for 6-48h warm/cold flow judgement."
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: "结构化来源以 weather.gov、Open-Meteo、Meteoblue 为主，用于判断未来 6-48 小时冷暖平流趋势。"
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: isEnglish(locale)
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? "Structured trend layer mainly uses weather.gov and Open-Meteo for 6-48h warm/cold flow judgement."
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: "结构化来源以 weather.gov 和 Open-Meteo 为主，用于判断未来 6-48 小时冷暖平流趋势。",
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@@ -42,7 +42,7 @@ const MESSAGES: Record<Locale, Record<string, string>> = {
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"guide.title": "📎 PolyWeather 系统技术说明",
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"guide.closeAria": "关闭技术说明",
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"guide.footer":
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"数据源以 Aviation Weather / METAR、Turkish MGM、Open-Meteo、weather.gov 为主，部分城市补充 Meteoblue。",
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"数据源以 Aviation Weather / METAR、Turkish MGM、Open-Meteo、weather.gov 为主。",
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"history.title": "📊 历史准确率对账 - {city}",
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"history.closeAria": "关闭历史对账",
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@@ -143,7 +143,7 @@ const MESSAGES: Record<Locale, Record<string, string>> = {
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"guide.title": "📎 PolyWeather Technical Overview",
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"guide.closeAria": "Close technical overview",
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"guide.footer":
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"Primary data sources are Aviation Weather / METAR, Turkish MGM, Open-Meteo, and weather.gov, with Meteoblue added for selected cities.",
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"Primary data sources are Aviation Weather / METAR, Turkish MGM, Open-Meteo, and weather.gov.",
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"history.title": "📊 Historical Reconciliation - {city}",
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"history.closeAria": "Close history reconciliation",
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@@ -186,7 +186,6 @@ export interface ModelComparison {
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JMA?: number;
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MGM?: number;
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NWS?: number;
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Meteoblue?: number;
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}
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export interface DEBAnalysis {
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@@ -1,4 +1,4 @@
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<!DOCTYPE html>
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<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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@@ -151,7 +151,7 @@
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</div>
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<div class="guide-card">
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<h3>📆 未来日期分析</h3>
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<p>点击多日预报后打开的模态框，主要用于分析下一个交易日。 6-48 小时趋势以 weather.gov 和 Open-Meteo 为主，部分城市可会补充 Meteoblue； 0-2 小时临近判断则优先看 METAR 与周边站。</p>
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<p>点击多日预报后打开的模态框，主要用于分析下一个交易日。 6-48 小时趋势以 weather.gov 和 Open-Meteo 为主； 0-2 小时临近判断则优先看 METAR 与周边站。</p>
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</div>
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<div class="guide-card">
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<h3>📊 历史对账规则</h3>
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@@ -159,7 +159,7 @@
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</div>
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</div>
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<div class="guide-footer">
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<p>※ 数据源以 Aviation Weather / METAR、Turkish MGM、Open-Meteo、weather.gov 为主，部分城市补充 Meteoblue。</p>
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<p>※ 数据源以 Aviation Weather / METAR、Turkish MGM、Open-Meteo、weather.gov 为主。</p>
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</div>
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</div>
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</div>
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@@ -236,6 +236,3 @@
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</html>
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@@ -175,7 +175,6 @@ def _append_future_forecast_lines(
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mgm_daily[date_key] = day_high
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mm_raw = weather_data.get("multi_model") or {}
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mm_daily = mm_raw.get("daily_forecasts", {}) if isinstance(mm_raw, dict) else {}
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mb_daily = (weather_data.get("meteoblue") or {}).get("daily_highs", []) or []
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nws_periods = (weather_data.get("nws") or {}).get("forecast_periods", []) or []
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if len(dates) > 1:
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@@ -231,18 +230,6 @@ def _append_future_forecast_lines(
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lines.append("📅 " + " | ".join(future))
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return
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if isinstance(mb_daily, list) and len(mb_daily) > 1:
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future = []
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for idx in range(1, min(3, len(mb_daily))):
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day_temp = _sf(mb_daily[idx])
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if day_temp is None:
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continue
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day = (local_now + timedelta(days=idx)).strftime("%m-%d")
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future.append(f"{day}: MB {day_temp:.1f}{temp_symbol}")
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if future:
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lines.append("📅 " + " | ".join(future))
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return
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if isinstance(nws_periods, list) and nws_periods:
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future = []
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seen_days = set()
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@@ -358,14 +345,13 @@ def build_city_query_report(
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nws_high = _sf((weather_data.get("nws") or {}).get("today_high"))
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mgm_high = _sf((mgm.get("today_high") if isinstance(mgm, dict) else None))
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mb_high = _sf((weather_data.get("meteoblue") or {}).get("today_high"))
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metar_max_so_far = _sf((metar.get("current") or {}).get("max_temp_so_far"))
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today_t = _sf(max_temps[0]) if max_temps else None
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fallback_source = None
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metar_only_fallback = False
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if today_t is None:
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for source_name, candidate in (("MB", mb_high), ("NWS", nws_high), ("MGM", mgm_high)):
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for source_name, candidate in (("NWS", nws_high), ("MGM", mgm_high)):
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if candidate is not None:
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today_t = candidate
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fallback_source = source_name
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@@ -378,11 +364,6 @@ def build_city_query_report(
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sources = ["Open-Meteo"] if max_temps else []
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comp_parts: List[str] = []
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if mb_high is not None:
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if "MB" not in sources:
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sources.append("MB")
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if fallback_source != "MB":
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comp_parts.append(f"MB: {mb_high:.1f}{temp_symbol}")
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if nws_high is not None:
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if "NWS" not in sources:
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sources.append("NWS")
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@@ -402,18 +383,10 @@ def build_city_query_report(
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if not sources:
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sources = ["N/A"]
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divergence_warning = ""
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base_for_divergence = _sf(max_temps[0]) if max_temps else today_t
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if mb_high is not None and base_for_divergence is not None:
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diff = abs(mb_high - base_for_divergence)
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threshold = 5.0 if city_is_fahrenheit else 2.5
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if diff > threshold:
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divergence_warning = f" ⚠️ <b>模型显著分歧 ({diff:.1f}{temp_symbol})</b>"
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comp_str = f" ({' | '.join(comp_parts)})" if comp_parts else ""
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msg_lines.append(f"\n📊 <b>预报 ({' | '.join(sources)})</b>")
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msg_lines.append(
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f"👉 <b>今天: {today_t_display}{temp_symbol}{comp_str}</b>{divergence_warning}"
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f"👉 <b>今天: {today_t_display}{temp_symbol}{comp_str}</b>"
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)
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_append_future_forecast_lines(
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@@ -62,7 +62,6 @@ def analyze_weather_trend(
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metar = weather_data.get("metar", {})
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open_meteo = weather_data.get("open-meteo", {})
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mgm = weather_data.get("mgm") or {}
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mb = weather_data.get("meteoblue", {})
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nws = weather_data.get("nws", {})
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empty_result = ("", "", {})
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@@ -81,8 +80,6 @@ def analyze_weather_trend(
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current_forecasts: Dict[str, Optional[float]] = {}
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if daily.get("temperature_2m_max"):
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current_forecasts["Open-Meteo"] = _sf(daily.get("temperature_2m_max")[0])
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if mb.get("today_high") is not None:
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current_forecasts["Meteoblue"] = _sf(mb.get("today_high"))
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if nws.get("today_high") is not None:
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current_forecasts["NWS"] = _sf(nws.get("today_high"))
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@@ -1263,6 +1263,7 @@ class PolymarketReadOnlyLayer:
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ranked: List[
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Tuple[
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float,
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float,
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float,
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Dict[str, Any],
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@@ -1315,6 +1316,7 @@ class PolymarketReadOnlyLayer:
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(
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market_prob,
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volume,
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bucket_temp,
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market,
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yes_token,
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no_token,
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@@ -1331,7 +1333,16 @@ class PolymarketReadOnlyLayer:
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max_items = max(1, int(limit or 4))
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primary_slug = str(primary_market.get("slug") or "").strip().lower()
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for market_prob, _volume, market, yes_token, no_token, yes_prices, no_prices in ranked[
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for (
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market_prob,
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_volume,
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bucket_temp,
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market,
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yes_token,
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no_token,
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yes_prices,
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no_prices,
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) in ranked[
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:max_items
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]:
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yes_buy = _extract_price(yes_prices.get("buy"))
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@@ -42,25 +42,10 @@ class WeatherDataCollector:
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"munich": ["EDDM", "EDMO", "EDJA"],
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}
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# Meteoblue 仅在增益最大的城市启用（减少配额消耗与冗余请求）
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METEOBLUE_PRIORITY_CITIES = {
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"ankara",
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"london",
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"paris",
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"seoul",
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"toronto",
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"buenos aires",
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"wellington",
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"lucknow",
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"sao paulo",
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"munich",
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}
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def __init__(self, config: dict):
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self.config = config
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weather_cfg = config.get("weather", {})
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self.wunderground_key = weather_cfg.get("wunderground_api_key")
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self.meteoblue_key = weather_cfg.get("meteoblue_api_key")
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|
||||
self.timeout = 30 # 增加超时以支持高延迟 VPS
|
||||
self.session = requests.Session()
|
||||
@@ -91,11 +76,6 @@ class WeatherDataCollector:
|
||||
)
|
||||
self._open_meteo_last_call_ts: float = 0.0
|
||||
self._open_meteo_call_lock = threading.Lock()
|
||||
self.meteoblue_cache_ttl_sec = int(
|
||||
os.getenv("METEOBLUE_CACHE_TTL_SEC", "7200")
|
||||
)
|
||||
self._meteoblue_cache: Dict[str, Dict] = {}
|
||||
self._meteoblue_cache_lock = threading.Lock()
|
||||
self.metar_cache_ttl_sec = int(
|
||||
os.getenv("METAR_CACHE_TTL_SEC", "600") # 默认 10 分钟
|
||||
)
|
||||
@@ -1558,105 +1538,6 @@ class WeatherDataCollector:
|
||||
return fallback
|
||||
return None
|
||||
|
||||
def fetch_from_meteoblue(
|
||||
self,
|
||||
lat: float,
|
||||
lon: float,
|
||||
timezone_name: str = "UTC",
|
||||
use_fahrenheit: bool = False,
|
||||
) -> Optional[Dict]:
|
||||
"""
|
||||
通过 Meteoblue 官方 API 获取高精度预测数据
|
||||
带本地缓存，避免频繁请求触发 429。
|
||||
"""
|
||||
if not self.meteoblue_key:
|
||||
logger.warning("Meteoblue API Key 未配置，跳过抓取。")
|
||||
return None
|
||||
|
||||
cache_key = f"{round(float(lat), 4)}:{round(float(lon), 4)}:{'f' if use_fahrenheit else 'c'}"
|
||||
now_ts = time.time()
|
||||
with self._meteoblue_cache_lock:
|
||||
cached = self._meteoblue_cache.get(cache_key)
|
||||
if (
|
||||
cached
|
||||
and now_ts - float(cached.get("t", 0)) < self.meteoblue_cache_ttl_sec
|
||||
):
|
||||
cached_data = cached.get("data")
|
||||
if isinstance(cached_data, dict):
|
||||
return dict(cached_data)
|
||||
|
||||
try:
|
||||
# 1. 调用官方 API (使用 basic-day 包，它是多模型 ML 融合结果)
|
||||
# 格式: https://my.meteoblue.com/packages/basic-day?apikey=KEY&lat=LAT&lon=LON&format=json
|
||||
url = "https://my.meteoblue.com/packages/basic-day"
|
||||
params = {
|
||||
"apikey": self.meteoblue_key,
|
||||
"lat": lat,
|
||||
"lon": lon,
|
||||
"format": "json",
|
||||
"as_daylight": "true",
|
||||
}
|
||||
|
||||
response = self.session.get(url, params=params, timeout=self.timeout)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
day_data = data.get("data_day", {})
|
||||
max_temps = day_data.get("temperature_max", [])
|
||||
|
||||
if not max_temps:
|
||||
logger.warning(
|
||||
f"Meteoblue API 返回数据中找不到最高温 (坐标: {lat},{lon})"
|
||||
)
|
||||
return None
|
||||
|
||||
# 2. 转换单位
|
||||
def c_to_f(c):
|
||||
return round((c * 9 / 5) + 32, 1)
|
||||
|
||||
result = {
|
||||
"source": "meteoblue",
|
||||
"today_high": None,
|
||||
"daily_highs": [],
|
||||
"unit": "fahrenheit" if use_fahrenheit else "celsius",
|
||||
"url": f"https://www.meteoblue.com/en/weather/week/{lat}N{lon}E", # 仅供参考
|
||||
}
|
||||
|
||||
# 提取今日最高
|
||||
mb_today_c = max_temps[0]
|
||||
result["today_high"] = c_to_f(mb_today_c) if use_fahrenheit else mb_today_c
|
||||
|
||||
# 提取接下来几天的最高温
|
||||
if use_fahrenheit:
|
||||
result["daily_highs"] = [c_to_f(t) for t in max_temps]
|
||||
else:
|
||||
result["daily_highs"] = max_temps
|
||||
|
||||
with self._meteoblue_cache_lock:
|
||||
self._meteoblue_cache[cache_key] = {
|
||||
"t": now_ts,
|
||||
"data": dict(result),
|
||||
}
|
||||
|
||||
logger.info(
|
||||
f"✅ Meteoblue API 获取成功 ({lat},{lon}): 今天 {result['today_high']}{result['unit']}"
|
||||
)
|
||||
return result
|
||||
except Exception as e:
|
||||
status_code = getattr(getattr(e, "response", None), "status_code", None)
|
||||
if status_code == 429:
|
||||
logger.warning("Meteoblue API 限流(429)，尝试使用本地缓存回退。")
|
||||
else:
|
||||
logger.error(f"Meteoblue API fetch failed: {e}")
|
||||
|
||||
with self._meteoblue_cache_lock:
|
||||
stale = self._meteoblue_cache.get(cache_key)
|
||||
if stale and isinstance(stale.get("data"), dict):
|
||||
fallback = dict(stale["data"])
|
||||
fallback["stale_cache"] = True
|
||||
return fallback
|
||||
return None
|
||||
|
||||
def extract_date_from_title(self, title: str) -> Optional[str]:
|
||||
"""
|
||||
从标题中提取日期并标准化为 YYYY-MM-DD
|
||||
@@ -1836,7 +1717,6 @@ class WeatherDataCollector:
|
||||
unit = "f" if use_fahrenheit else "c"
|
||||
open_meteo_key = f"{base}:14:{unit}"
|
||||
ensemble_key = f"{base}:{unit}"
|
||||
meteoblue_key = ensemble_key
|
||||
multi_model_key = ensemble_key
|
||||
|
||||
with self._open_meteo_cache_lock:
|
||||
@@ -1845,8 +1725,6 @@ class WeatherDataCollector:
|
||||
self._ensemble_cache.pop(ensemble_key, None)
|
||||
with self._multi_model_cache_lock:
|
||||
self._multi_model_cache.pop(multi_model_key, None)
|
||||
with self._meteoblue_cache_lock:
|
||||
self._meteoblue_cache.pop(meteoblue_key, None)
|
||||
|
||||
icao = self.get_icao_code(city)
|
||||
if icao:
|
||||
@@ -1962,16 +1840,6 @@ class WeatherDataCollector:
|
||||
# 获取时区偏移以过滤 METAR
|
||||
utc_offset = open_meteo.get("utc_offset", 0)
|
||||
|
||||
if city_lower in self.METEOBLUE_PRIORITY_CITIES:
|
||||
mb_data = self.fetch_from_meteoblue(
|
||||
lat,
|
||||
lon,
|
||||
timezone_name=open_meteo.get("timezone", "UTC"),
|
||||
use_fahrenheit=use_fahrenheit,
|
||||
)
|
||||
if mb_data:
|
||||
results["meteoblue"] = mb_data
|
||||
|
||||
# 对美国城市，额外获取 NWS 高精预报
|
||||
if use_fahrenheit:
|
||||
nws_data = self.fetch_nws(lat, lon)
|
||||
@@ -2023,15 +1891,6 @@ class WeatherDataCollector:
|
||||
if cluster_data:
|
||||
results["mgm_nearby"] = cluster_data
|
||||
|
||||
if city_lower in self.METEOBLUE_PRIORITY_CITIES:
|
||||
mb_data = self.fetch_from_meteoblue(
|
||||
lat,
|
||||
lon,
|
||||
timezone_name="UTC",
|
||||
use_fahrenheit=use_fahrenheit,
|
||||
)
|
||||
if mb_data:
|
||||
results["meteoblue"] = mb_data
|
||||
if use_fahrenheit:
|
||||
nws_data = self.fetch_nws(lat, lon)
|
||||
if nws_data:
|
||||
|
||||
@@ -19,7 +19,6 @@ def load_config():
|
||||
"openweather_api_key": get_env_or_none("OPENWEATHER_API_KEY"),
|
||||
"wunderground_api_key": get_env_or_none("WUNDERGROUND_API_KEY"),
|
||||
"visualcrossing_api_key": get_env_or_none("VISUALCROSSING_API_KEY"),
|
||||
"meteoblue_api_key": get_env_or_none("METEOBLUE_API_KEY"),
|
||||
"proxy": os.getenv("HTTPS_PROXY") or os.getenv("HTTP_PROXY"),
|
||||
},
|
||||
"telegram": {
|
||||
|
||||
@@ -62,7 +62,6 @@ def _make_weather_data(
|
||||
"p90": ens_p90,
|
||||
},
|
||||
"multi_model": {"forecasts": multi_model or {}},
|
||||
"meteoblue": {},
|
||||
"nws": {},
|
||||
}
|
||||
return data
|
||||
|
||||
@@ -252,13 +252,10 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
|
||||
forecast_daily = [{"date": d, "max_temp": t} for d, t in zip(dates, maxtemps)]
|
||||
if om_today is None:
|
||||
nws_high = _sf(raw.get("nws", {}).get("today_high"))
|
||||
mb_high = _sf(raw.get("meteoblue", {}).get("today_high"))
|
||||
mgm_high = _sf(mgm.get("today_high")) if mgm else None
|
||||
fallback_high = (
|
||||
nws_high
|
||||
if nws_high is not None
|
||||
else mb_high
|
||||
if mb_high is not None
|
||||
else mgm_high
|
||||
if mgm_high is not None
|
||||
else max_so_far
|
||||
@@ -291,9 +288,6 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
|
||||
nws_high = _sf(raw.get("nws", {}).get("today_high"))
|
||||
if nws_high is not None:
|
||||
current_forecasts["NWS"] = nws_high
|
||||
mb_high = _sf(raw.get("meteoblue", {}).get("today_high"))
|
||||
if mb_high is not None:
|
||||
current_forecasts["Meteoblue"] = mb_high
|
||||
mgm_high = _sf(mgm.get("today_high")) if mgm else None
|
||||
if mgm_high is not None:
|
||||
current_forecasts["MGM"] = mgm_high
|
||||
@@ -680,7 +674,6 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
|
||||
},
|
||||
"source_forecasts": {
|
||||
"weather_gov": raw.get("nws") or {},
|
||||
"meteoblue": raw.get("meteoblue") or {},
|
||||
},
|
||||
"multi_model": {k: v for k, v in current_forecasts.items() if v is not None},
|
||||
"multi_model_daily": multi_model_daily,
|
||||
|
||||
Reference in New Issue
Block a user