feat: introduce PolyWeather application with an interactive map, detailed city weather, trend analysis, and a supporting API.

src/analysis/trend_engine.py

@@ -329,23 +329,15 @@ def analyze_weather_trend(
                 f"实测最高 {max_so_far}{temp_symbol}，偏差 {forecast_miss_deg}°。当前趋势: {_trend_dir}。"
             )
 
-        # Gaussian CDF
+        # Probability Engine
-        def _norm_cdf(x, m, s):
+        probs_result = calculate_prob_distribution(
-            return 0.5 * (1 + math.erf((x - m) / (s * math.sqrt(2))))
+            mu, sigma, max_so_far, temp_symbol
-
+        )
-        min_possible_wu = round(max_so_far) if max_so_far is not None else -999
+        mu = probs_result.get("mu", mu)
-        probs = {}
+        probabilities = probs_result.get("probabilities", [])
-        for n in range(round(mu) - 2, round(mu) + 3):
+        sorted_probs = probs_result.get("sorted_probs", [])
-            if n < min_possible_wu:
+        
-                continue
+        if sorted_probs:
-            p = _norm_cdf(n + 0.5, mu, sigma) - _norm_cdf(n - 0.5, mu, sigma)
-            if p > 0.01:
-                probs[n] = p
-
-        total_p = sum(probs.values())
-        if total_p > 0:
-            probs = {k: v / total_p for k, v in probs.items()}
-            sorted_probs = sorted(probs.items(), key=lambda x: x[1], reverse=True)
             prob_parts = [
                 f"{int(t)}{temp_symbol} [{t - 0.5}~{t + 0.5}) {p * 100:.0f}%"
                 for t, p in sorted_probs[:4]
@@ -354,10 +346,6 @@ def analyze_weather_trend(
                 prob_str = " | ".join(prob_parts)
                 insights.append(f"🎲 <b>结算概率</b> (μ={mu:.1f})：{prob_str}")
                 ai_features.append(f"🎲 数学概率分布：{prob_str}")
-            for t, p in sorted_probs[:4]:
-                probabilities.append(
-                    {"value": int(t), "range": f"[{t-0.5}~{t+0.5})", "probability": round(p, 3)}
-                )
 
     elif is_dead_market:
         settled_wu = round(max_so_far) if max_so_far is not None else 0
@@ -538,6 +526,54 @@ def analyze_weather_trend(
         "cur_temp": cur_temp,
         "wu_settle": round(max_so_far) if max_so_far is not None else None,
     }
-
     display_str = "\n".join(insights) if insights else ""
     return display_str, "\n".join(ai_features), structured
+
+
+def calculate_prob_distribution(
+    mu: float, sigma: float, max_so_far: Optional[float], temp_symbol: str
+) -> Dict[str, Any]:
+    """
+    Generalized Gaussian probability distribution calculation.
+    """
+    if mu is None or sigma is None:
+        return {}
+
+    def _norm_cdf(x, m, s):
+        # 0.5 * (1 + erf( (x-m)/(s*sqrt(2)) ))
+        return 0.5 * (1 + math.erf((x - m) / (sigma * math.sqrt(2))))
+
+    min_possible_wu = round(max_so_far) if max_so_far is not None else -999
+    probs = {}
+    
+    # Range: mu +/- 3 sigma or at least +/- 2 degrees
+    search_range = max(2, int(sigma * 2.5))
+    target_mu = round(mu)
+    
+    for n in range(target_mu - search_range, target_mu + search_range + 1):
+        if n < min_possible_wu:
+            continue
+        p = _norm_cdf(n + 0.5, mu, sigma) - _norm_cdf(n - 0.5, mu, sigma)
+        if p > 0.01:
+            probs[n] = p
+
+    total_p = sum(probs.values())
+    sorted_probs = []
+    probabilities = []
+    
+    if total_p > 0:
+        norm_probs = {k: v / total_p for k, v in probs.items()}
+        sorted_probs = sorted(norm_probs.items(), key=lambda x: x[1], reverse=True)
+        for t, p in sorted_probs[:4]:
+            probabilities.append({
+                "value": int(t),
+                "range": f"[{t-0.5}~{t+0.5})",
+                "probability": round(p, 3)
+            })
+
+    return {
+        "mu": mu,
+        "sigma": sigma,
+        "probabilities": probabilities,
+        "sorted_probs": sorted_probs
+    }

web/app.py

@@ -284,7 +284,7 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
     # ── 10. Shared analysis (probability, trend, AI) via trend_engine ──
     # This single call replaces the duplicate probability engine, dead market
     # detection, forecast bust grading, and AI context building.
-    from src.analysis.trend_engine import analyze_weather_trend as _trend_analyze
+    from src.analysis.trend_engine import analyze_weather_trend as _trend_analyze, calculate_prob_distribution
     from src.analysis.ai_analyzer import get_ai_analysis
 
     probabilities = []
@@ -434,19 +434,33 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
                 day_m["MGM"] = _sf(mgm_daily[d_str])
             
             d_val, d_winfo = None, ""
+            d_probs = []
             if day_m:
                 try:
                     blended, winfo = calculate_dynamic_weights(city, day_m)
                     if blended is not None:
                         d_val = blended
                         d_winfo = winfo
+                        
+                        # Calculate future probability based on model divergence
+                        m_vals = [v for v in day_m.values() if v is not None]
+                        if len(m_vals) > 1:
+                            # Use spread as a proxy for sigma. 
+                            # sigma = (max-min)/2 with a floor of 0.6
+                            d_sigma = max(0.6, (max(m_vals) - min(m_vals)) / 2.0)
+                        else:
+                            d_sigma = 1.0
+                        
+                        prob_obj = calculate_prob_distribution(d_val, d_sigma, None, sym)
+                        d_probs = prob_obj.get("probabilities", [])
                 except Exception:
                     pass
         
         if day_m:
             multi_model_daily[d_str] = {
                 "models": day_m,
-                "deb": {"prediction": d_val, "weights_info": d_winfo}
+                "deb": {"prediction": d_val, "weights_info": d_winfo},
+                "probabilities": d_probs if i > 0 else probabilities # Use today's real prob for today
             }
 
     # ── Assemble result ──

web/static/app.js

@@ -818,8 +818,18 @@ function renderChart(data) {
 
 function renderProbabilities(data) {
   const container = document.getElementById("probBars");
-  const probs = data.probabilities?.distribution || [];
+  const targetDate = selectedForecastDate || data.local_date;
-  const mu = data.probabilities?.mu;
+
+  let probs = [];
+  let mu = null;
+
+  if (targetDate === data.local_date) {
+    probs = data.probabilities?.distribution || [];
+    mu = data.probabilities?.mu;
+  } else if (data.multi_model_daily && data.multi_model_daily[targetDate]) {
+    probs = data.multi_model_daily[targetDate].probabilities || [];
+    mu = data.multi_model_daily[targetDate].deb?.prediction;
+  }
 
   if (probs.length === 0) {
     container.innerHTML =
@@ -834,7 +844,6 @@ function renderProbabilities(data) {
 
   probs.forEach((p, i) => {
     const pct = Math.round(p.probability * 100);
-    const width = Math.max(pct, 8);
     html += `
             <div class="prob-row">
                 <div class="prob-label">${p.value}${data.temp_symbol}</div>
@@ -962,6 +971,7 @@ function switchForecastDate(cityName, dateStr) {
   const data = cityDataCache[cityName];
   if (data) {
     renderModels(data);
+    renderProbabilities(data);
     renderForecast(data);
   }
 }