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

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This commit is contained in:
2569718930@qq.com
2026-03-05 04:28:35 +08:00
parent 7c087b2c81
commit a1416d1324
3 changed files with 87 additions and 27 deletions
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src/analysis/trend_engine.py
+58 -22
View File
@@ -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
def _norm_cdf(x, m, s):
return 0.5 * (1 + math.erf((x - m) / (s * math.sqrt(2))))
min_possible_wu = round(max_so_far) if max_so_far is not None else -999
probs = {}
for n in range(round(mu) - 2, round(mu) + 3):
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())
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)
# Probability Engine
probs_result = calculate_prob_distribution(
mu, sigma, max_so_far, temp_symbol
)
mu = probs_result.get("mu", mu)
probabilities = probs_result.get("probabilities", [])
sorted_probs = probs_result.get("sorted_probs", [])
if sorted_probs:
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
+16 -2
View File
@@ -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
+13 -3
View File
@@ -818,8 +818,18 @@ function renderChart(data) {
function renderProbabilities(data) {
const container = document.getElementById("probBars");
const probs = data.probabilities?.distribution || [];
const mu = data.probabilities?.mu;
const targetDate = selectedForecastDate || data.local_date;
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);
}
}