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feat: Implement PolyWeather web map API with FastAPI, integrating existing weather data collection and analysis modules.

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This commit is contained in:
2569718930@qq.com
2026-03-05 16:33:00 +08:00
parent a1416d1324
commit 5f04506aad
7 changed files with 328 additions and 382 deletions
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bot_listener.py
+15 -67
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@@ -63,24 +63,9 @@ def start_bot():
)
return
from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
city_input = parts[1].strip().lower()
# 复用城市名映射
city_aliases = {
"ank": "ankara",
"lon": "london",
"par": "paris",
"nyc": "new york",
"chi": "chicago",
"dal": "dallas",
"mia": "miami",
"atl": "atlanta",
"sea": "seattle",
"tor": "toronto",
"sel": "seoul",
"ba": "buenos aires",
"wel": "wellington",
}
city_name = city_aliases.get(city_input, city_input)
city_name = ALIASES.get(city_input, city_input)
from src.analysis.deb_algorithm import load_history
import os as _os
@@ -262,75 +247,38 @@ def start_bot():
)
return
from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
city_input = parts[1].strip().lower()
# --- 使用统一注册表解析城市 ---
SUPPORTED_CITIES = list(CITY_REGISTRY.keys())
# --- 核心标准名称映射表 ---
# 这里的 Key 是缩写或别名，Value 是 Open-Meteo 识别的标准全称
STANDARD_MAPPING = {
"sel": "seoul",
"seo": "seoul",
"首尔": "seoul",
"lon": "london",
"伦敦": "london",
"tor": "toronto",
"多伦多": "toronto",
"ank": "ankara",
"安卡拉": "ankara",
"wel": "wellington",
"惠灵顿": "wellington",
"ba": "buenos aires",
"布宜诺斯艾利斯": "buenos aires",
"nyc": "new york",
"ny": "new york",
"纽约": "new york",
"chi": "chicago",
"芝加哥": "chicago",
"sea": "seattle",
"西雅图": "seattle",
"mia": "miami",
"迈阿密": "miami",
"atl": "atlanta",
"亚特兰大": "atlanta",
"dal": "dallas",
"达拉斯": "dallas",
"la": "los angeles",
"洛杉矶": "los angeles",
"par": "paris",
"巴黎": "paris",
}
# 支持的城市全名列表（用于模糊匹配）
SUPPORTED_CITIES = list(set(STANDARD_MAPPING.values()))
# 1. 第一优先级：严格全字匹配（别名/缩写）
city_name = STANDARD_MAPPING.get(city_input)
# 2. 第二优先级：输入本身就是城市全名
# 1. 第一优先级：全称或别名完全匹配
city_name = ALIASES.get(city_input)
if not city_name and city_input in SUPPORTED_CITIES:
city_name = city_input
# 3. 第三优先级：前缀匹配（在别名和城市全名中搜索）
# 2. 第二优先级：前缀模糊匹配
if not city_name and len(city_input) >= 2:
# 先搜别名
for k, v in STANDARD_MAPPING.items():
# 搜别名
for k, v in ALIASES.items():
if k.startswith(city_input):
city_name = v
break
# 再搜城市全名
# 搜城市全名
if not city_name:
for full_name in SUPPORTED_CITIES:
if full_name.startswith(city_input):
city_name = full_name
break
# 4. 未找到 → 报错，列出支持的城市
# 3. 未找到 → 报错
if not city_name:
city_list = ", ".join(sorted(set(STANDARD_MAPPING.values())))
city_list = ", ".join(sorted(SUPPORTED_CITIES))
bot.reply_to(
message,
f"❌ 未找到城市: <b>{city_input}</b>\n\n"
f"支持的城市: {city_list}\n\n"
f"也可以用缩写，如 <code>/city dal</code> 查达拉斯",
f"支持的城市: {city_list}",
parse_mode="HTML",
)
return
config/config.yaml
+16 -1
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@@ -1,6 +1,6 @@
# Weather API Configuration
weather:
meteoblue_api_key: null # Set via METEOBLUE_API_KEY env var
meteoblue_api_key: null # Set via METEOBLUE_API_KEY env var
timeout: 30
# Target Cities
@@ -30,6 +30,21 @@ cities:
country: "USA"
latitude: 41.8781
longitude: -87.6298
- id: "lucknow"
city: "Lucknow"
country: "India"
latitude: 26.7606
longitude: 80.8893
- id: "sao paulo"
city: "São Paulo"
country: "Brazil"
latitude: -23.4356
longitude: -46.4731
- id: "munich"
city: "Munich"
country: "Germany"
latitude: 48.3538
longitude: 11.7861
# Logging
logging:
docs/POLYMUSIC_INDEPENDENT_DOC.md
-87
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@@ -1,87 +0,0 @@
# 🎵 PolyMusic: 音乐市场量化分析系统 - 独立开发文档
## 1. 项目定位
PolyMusic 是一个独立的量化分析系统，旨在通过聚合 **流媒体实测数据 (Spotify/Apple Music)**、**社交媒体前置信号 (TikTok/YouTube)** 和 **行业动态 (Awards/Releases)**，为音乐相关的预测市场提供精准的决策分析。
---
## 2. 核心架构设计
### A. 数据采集层 (The Data Feed)
- **Spotify Scanner**:
- 每日定时抓取 Global & US Top 200 榜单。
- 提取字段：Track Name, Artist, Daily Streams, Position, Previous Position.
- **Viral Tracker (前哨信号)**:
- 监控 TikTok 热门音频趋势图，追踪 BGM 使用量增幅 (Acceleration)。
- 监控 YouTube Trending 榜单。
- **Contextual Hub (行业动态)**:
- 重大活动日历：超级碗、格莱美、科切拉音乐节、主流艺人回归预热。
### B. 分析引擎 (The Trend Engine)
- **Accumulation Solver (积分缺口分析)**:
- 专门针对“周榜”市场。根据当前已消耗的天数，计算出各候选人要反超第一名所需的每日平均流值及其标准差。
- **Decay Controller (热度半衰期计算)**:
- 为突发热点（如夺金表演、空降 MV）建立衰减曲线记录。判断当前的高流值是“单日冲击”还是“长期阶跃”。
- **Consensus Tracker**:
- 对比 Kworb, Billboard 预测与 Polymarket 赔率的偏差 (MAE)。
### C. AI 决策层 (Groq LLaMA 3.3 70B)
- **Prompt 逻辑框架**:
- **P0 (Event Trigger)**: 是否有头部艺人突然在 Instagram/Twitter 进行大规模预热或空降。
- **P1 (Viral Drift)**: 该歌曲的传播是否已由于某个非音乐事件（体育、电影、社交媒体挑战）出现斜率阶跃。
- **P2 (Consistency Check)**: 预测市场的价格波动是否与目前观察到的流值增长率相匹配。
---
## 3. 技术栈建议
- **Backend**: Python 3.11+ (FastAPI)
- **Database**: SQLite (存储每日流值与历史 MAE)
- **AI Engine**: Groq SDK (LLaMA 3.3 70B)
- **Crawler**: Requests / BeautifulSoup / Selenium (用于绕过部分动态频率限制)
---
## 4. 目录结构预览 (Independent Project)
```text
PolyMusic/
├── src/
│ ├── data/ # 爬虫与数据采集
│ │ ├── spotify.py
│ │ ├── tiktok.py
│ │ └── billboard.py
│ ├── analysis/ # 核心算法
│ │ ├── accumulation.py
│ │ └── decay_model.py
│ └── ai/ # AI 决策管线
│ └── prompt_engine.py
├── data/ # 本地存储 (JSON/SQLite)
├── web/ # 可视化面板 (类似于 PolyWeather Map)
├── bot_listener.py # Telegram 交互入口
└── requirements.txt
```
---
## 5. 核心博弈指标 (KPIs)
- **Stream Gap (流值缺口)**：反超所需最低日均流值。
- **Momentum Coefficient (动能系数)**：流值增长的二阶导数。
- **Price Inaccuracy (定价误差)**：Polymarket 赔率与量化结果的期望偏差。
---
## 6. 下一步动作
1. 初始化项目结构。
2. 优先攻克 Spotify 每日 Top 200 的数据持久化（通过爬虫或自动化工具）。
3. 建立第一个周榜计算模型，并在本周五（3月6日结算日）进行实战对账。
---
_Document created for independent project initialization on 2026-03-04_
src/data_collection/city_registry.py
+241
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@@ -0,0 +1,241 @@
# PolyWeather City Registry
# A unified "Source of Truth" for all tracked cities, coordinates, ICAO codes, and risk profiles.
CITY_REGISTRY = {
"ankara": {
"name": "Ankara",
"lat": 40.1281,
"lon": 32.9951,
"icao": "LTAC",
"tz_offset": 10800,
"use_fahrenheit": False,
"risk_level": "medium",
"risk_emoji": "🟡",
"airport_name": "Esenboğa 机场",
"distance_km": 24.5,
"warning": "内陆高原城市，昼夜温差大（可达15°C+）; 激进取整效应明显。",
},
"london": {
"name": "London",
"lat": 51.5048,
"lon": 0.0522,
"icao": "EGLC",
"tz_offset": 0,
"use_fahrenheit": False,
"risk_level": "low",
"risk_emoji": "🟢",
"airport_name": "London City 机场",
"distance_km": 12.7,
"warning": "泰晤士河局部微气候影响。",
},
"paris": {
"name": "Paris",
"lat": 49.0097,
"lon": 2.5480,
"icao": "LFPG",
"tz_offset": 3600,
"use_fahrenheit": False,
"risk_level": "medium",
"risk_emoji": "🟡",
"airport_name": "Charles de Gaulle 机场",
"distance_km": 25.2,
"warning": "城市热岛效应：市区比机场偏暖1-2°C。",
},
"seoul": {
"name": "Seoul",
"lat": 37.4602,
"lon": 126.4407,
"icao": "RKSI",
"tz_offset": 32400,
"use_fahrenheit": False,
"risk_level": "high",
"risk_emoji": "🔴",
"airport_name": "仁川国际机场",
"distance_km": 48.8,
"warning": "距离太远，海洋性vs大陆性气候差异大。",
},
"toronto": {
"name": "Toronto",
"lat": 43.6777,
"lon": -79.6248,
"icao": "CYYZ",
"tz_offset": -18000,
"use_fahrenheit": False,
"risk_level": "low",
"risk_emoji": "🟢",
"airport_name": "Pearson 国际机场",
"distance_km": 19.6,
"warning": "冬季湖效应偶尔导致局部强降温。",
},
"buenos aires": {
"name": "Buenos Aires",
"lat": -34.8222,
"lon": -58.5358,
"icao": "SAEZ",
"tz_offset": -10800,
"use_fahrenheit": False,
"risk_level": "medium",
"risk_emoji": "🟡",
"airport_name": "Ezeiza 国际机场",
"distance_km": 28.1,
"warning": "夏天城区可比郊区高2-3°C。",
},
"wellington": {
"name": "Wellington",
"lat": -41.3272,
"lon": 174.8053,
"icao": "NZWN",
"tz_offset": 46800,
"use_fahrenheit": False,
"risk_level": "low",
"risk_emoji": "🟢",
"airport_name": "惠灵顿国际机场",
"distance_km": 5.5,
"warning": "风大影响体感，但温度测量偏差小。",
},
"new york": {
"name": "New York",
"lat": 40.7769,
"lon": -73.8740,
"icao": "KLGA",
"tz_offset": -18000,
"use_fahrenheit": True,
"risk_level": "low",
"risk_emoji": "🟢",
"airport_name": "LaGuardia 机场",
"distance_km": 14.5,
"warning": "东河水汽可能在春季产生温差。",
},
"chicago": {
"name": "Chicago",
"lat": 41.9742,
"lon": -87.9073,
"icao": "KORD",
"tz_offset": -21600,
"use_fahrenheit": True,
"risk_level": "high",
"risk_emoji": "🔴",
"airport_name": "O'Hare 国际机场",
"distance_km": 25.3,
"warning": "密歇根湖效应：湖边vs内陆可差10°F+。",
},
"dallas": {
"name": "Dallas",
"lat": 32.8471,
"lon": -96.8518,
"icao": "KDAL",
"tz_offset": -21600,
"use_fahrenheit": True,
"risk_level": "medium",
"risk_emoji": "🟡",
"airport_name": "Dallas Love Field 机场",
"distance_km": 11.2,
"warning": "城市热岛效应在夏季午后会使温度略高于郊区。",
},
"miami": {
"name": "Miami",
"lat": 25.7959,
"lon": -80.2870,
"icao": "KMIA",
"tz_offset": -18000,
"use_fahrenheit": True,
"risk_level": "low",
"risk_emoji": "🟢",
"airport_name": "Miami 国际机场",
"distance_km": 10.3,
"warning": "温差较小，数据稳定。",
},
"atlanta": {
"name": "Atlanta",
"lat": 33.6407,
"lon": -84.4277,
"icao": "KATL",
"tz_offset": -18000,
"use_fahrenheit": True,
"risk_level": "low",
"risk_emoji": "🟢",
"airport_name": "Hartsfield-Jackson 机场",
"distance_km": 12.6,
"warning": "数据较准。",
},
"seattle": {
"name": "Seattle",
"lat": 47.4502,
"lon": -122.3088,
"icao": "KSEA",
"tz_offset": -28800,
"use_fahrenheit": True,
"risk_level": "low",
"risk_emoji": "🟢",
"airport_name": "Sea-Tac 国际机场",
"distance_km": 17.4,
"warning": "微气候差异存在但较小。",
},
"lucknow": {
"name": "Lucknow",
"lat": 26.7606,
"lon": 80.8893,
"icao": "VILK",
"tz_offset": 19800,
"use_fahrenheit": False,
"risk_level": "medium",
"risk_emoji": "🟡",
"airport_name": "Chaudhary Charan Singh 国际机场",
"distance_km": 14.0,
"warning": "印度北方热岛效应，夏季午后机场反馈略低于市区。",
},
"sao paulo": {
"name": "São Paulo",
"lat": -23.4356,
"lon": -46.4731,
"icao": "SBGR",
"tz_offset": -10800,
"use_fahrenheit": False,
"risk_level": "high",
"risk_emoji": "🔴",
"airport_name": "São Paulo/Guarulhos 机场",
"distance_km": 25.0,
"warning": "距离远且海拔有差异，局部降雨温差极大。",
},
"munich": {
"name": "Munich",
"lat": 48.3538,
"lon": 11.7861,
"icao": "EDDM",
"tz_offset": 3600,
"use_fahrenheit": False,
"risk_level": "high",
"risk_emoji": "🔴",
"airport_name": "Munich 机场",
"distance_km": 28.5,
"warning": "距离非常远，空旷机场夜间降温快，冬季易生大雾压制气温。",
},
}
ALIASES = {
# English shortcuts
"ank": "ankara", "lon": "london", "par": "paris",
"nyc": "new york", "ny": "new york", "chi": "chicago",
"dal": "dallas", "mia": "miami", "atl": "atlanta",
"sea": "seattle", "tor": "toronto", "sel": "seoul",
"seo": "seoul", "ba": "buenos aires", "wel": "wellington",
"luc": "lucknow", "sp": "sao paulo", "mun": "munich",
# Chinese names
"安卡拉": "ankara",
"伦敦": "london",
"巴黎": "paris",
"纽约": "new york",
"芝加哥": "chicago",
"达拉斯": "dallas",
"迈阿密": "miami",
"亚特兰大": "atlanta",
"西雅图": "seattle",
"多伦多": "toronto",
"首尔": "seoul",
"布宜诺斯艾利斯": "buenos aires",
"惠灵顿": "wellington",
"勒克瑙": "lucknow",
"圣保罗": "sao paulo",
"慕尼黑": "munich",
}
src/data_collection/city_risk_profiles.py
+18 -161
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@@ -1,171 +1,28 @@
# Polymarket 城市温度市场 - 数据偏差风险档案
# 基于 METAR 机场站与市区实际温度的系统性差异
from src.data_collection.city_registry import CITY_REGISTRY
# Generate profiles from registry
CITY_RISK_PROFILES = {
# 🔴 高危城市 - 数据偏差大，容易误判
"seoul": {
"risk_level": "high",
"risk_emoji": "🔴",
"icao": "RKSI",
"airport_name": "仁川国际机场",
"distance_km": 48.8,
"elevation_diff_m": 0,
"typical_bias_f": 5.8,
"bias_direction": "机场靠海偏暖，市区内陆更冷",
"warning": "距离太远，根本不是同一个天气区",
"season_notes": None,
},
"chicago": {
"risk_level": "high",
"risk_emoji": "🔴",
"icao": "KORD",
"airport_name": "O'Hare 国际机场",
"distance_km": 25.3,
"elevation_diff_m": 42,
"typical_bias_f": 4.0,
"bias_direction": "密歇根湖效应：风向变化时湖边vs内陆可差10°F+",
"warning": "冬天温差最不稳定",
"season_notes": "冬季",
},
# 🟡 中危城市 - 存在系统偏差，需注意
"ankara": {
"risk_level": "medium",
"risk_emoji": "🟡",
"icao": "LTAC",
"airport_name": "Esenboğa 机场",
"distance_km": 24.5,
"elevation_diff_m": 65,
"typical_bias_f": 2.0,
"bias_direction": "机场海拔更高",
"warning": "内陆高原城市，昼夜温差大（可达15°C+）",
"season_notes": "下午最高温时偏差会放大",
"metar_rounding": "激进取整：METAR 报告的温度偏高，例如实际 3.4°C 可能报告为 4°C，这意味着 METAR 显示的整数温度往往已接近下一个 WU 结算值。",
},
"london": {
"risk_level": "low",
"risk_emoji": "🟢",
"icao": "EGLC",
"airport_name": "London City 机场",
"distance_km": 12.7,
"elevation_diff_m": 4,
"typical_bias_f": 0.5,
"bias_direction": "河水调节效应：泰晤士河 Royal Docks 使得夏天偏凉，冬天偏暖",
"warning": "极端天气日（热浪/寒潮）偏差会显著放大",
"season_notes": None,
},
"dallas": {
"risk_level": "medium",
"risk_emoji": "🟡",
"icao": "KDAL",
"airport_name": "Dallas Love Field 机场",
"distance_km": 11.2,
"elevation_diff_m": 0,
"typical_bias_f": 1.1,
"bias_direction": "比 DFW 更接近市中心，数据更准",
"warning": "城市热岛效应在夏季午后会使温度略高于郊区",
"season_notes": None,
},
"buenos aires": {
"risk_level": "medium",
"risk_emoji": "🟡",
"icao": "SAEZ",
"airport_name": "Ezeiza 国际机场",
"distance_km": 28.1,
"elevation_diff_m": 0,
"typical_bias_f": 1.2,
"bias_direction": "夏天城区可比郊区高2-3°C",
"warning": "距离远但地形平坦，偏差稳定可预测",
"season_notes": "夏季",
},
"paris": {
"risk_level": "medium",
"risk_emoji": "🟡",
"icao": "LFPG",
"airport_name": "Charles de Gaulle 机场",
"distance_km": 25.2,
"elevation_diff_m": 26,
"typical_bias_f": 1.5,
"bias_direction": "城市热岛效应：市区比机场偏暖1-2°C",
"warning": "机场在北郊，冬季北风时比市区更冷",
"season_notes": "夏季热浪期间偏差最大",
},
# 🟢 低危城市 - 数据相对靠谱
"toronto": {
"risk_level": "low",
"risk_emoji": "🟢",
"icao": "CYYZ",
"airport_name": "Pearson 国际机场",
"distance_km": 19.6,
"elevation_diff_m": 0,
"typical_bias_f": 0.3,
"bias_direction": None,
"warning": "冬季湖效应偶尔炸裂",
"season_notes": "冬季",
},
"new york": {
"risk_level": "low",
"risk_emoji": "🟢",
"icao": "KLGA",
"airport_name": "LaGuardia 机场",
"distance_km": 14.5,
"elevation_diff_m": 0,
"typical_bias_f": 0.7,
"bias_direction": "相比 JFK 更靠近曼哈顿",
"warning": "东河水汽可能在春季产生微小的降温效果",
"season_notes": None,
},
"seattle": {
"risk_level": "low",
"risk_emoji": "🟢",
"icao": "KSEA",
"airport_name": "Sea-Tac 国际机场",
"distance_km": 17.4,
"elevation_diff_m": 0,
"typical_bias_f": 0.6,
"bias_direction": "微气候差异存在但较小",
"warning": None,
"season_notes": None,
},
"atlanta": {
"risk_level": "low",
"risk_emoji": "🟢",
"icao": "KATL",
"airport_name": "Hartsfield-Jackson 机场",
"distance_km": 12.6,
"elevation_diff_m": 0,
"typical_bias_f": 0.5,
"bias_direction": None,
"warning": None,
"season_notes": None,
},
"miami": {
"risk_level": "low",
"risk_emoji": "🟢",
"icao": "KMIA",
"airport_name": "Miami 国际机场",
"distance_km": 10.3,
"elevation_diff_m": 0,
"typical_bias_f": 0.3,
"bias_direction": None,
"warning": None,
"season_notes": None,
},
"wellington": {
"risk_level": "low",
"risk_emoji": "🟢",
"icao": "NZWN",
"airport_name": "Wellington 机场",
"distance_km": 5.1,
"elevation_diff_m": 0,
"typical_bias_f": 0.2,
"bias_direction": None,
"warning": "12城最近，数据最靠谱",
"season_notes": None,
},
cid: {
"risk_level": info["risk_level"],
"risk_emoji": info["risk_emoji"],
"icao": info["icao"],
"airport_name": info["airport_name"],
"distance_km": info["distance_km"],
"warning": info["warning"],
# Backwards compatibility flags if needed
"typical_bias_f": info.get("typical_bias_f", 0.0),
"elevation_diff_m": info.get("elevation_diff_m", 0),
"bias_direction": info.get("bias_direction", None),
"season_notes": info.get("season_notes", None),
}
for cid, info in CITY_REGISTRY.items()
}
def get_city_risk_profile(city_name: str) -> dict:
def get_city_risk_profile(city: str) -> dict:
"""获取城市的风险档案"""
city_lower = city_name.lower().strip()
src/data_collection/weather_sources.py
+25 -42
View File
@@ -17,24 +17,10 @@ class WeatherDataCollector:
- NOAA Aviation Weather (METAR - airport observations)
"""
# Polymarket 12 个天气市场对应的 ICAO 机场代码
# 这些是 Weather Underground 结算源使用的气象站
CITY_TO_ICAO = {
"seattle": "KSEA", # Seattle-Tacoma Airport
"london": "EGLC", # London City Airport
"dallas": "KDAL", # Dallas Love Field
"miami": "KMIA", # Miami International
"atlanta": "KATL", # Hartsfield-Jackson
"chicago": "KORD", # O'Hare International
"new york": "KLGA", # LaGuardia Airport
"nyc": "KLGA", # Alias
"seoul": "RKSI", # Incheon International
"ankara": "LTAC", # Esenboğa International
"toronto": "CYYZ", # Toronto Pearson
"wellington": "NZWN", # Wellington International
"buenos aires": "SAEZ", # Ezeiza International
"paris": "LFPG", # Charles de Gaulle
}
from src.data_collection.city_registry import CITY_REGISTRY
CITY_TO_ICAO = {cid: info["icao"] for cid, info in CITY_REGISTRY.items()}
# Alias
CITY_TO_ICAO["nyc"] = "KLGA"
# 城市周边 METAR 集群（用于在全球城市模拟类似安卡拉的多测站地图分布）
CITY_METAR_CLUSTERS = {
@@ -49,6 +35,8 @@ class WeatherDataCollector:
"atlanta": ["KATL", "KPDK", "KFTY"],
"miami": ["KMIA", "KOPF", "KTMB"],
"seattle": ["KSEA", "KBFI", "KPAE"],
"sao paulo": ["SBGR", "SBSP", "SBKP"],
"munich": ["EDDM", "EDMO", "EDJA"],
}
def __init__(self, config: dict):
@@ -1196,34 +1184,29 @@ class WeatherDataCollector:
"""
使用 Open-Meteo Geocoding API 获取城市坐标 (免费, 无需 Key)
"""
# 坐标使用 METAR 机场位置（Polymarket 以机场数据结算）
static_coords = {
"london": {"lat": 51.5053, "lon": 0.0553}, # EGLC London City
"paris": {"lat": 49.0097, "lon": 2.5478}, # LFPG Charles de Gaulle
"new york": {"lat": 40.7750, "lon": -73.8750}, # KLGA LaGuardia
"new york's central park": {"lat": 40.7812, "lon": -73.9665},
"nyc": {"lat": 40.7750, "lon": -73.8750}, # KLGA LaGuardia
"seattle": {"lat": 47.4499, "lon": -122.3118}, # KSEA Sea-Tac
"chicago": {"lat": 41.9769, "lon": -87.9081}, # KORD O'Hare
"dallas": {"lat": 32.8459, "lon": -96.8509}, # KDAL Love Field
"miami": {"lat": 25.7933, "lon": -80.2906}, # KMIA International
"atlanta": {"lat": 33.6367, "lon": -84.4281}, # KATL Hartsfield-Jackson
"seoul": {"lat": 37.4691, "lon": 126.4510}, # RKSI Incheon
"toronto": {"lat": 43.6759, "lon": -79.6294}, # CYYZ Pearson
"ankara": {"lat": 40.1281, "lon": 32.9950}, # LTAC Esenboğa
"wellington": {"lat": -41.3272, "lon": 174.8053}, # NZWN Wellington
"buenos aires": {"lat": -34.8222, "lon": -58.5358}, # SAEZ Ezeiza
}
from src.data_collection.city_registry import CITY_REGISTRY
normalized_city = city.lower().strip()
if normalized_city in static_coords:
return static_coords[normalized_city]
# 模糊匹配映射 (针对包含城市名的情况)
for key in static_coords:
# 1. Check registry first (Source of Truth)
if normalized_city in CITY_REGISTRY:
info = CITY_REGISTRY[normalized_city]
return {"lat": info["lat"], "lon": info["lon"]}
# 2. Hardcoded specific cases or aliases
static_aliases = {
"new york's central park": "new york",
"nyc": "new york"
}
if normalized_city in static_aliases:
root_city = static_aliases[normalized_city]
info = CITY_REGISTRY[root_city]
return {"lat": info["lat"], "lon": info["lon"]}
for key in CITY_REGISTRY:
if key in normalized_city:
logger.debug(f"地理编码命中模糊映射: {city} -> {key}")
return static_coords[key]
info = CITY_REGISTRY[key]
return {"lat": info["lat"], "lon": info["lon"]}
try:
url = "https://geocoding-api.open-meteo.com/v1/search"
web/app.py
+13 -24
View File
@@ -39,31 +39,20 @@ app.mount("/static", StaticFiles(directory=_static), name="static")
_config = load_config()
_weather = WeatherDataCollector(_config)
# ──────────────────────────────────────────────────────────
# City Registry
# ──────────────────────────────────────────────────────────
CITIES: Dict[str, Dict[str, Any]] = {
"ankara": {"lat": 40.1281, "lon": 32.9951, "f": False, "tz": 10800},
"london": {"lat": 51.5048, "lon": 0.0522, "f": False, "tz": 0},
"paris": {"lat": 49.0097, "lon": 2.5480, "f": False, "tz": 3600},
"seoul": {"lat": 37.4602, "lon": 126.4407, "f": False, "tz": 32400},
"toronto": {"lat": 43.6777, "lon": -79.6248, "f": False, "tz": -18000},
"buenos aires": {"lat": -34.8222, "lon": -58.5358, "f": False, "tz": -10800},
"wellington": {"lat": -41.3272, "lon": 174.8053, "f": False, "tz": 46800},
"new york": {"lat": 40.7769, "lon": -73.8740, "f": True, "tz": -18000},
"chicago": {"lat": 41.9742, "lon": -87.9073, "f": True, "tz": -21600},
"dallas": {"lat": 32.8471, "lon": -96.8518, "f": True, "tz": -21600},
"miami": {"lat": 25.7959, "lon": -80.2870, "f": True, "tz": -18000},
"atlanta": {"lat": 33.6407, "lon": -84.4277, "f": True, "tz": -18000},
"seattle": {"lat": 47.4502, "lon": -122.3088, "f": True, "tz": -28800},
}
from src.data_collection.city_registry import CITY_REGISTRY, ALIASES
ALIASES = {
"ank": "ankara", "lon": "london", "par": "paris",
"nyc": "new york", "chi": "chicago", "dal": "dallas",
"mia": "miami", "atl": "atlanta", "sea": "seattle",
"tor": "toronto", "sel": "seoul", "ba": "buenos aires",
"wel": "wellington",
# ──────────────────────────────────────────────────────────
# City Registry Transformation
# ──────────────────────────────────────────────────────────
# Convert registry to the internal format expected by app logic
CITIES: Dict[str, Dict[str, Any]] = {
cid: {
"lat": info["lat"],
"lon": info["lon"],
"f": info["use_fahrenheit"],
"tz": info["tz_offset"]
}
for cid, info in CITY_REGISTRY.items()
}
# ──────────────────────────────────────────────────────────