144 lines
5.6 KiB
Python
144 lines
5.6 KiB
Python
"""
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Weather feature module.
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Fetches weather data from Open-Meteo API.
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"""
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import requests
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from datetime import datetime
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from ..core.config import WEATHER_LAT, WEATHER_LON, WEATHER_CITY
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def get_wmo_description(code: int) -> str:
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"""Decodes WMO weather code to Russian description."""
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codes = {
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0: "ясно",
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1: "преимущественно ясно",
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2: "переменная облачность",
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3: "пасмурно",
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45: "туман",
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48: "изморозь",
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51: "легкая морось",
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53: "умеренная морось",
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55: "плотная морось",
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56: "ледяная морось",
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57: "плотная ледяная морось",
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61: "слабый дождь",
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63: "умеренный дождь",
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65: "сильный дождь",
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66: "ледяной дождь",
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67: "сильный ледяной дождь",
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71: "слабый снег",
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73: "снегопад",
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75: "сильный снегопад",
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77: "снежные зерна",
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80: "слабый ливень",
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81: "умеренный ливень",
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82: "сильный ливень",
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85: "слабый снегопад",
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86: "сильный снегопад",
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95: "гроза",
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96: "гроза с градом",
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99: "сильная гроза с градом"
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}
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return codes.get(code, "осадки")
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def get_weather_report() -> str:
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"""
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Fetches detailed weather report.
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Structure:
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1. Current temp and precipitation.
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2. Today's min/max temp.
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3. Next 4 hours forecast (temp + precipitation).
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"""
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if not all([WEATHER_LAT, WEATHER_LON, WEATHER_CITY]):
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return "Настройки погоды не найдены. Проверьте конфигурацию."
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url = "https://api.open-meteo.com/v1/forecast"
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params = {
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"latitude": WEATHER_LAT,
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"longitude": WEATHER_LON,
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"current": "temperature_2m,precipitation,weather_code",
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"hourly": "temperature_2m,precipitation,weather_code",
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"daily": "temperature_2m_max,temperature_2m_min",
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"timezone": "auto",
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"forecast_days": 2
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}
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try:
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response = requests.get(url, params=params, timeout=5)
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response.raise_for_status()
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data = response.json()
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# --- 1. Current Weather ---
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curr = data["current"]
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temp_now = round(curr["temperature_2m"])
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precip_now = curr["precipitation"]
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code_now = curr["weather_code"]
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desc_now = get_wmo_description(code_now)
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report = f"Сейчас в городе {WEATHER_CITY} {temp_now} градусов, {desc_now}."
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if precip_now > 0:
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report += f" Выпало {precip_now} миллиметров осадков."
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# --- 2. Today's Range ---
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# daily arrays usually start from today [0]
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daily = data["daily"]
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t_max = round(daily["temperature_2m_max"][0])
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t_min = round(daily["temperature_2m_min"][0])
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report += f" Сегодня температура будет от {t_min} до {t_max} градусов."
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# --- 3. Forecast Next 4 Hours ---
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current_hour = datetime.now().hour
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hourly_temps = data["hourly"]["temperature_2m"]
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hourly_precip = data["hourly"]["precipitation"]
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hourly_codes = data["hourly"]["weather_code"]
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# Start from next hour
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start_idx = current_hour + 1
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end_idx = min(start_idx + 4, len(hourly_temps))
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next_temps = hourly_temps[start_idx:end_idx]
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next_precip = hourly_precip[start_idx:end_idx]
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next_codes = hourly_codes[start_idx:end_idx]
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if next_temps:
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report += " Прогноз на ближайшие 4 часа: "
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# Group by roughly similar weather to avoid repetition?
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# Or just list them simply.
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# "В 14:00 -5, ясно. В 15:00 -5, снег." -> a bit verbose.
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# Simplified: "Температура около -5, возможен слабый снег."
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# Let's verify if weather changes significantly.
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# If consistent, summarize. If not, list.
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# Simple approach for TTS:
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avg_temp = round(sum(next_temps) / len(next_temps))
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# Check if any precipitation is expected
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will_precip = any(p > 0 for p in next_precip)
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unique_codes = set(next_codes)
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# Determine dominant weather description
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if len(unique_codes) == 1:
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weather_desc = get_wmo_description(list(unique_codes)[0])
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else:
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# Priority to precipitation codes
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precip_codes = [c for c in unique_codes if c > 3] # >3 implies not clear/cloudy
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if precip_codes:
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weather_desc = get_wmo_description(max(precip_codes)) # Take the most severe
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else:
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weather_desc = "переменная облачность"
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report += f"температура около {avg_temp} градусов, {weather_desc}."
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if will_precip:
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report += " Ожидаются осадки."
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else:
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report += " Без существенных осадков."
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return report
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except Exception as e:
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print(f"❌ Ошибка получения погоды: {e}")
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return "Не удалось получить полные данные о погоде." |