team-2/PlantDashboard/plant_meteo.py
2025-08-02 06:14:14 +02:00

83 lines
3.6 KiB
Python

import openmeteo_requests
import pandas as pd
import requests_cache
from retry_requests import retry
import geocoder
class HappyMeteo:
def __init__(self):
# Setup the Open-Meteo API client with cache and retry on error
cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)
retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
self.openmeteo = openmeteo_requests.Client(session = retry_session)
def get_current_location(self):
"""Get current location using IP geolocation"""
try:
g = geocoder.ip('me')
if g.ok:
latitude = g.latlng[0]
longitude = g.latlng[1]
print(f"Latitude: {latitude}")
print(f"Longitude: {longitude}")
print(f"Address: {g.address}")
return latitude, longitude
else:
print("Could not determine location")
return None, None
except Exception as e:
print(f"Error getting location: {e}")
return None, None
def openMeteoCall(self, timeLapse):
lat, lon = self.get_current_location()
# Make sure all required weather variables are listed here
# The order of variables in hourly or daily is important to assign them correctly below
url = "https://api.open-meteo.com/v1/forecast"
params = {
"latitude": lat,
"longitude": lon,
"daily": ["weather_code", "temperature_2m_mean", "rain_sum", "showers_sum", "precipitation_sum", "daylight_duration", "relative_humidity_2m_mean"],
"timezone": "auto",
"forecast_days": timeLapse
}
responses = self.openmeteo.weather_api(url, params=params)
# Process first location. Add a for-loop for multiple locations or weather models
response = responses[0]
print(f"Coordinates: {response.Latitude()}°N {response.Longitude()}°E")
print(f"Elevation: {response.Elevation()} m asl")
print(f"Timezone: {response.Timezone()}{response.TimezoneAbbreviation()}")
print(f"Timezone difference to GMT+0: {response.UtcOffsetSeconds()}s")
# Process daily data. The order of variables needs to be the same as requested.
daily = response.Daily()
daily_weather_code = daily.Variables(0).ValuesAsNumpy()
daily_temperature_2m_mean = daily.Variables(1).ValuesAsNumpy()
daily_rain_sum = daily.Variables(2).ValuesAsNumpy()
daily_showers_sum = daily.Variables(3).ValuesAsNumpy()
daily_precipitation_sum = daily.Variables(4).ValuesAsNumpy()
daily_daylight_duration = daily.Variables(5).ValuesAsNumpy()
daily_relative_humidity_2m_mean = daily.Variables(6).ValuesAsNumpy()
daily_data = {"date": pd.date_range(
start = pd.to_datetime(daily.Time(), unit = "s", utc = True),
end = pd.to_datetime(daily.TimeEnd(), unit = "s", utc = True),
freq = pd.Timedelta(seconds = daily.Interval()),
inclusive = "left"
)}
daily_data["weather_code"] = daily_weather_code
daily_data["temperature_2m_mean"] = daily_temperature_2m_mean
daily_data["rain_sum"] = daily_rain_sum
daily_data["showers_sum"] = daily_showers_sum
daily_data["precipitation_sum"] = daily_precipitation_sum
daily_data["daylight_duration"] = daily_daylight_duration
daily_data["relative_humidity_2m_mean"] = daily_relative_humidity_2m_mean
daily_dataframe = pd.DataFrame(data = daily_data)
print("\nDaily data\n", daily_dataframe)