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import openmeteo_requests
import requests_cache
import pandas as pd
from retry_requests import retry
# 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)
openmeteo = openmeteo_requests.Client(session=retry_session)
def get_info_meteo(latitude, longitude):
"""Function that creates a Json file containing the weather data of the location
ARGS:
latitude (Float) : latitude coordinate
longitude (Float): longitude coordinate
"""
url = "https://api.open-meteo.com/v1/forecast"
params = {
"latitude": latitude,
"longitude": longitude,
"hourly": {"relative_humidity_2m", "soil_temperature_0cm", "soil_moisture_0_to_1cm"},
"daily": {"temperature_2m_max", "precipitation_sum", "wind_speed_10m_max", "sunshine_duration"},
"past_days": 30,
"forecast_days": 7
}
responses = openmeteo.weather_api(url, params=params)
response = responses[0]
# Hourly dataframe
hourly = response.Hourly()
hourly_relative_humidity_2m = hourly.Variables(0).ValuesAsNumpy()
hourly_soil_temperature_0cm = hourly.Variables(1).ValuesAsNumpy()
hourly_soil_moisture_0_to_1cm = hourly.Variables(2).ValuesAsNumpy()
hourly_data = {"date": pd.date_range(
start=pd.to_datetime(hourly.Time(), unit="s", utc=True),
end=pd.to_datetime(hourly.TimeEnd(), unit="s", utc=True),
freq=pd.Timedelta(seconds=hourly.Interval()),
inclusive="left"
)}
hourly_data["relative_humidity_2m_%"] = hourly_relative_humidity_2m
hourly_data["soil_temperature_0cm_C"] = hourly_soil_temperature_0cm
hourly_data["soil_moisture_0_to_1cm_m³"] = hourly_soil_moisture_0_to_1cm
hourly_dataframe = pd.DataFrame(data=hourly_data)
# Average hourly data per day
hourly_dataframe['day_date'] = hourly_dataframe['date'].dt.strftime(
'%Y-%m-%d')
tmp = hourly_dataframe.groupby('day_date')[
['relative_humidity_2m_%', 'soil_temperature_0cm_C', 'soil_moisture_0_to_1cm_m³']].mean()
avg_hourly_dataframe = tmp.rename(columns={'relative_humidity_2m_%': 'avg_hourly_relative_humidity_2m_%',
'soil_temperature_0cm_C': 'avg_hourly_soil_temperature_0cm_C', 'soil_moisture_0_to_1cm_m³': 'avg_hourly_soil_moisture_0_to_1cm_m³'})
# Daily dataframe
daily = response.Daily()
daily_temperature_2m_max = daily.Variables(0).ValuesAsNumpy()
daily_precipitation_sum = daily.Variables(1).ValuesAsNumpy()
daily_wind_speed_10m_max = daily.Variables(2).ValuesAsNumpy()
daily_sunshine_duration = daily.Variables(3).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='D',
inclusive="left"
)}
daily_data["daily_temperature_2m_max_C"] = daily_temperature_2m_max
daily_data["daily_precipitation_sum_mm"] = daily_precipitation_sum
daily_data["daily_wind_speed_10m_max_km/h"] = daily_wind_speed_10m_max
daily_data["daily_sunshine_duration_sec"] = daily_sunshine_duration
daily_dataframe = pd.DataFrame(data=daily_data)
daily_dataframe['day_date'] = daily_dataframe['date'].dt.strftime(
'%Y-%m-%d')
total_df = pd.merge(daily_dataframe, avg_hourly_dataframe,
on="day_date", how="left")
total_df['date'] = total_df['day_date']
total_df = total_df.drop('day_date', axis=1)
return total_df
#total_df.to_json("app/data/weather_data.json", orient='columns')
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