def _weather_data_extraction(lat, lon):
start = datetime(2015, 1, 1)
end = datetime(2021, 1, 16)
stations = Stations()
stations = stations.nearby(lat, lon)
stations = stations.inventory('daily', (start, end))
station = stations.fetch(1)
data = Daily(station, start, end)
data = data.fetch()
return data
"""
Example: Closest weather station by coordinates
Meteorological data provided by Meteostat (https://dev.meteostat.net)
under the terms of the Creative Commons Attribution-NonCommercial
4.0 International Public License.
The code is licensed under the MIT license.
"""
from meteostat import Stations
# Get weather station
stations = Stations()
stations = stations.nearby(50, 8)
stations = stations.inventory('hourly', True)
station = stations.fetch(1).to_dict('records')[0]
# Print name
print('Closest weather station at coordinates 50, 8:', station["name"])
from datetime import datetime
import matplotlib.pyplot as plt
from meteostat import Stations, Daily
# Configuration
Daily.max_threads = 5
# Time period
start = datetime(1980, 1, 1)
end = datetime(2019, 12, 31)
# Get random weather stations in the US
stations = Stations()
stations = stations.region('US')
stations = stations.inventory('daily', (start, end))
stations = stations.fetch(limit=20, sample=True)
# Get daily data
data = Daily(stations, start, end)
# Normalize & aggregate
data = data.normalize().aggregate('1Y', spatial=True).fetch()
# Chart title
TITLE = 'Average US Annual Temperature from 1980 to 2019'
# Plot chart
data.plot(y=['tavg'], title=TITLE)
plt.show()