def fetch(
start=datetime(2020, 1, 1),
end=datetime.now(),
lat=33.5020, # Closest to UAB
lon=-86.8064):
start += timedelta(hours=6) # UTC offset
end += timedelta(hours=6)
end.replace(microsecond=0, second=0)
stations = Stations(lat=lat, lon=lon)
station = stations.fetch(1)
data = Hourly(station, start, end)
data = data.normalize()
data = data.interpolate()
df = data.fetch()
out = {}
last_row = None
for row in df.itertuples():
if last_row:
out.update(interpolate_minutes(last_row, row))
last_row = row
current_time = last_row.time - timedelta(hours=6)
while current_time <= end - timedelta(hours=6):
out[create_datetime_ID(current_time)] = {
"time": current_time,
"temp": last_row.temp
}
current_time += timedelta(minutes=1)
return out
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
def meteostat(lat_degr,long_degr,alt_avrg,start_time,end_time):
#Retrieve nearest weather station
stations = Stations()
stations = stations.nearby(lat_degr, long_degr)
station = stations.fetch(1)
#Use Point to agregate nearby weather stations data for better accuracy
weather_point = Point(lat_degr, long_degr,alt_avrg)
weather_data = Hourly(weather_point, start_time - timedelta(0,7200), end_time + timedelta(0,7200))
weather_data = weather_data.fetch()
#Use weather data from nearest station if Point returns an empty dataset
if weather_data.empty:
weather_data = Hourly(station, start_time - timedelta(0,7200), end_time + timedelta(0,7200))
weather_data = weather_data.fetch()
return weather_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()
from meteostat import Stations, Daily from datetime import datetime import matplotlib.pyplot as plt # Hourly stations = Stations(lat=49.2497, lon=-123.1193) station = stations.fetch(1) data = Daily(station, start=datetime(2018, 1, 1), end=datetime(2018, 12, 31)) data = data.fetch() data.plot(x='time', y=['tavg', 'tmin', 'tmax'], kind='line') plt.show()
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 datetime import datetime
import matplotlib.pyplot as plt
from meteostat import Stations, Daily
# Get weather stations by WMO ID
stations = Stations()
stations = stations.id('meteostat', ('D1424', '10729', '10803', '10513'))
stations = stations.fetch()
# Get names of weather stations
names = stations['name'].to_list()
# Time period
start = datetime(2000, 1, 1)
end = datetime(2019, 12, 31)
# Get daily data
data = Daily(stations, start, end)
data = data.aggregate(freq='1Y').fetch()
# Plot chart
fig, ax = plt.subplots(figsize=(8, 6))
data.unstack('station')['tmax'].plot(legend=True,
def get_nearest_station(latitude: float, longitude: float) -> str:
# Get closest weather station to the match location
stations = Stations().nearby(latitude, longitude)
station = stations.fetch(1)
LOGGER.info("Using nearest station: %s" % station)
return station.index.array[0]
