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
def test_area(self):
# Select weather stations in southern hemisphere
station = Stations(bounds = [0, -180, -90, 180]).fetch(1).to_dict('records')[0]
# Check if -90 <= latitude <= 0
self.assertTrue(
-90 <= station['latitude'] <= 0,
'Weather station is not in latitude range'
)
def test_nearby(self):
# Selecting closest weather station to Frankfurt Airport
station = Stations(lat = 50.05, lon = 8.6).fetch(1).to_dict('records')[0]
# Check if country code matches Germany
self.assertEqual(
station['country'],
'DE',
'Closest weather stations returns country code ' + station['country'] + ', should be DE'
)
def test_identifier(self):
# Select weather station 'Toronto Pearson Airport'
station = Stations(id = '71624').fetch(1).to_dict('records')[0]
# Check if ICAO ID matches CYYZ
self.assertEqual(
station['icao'],
'CYYZ',
'Weather station returns ICAO ID ' + station['icao'] + ', should be CYYZ'
)
def test_area(self):
"""
Test: Stations by geographical area
"""
# Select weather stations in southern hemisphere
station = Stations().bounds((0, -180),
(-90, 180)).fetch(1).to_dict('records')[0]
# Check if -90 <= latitude <= 0
self.assertTrue(-90 <= station['latitude'] <= 0,
'Weather station is not in latitude range')
def test_regional(self):
"""
Test: Stations by country/region code
"""
# Select a weather station in Ontario, Canada
station = Stations().region('CA', 'ON').fetch(1).to_dict('records')[0]
# Check if country code matches Canada
self.assertEqual(
station['country'], 'CA', 'Weather station returns country code ' +
station['country'] + ', should be CA')
# Check if region code matches Ontario
self.assertEqual(
station['region'], 'ON', 'Weather station returns province code ' +
station['region'] + ', should be ON')
"""
The hottest places in Germany
"""
from meteostat import Stations, Daily
from datetime import datetime
import matplotlib.pyplot as plt
# Get weather stations by Meteostat ID
stations = Stations(id=['D1424', '10729', '10803', '10513']).fetch()
# Get names of weather stations
names = stations['name'].to_list()
# Get daily data since 2000
data = Daily(stations, start=datetime(2000, 1, 1), end=datetime(2019, 12, 31))
# Aggregate annually
data = data.aggregate(freq='1Y').fetch()
# Plot data
fig, ax = plt.subplots(figsize=(8, 6))
data.unstack('station')['tmax'].plot(kind='line',
legend=True,
ax=ax,
style='.-',
ylabel='Max. Annual Temperature (°C)',
title='Max. Temperature Report')
plt.legend(names)
# Show plot
"""
Spatial sampling with Meteostat
"""
from meteostat import Stations, Daily
from datetime import datetime
import matplotlib.pyplot as plt
# Get 20 random weather stations in Germany
stations = Stations(country='DE', daily=datetime(2005, 1,
1)).fetch(limit=20,
sample=True)
# Get daily data
data = Daily(stations,
max_threads=5,
start=datetime(1980, 1, 1),
end=datetime(2019, 12, 31))
# Normalize data and aggregate
data = data.normalize().aggregate(freq='5Y', spatial=True).fetch()
# Plot chart
data.plot(y=['tavg'],
kind='line',
title='Sampled DE Annual Mean Temperature from 1980 to 2019')
plt.show()
from meteostat import Stations
# Get number of stations in northern hemisphere
stations = Stations(bounds=[90, -180, 0, 180])
print('Stations in northern hemisphere:')
print(stations.count())
# Get number of stations in southern hemisphere
stations = Stations(bounds=[0, -180, -90, 180])
print('Stations in southern hemisphere:')
print(stations.count())
"""
Example: Aggregation
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
from meteostat import Stations, Hourly
# Hourly
stations = Stations()
stations = stations.id('wmo', '10637')
station = stations.fetch()
# Time period
start = datetime(2018, 1, 1)
end = datetime(2018, 1, 1, 23, 59)
# Get hourly data & aggregate
data = Hourly(station, start, end)
data = data.aggregate('1D')
data = data.fetch()
# Print
print(data)
"""
Example: Comparing multiple weather stations
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('wmo', ('71624', '72295', '68816', '94767'))
stations = stations.fetch()
# Get names of weather stations
names = stations['name'].to_list()
# Time period
start = datetime(2019, 1, 1)
end = datetime(2019, 12, 31)
# Get daily data
data = Daily(stations, start, end)
data = data.fetch()
# Plot chart
The code is licensed under the MIT license.
"""
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)
Example: Simple chart
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
# Time period
start = datetime(2018, 1, 1)
end = datetime(2018, 12, 31)
# Get a weather station
stations = Stations()
stations = stations.nearby(49.2497, -123.1193)
stations = stations.inventory('daily', (start, end))
station = stations.fetch(1)
# Get daily data
data = Daily(station, start, end)
data = data.fetch()
# Plot chart
data.plot(y=['tavg', 'tmin', 'tmax'])
plt.show()
def get_station(lat, lon, cnt=10):
return Stations(lat=lat, lon=lon).fetch(
cnt) # get closest station to given coordinates
from meteostat import Stations, Hourly from datetime import datetime import matplotlib.pyplot as plt # Hourly stations = Stations(lat = 50, lon = 8) station = stations.fetch(1) data = Hourly(station, start = datetime(2010, 1, 1), end = datetime(2020, 1, 1, 23, 59)) data = data.fetch() data.plot(x = 'time', y = ['temp'], kind = 'line') plt.show()
"""
Example: Comparing aggregated data
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 Meteostat ID
stations = Stations()
stations = stations.fetch()
stations = stations[stations.index.isin(('D1424', '10729', '10803', '10513'))]
# 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)
# Aggregate annually
data = data.aggregate(freq='1Y').fetch()
"""
Example: Get weather stations by identifier
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 with ICAO ID EDDF
stations = Stations()
station = stations.id('icao', 'EDDF').fetch()
# Print station
print(station)
from meteostat import Stations
from datetime import datetime
# Closest weather station for position
stations = Stations(lat = 50, lon = 8, hourly = datetime(1940, 1, 1))
station = stations.fetch(1).to_dict('records')[0]
print('Closest weather station at coordinates 50, 8:')
print(station["name"])
from meteostat import Stations
# Get weather station with Meteostat ID 10637
stations = Stations(id='10637', icao='EDDF')
station = stations.fetch(1).to_dict('records')[0]
# Print name
print(station["name"])
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]
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()
"""
Example: Get weather stations by geographical area
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 all stations
stations = Stations()
# Get number of stations in northern hemisphere
northern = stations.bounds((90, -180), (0, 180))
print('Stations in northern hemisphere:', northern.count())
# Get number of stations in southern hemisphere
southern = stations.bounds((0, -180), (-90, 180))
print('Stations in southern hemisphere:', southern.count())
from meteostat import Stations, Hourly
from datetime import datetime
# Hourly
stations = Stations(wmo='10637')
station = stations.fetch(1)
data = Hourly(station,
start=datetime(2020, 1, 1),
end=datetime(2020, 1, 1, 23, 59)).aggregate(freq='1D')
print(data.fetch())
from meteostat import Stations, Daily
from datetime import datetime
import matplotlib.pyplot as plt
stations = Stations(country='US', daily=datetime(2005, 1, 1)).sample(5).fetch()
data = Daily(stations,
max_threads=5,
start=datetime(1980, 1, 1),
end=datetime(2019, 12, 31))
data = data.normalize().aggregate(freq='1Y', spatial=True).fetch()
data.plot(y=['tavg'],
kind='line',
title='Average US Annual Temperature from 1980 to 2019')
plt.show()
"""
Example: Comparing aggregated data
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
from meteostat import Stations, Daily
from datetime import datetime
import matplotlib.pyplot as plt
# Get weather stations by WMO ID
stations = Stations(wmo = ['71624', '72295', '68816', '94767']).fetch()
# Get names of weather stations
names = stations['name'].to_list()
# Get daily data for 2019
data = Daily(stations, start = datetime(2019, 1, 1), end = datetime(2019, 12, 31))
data = data.fetch()
# Plot data
ax = data.set_index('time').groupby(['station'])['tavg'].plot(kind = 'line', legend = True, ylabel = 'Avg. Daily Temperature °C', title = 'Average Temperature Report for 2019')
plt.legend(names)
# Show plot
plt.show()
"""
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"])
""" Example: Simple chart 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, Hourly # Get a weather station stations = Stations() stations = stations.nearby(50, 8) station = stations.fetch(1) # Time period start = datetime(2017, 1, 1) end = datetime(2017, 1, 1, 23, 59) # Get hourly data data = Hourly(station, start, end) data = data.fetch() # Plot chart data.plot(y='temp') plt.show()
