def test_coverage(self):
# Get 2018 daily data for Frankfurt Airport
data = Daily(['10637'], start = datetime(2018, 1, 1), end = datetime(2018, 12, 31))
coverage = data.normalize().coverage()
# Check if coverage is 100%
self.assertEqual(
coverage,
1,
'Normalized daily data returns coverage of ' + str(coverage) + ', should be 1'
)
def test_aggregate(self):
# Get 2018 daily data for Frankfurt Airport
data = Daily(['10637'], start = datetime(2018, 1, 1), end = datetime(2018, 12, 31))
count = data.normalize().aggregate(freq = '1W').count()
# Check if count matches 53
self.assertEqual(
count,
53,
'Aggregated daily data returns count of ' + str(count) + ', should be 53'
)
def test_normalize(self):
# Get 2018 daily data for Frankfurt Airport
data = Daily(['10637'], start = datetime(2018, 1, 1), end = datetime(2018, 12, 31))
count = data.normalize().count()
# Check if count matches 365
self.assertEqual(
count,
365,
'Normalized daily data returns count of ' + str(count) + ', should be 365'
)
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 get_historical_data(self):
"""
returns: Dataframe of historical average temperature for a given city
within the specified time period
"""
lat, lon = self.get_lat_and_lon()
city = Point(lat, lon)
data = Daily(city, self.start_date, self.end_date)
data = data.fetch()
data = data.filter(['tavg']) # select average temperature column
data = data.fillna(method='ffill') # fill missing values
# get a specific day
#day = data.iloc[lambda x: x.index == '2014-06-23']
return data
# General configuration
Daily.max_age = 0
STATIONS_PER_CYCLE = 10
# Create Jasper instance
jsp = Jasper("task.inventory.daily")
# Get stations
stations = get_stations(jsp, "SELECT `id` FROM `stations`", STATIONS_PER_CYCLE)
if len(stations) > 0:
for station in stations:
try:
# Get daily data from Meteostat
data = Daily(station[0], model=False).fetch()
# Get start & end dates of time series
start = data.index.min().strftime("%Y-%m-%d")
end = data.index.max().strftime("%Y-%m-%d")
if len(start) == 10 and len(end) == 10:
jsp.query(f"""
INSERT INTO `inventory`(
`station`,
`mode`,
`start`,
`end`
) VALUES (
"{station[0]}",
""" Meteostat JSON API Server The code is licensed under the MIT license. """ from meteostat import Hourly, Daily, Monthly, Normals # Change cache directory to Apache user cache_dir = '/var/www/.meteostat/cache' Hourly.cache_dir = cache_dir Daily.cache_dir = cache_dir Monthly.cache_dir = cache_dir Normals.cache_dir = cache_dir # Max. cache time max_cache_time = 60 * 60 * 24 * 30 # Clear hourly cache Hourly.clear_cache(max_cache_time) # Clear daily cache Daily.clear_cache(max_cache_time) # Clear monthly cache Monthly.clear_cache(max_cache_time) # Clear normals cache Normals.clear_cache(max_cache_time)
from datetime import datetime
from meteostat import Point, Daily
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
start = datetime(2021, 1, 1)
end = datetime(2021, 12, 31)
oslo = Point(59.9139, 10.7522)
od = Daily(oslo, start, end)
od = od.fetch()
print(od.isnull().sum())
df = od.drop(['snow', 'wdir', 'wspd', 'wpgt', 'pres', 'tsun'], axis=1)
df = df.dropna()
print(df)
print(df.isna().sum())
corr = df.corr()
print(corr)
sns.heatmap(corr, cmap="Blues", annot=True)
if not od.empty:
exit()
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()
"""
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
"""
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
import matplotlib.pyplot as plt
from meteostat import Daily
# Time period
start = datetime(2018, 1, 1)
end = datetime(2018, 12, 31)
# Get daily data
data = Daily('10637', start, end)
# Group & aggregate weekly
data = data.normalize().aggregate(freq='1W').fetch()
# Plot chart
data.plot(y=['tavg', 'tmin', 'tmax'])
plt.show()
def get_raw_data(station_id, start=start, end=end):
data_daily = Daily([station_id], start=start, end=end)
return data_daily.fetch()
start = datetime(2021, 1, 1)
end = datetime(2021, 12, 31)
container = []
retrived = []
retrivedIndex = []
notRetrived = []
notRetrivedIndex = []
for k, val in enumerate(uniqueLocations):
stations = Stations()
stations = stations.nearby(val[0], val[1])
station = stations.fetch(1)
data = Daily(station, start, end)
data = data.fetch()
if len(data) > 0:
data['week'] = data.index.isocalendar().week
ndst = data.groupby('week').mean()
localdf = ndst[['tavg', 'prcp', 'pres']]
disc = localdf.isna().sum().sum()
if disc > 40:
notRetrived.append(val)
notRetrivedIndex.append(k)
print('Wait')
time.sleep(4)
print('Go')
"""
How the Atlantic causes mild winters in Europe
"""
from meteostat import Stations, Daily
from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
# Get weather stations by Meteostat ID
stations = Stations(id=['D1424', '10729', '10803', '10513']).fetch()
# Get daily data since 1980
data = Daily(stations,
max_threads=5,
start=datetime(1980, 1, 1),
end=datetime(2019, 12, 31))
# Normalize data
data = data.normalize().fetch()
# Function for converting degrees to Boolean (North-East or Not-North-East)
def direction(value):
if (value >= 337 and value <= 360) or value <= 114:
return 1
else:
return 0
# Convert wind direction
def stations_daily():
"""
Return daily station data in JSON format
"""
# Get query parameters
args = utils.get_parameters(parameters)
# Check if required parameters are set
if args['station'] and len(args['start']) == 10 and len(args['end']) == 10:
try:
# Convert start & end date strings to datetime
start = datetime.strptime(args['start'], '%Y-%m-%d')
end = datetime.strptime(f'{args["end"]} 23:59:59',
'%Y-%m-%d %H:%M:%S')
# Get number of days between start and end date
date_diff = (end - start).days
# Check date range
if date_diff < 0 or date_diff > max_days:
# Bad request
abort(400)
# Caching
now_diff = (datetime.now() - end).days
if now_diff < 30:
cache_time = 60 * 60 * 24
else:
cache_time = 60 * 60 * 24 * 3
Daily.max_age = cache_time
# Get data
data = Daily(args['station'], start, end, model=args['model'])
# Check if any data
if data.count() > 0:
# Normalize data
data = data.normalize()
# Aggregate
if args['freq']:
data = data.aggregate(args['freq'])
# Unit conversion
if args['units'] == 'imperial':
data = data.convert(units.imperial)
elif args['units'] == 'scientific':
data = data.convert(units.scientific)
# Fetch DataFrame
data = data.fetch()
# Convert to integer
data['tsun'] = data['tsun'].astype('Int64')
# DateTime Index to String
data.index = data.index.strftime('%Y-%m-%d')
data.index.rename('date', inplace=True)
data = data.reset_index().to_json(orient="records")
else:
# No data
data = '[]'
# Inject meta data
meta = {}
meta['generated'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
# Generate output string
output = f'''{{"meta":{json.dumps(meta)},"data":{data}}}'''
# Return
return utils.send_response(output, cache_time)
except BaseException:
# Bad request
abort(400)
else:
# Bad request
abort(400)
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,
ax=ax,
style='.-',
ylabel='Max. Annual Temperature (°C)',
title='Max. Temperature Report')
plt.legend(names)
plt.show()
from datetime import datetime
import pandas as pd
from meteostat import Point, Daily
start = datetime(2018, 1, 1)
end = datetime(2021, 3, 8)
YT = Point(37.251494, 127.071288)
data = Daily(YT, start, end)
data = data.fetch()
df = pd.DataFrame(data)
df.to_csv('weather.csv', index=True)
test = 1
# Mehrere Threads für schnellere Downloads
Daily.max_threads = 6
# Zeitliche Periode
start = datetime(2019, 12, 1)
end = datetime(2020, 11, 30)
# 50 Zufällige Wetterstationen in Deutschland
stations = Stations()
stations = stations.region('DE')
stations = stations.inventory('daily', (start, end))
stations = stations.fetch(limit=50, sample=True)
# Tageswerte laden
weather = Daily(stations, start, end)
# Daten monatlich aggregieren
weather = weather.aggregate('1MS', spatial=True).fetch()
# Titel des Diagramms
TITLE = 'Interesse an Klimaanlagen & Max. Temperaturen in Deutschland'
# Darstellung der Seitenaufrufe
ax = pageviews['Klimaanlage'].plot(color='tab:blue', title=TITLE)
ax.set_ylabel('Seitenaufrufe', color='tab:blue')
# Darstellung der Temperaturspitzen
ax2 = ax.twinx()
ax2.set_ylabel('Max. Temperatur (°C)', color='tab:red')
weather['tmax'].plot(ax=ax2, color='tab:red')
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
data.unstack('station')['tavg'].plot(
legend=True,
ylabel='Avg. Daily Temperature °C',
title='Average Temperature Report for 2019')
plt.legend(names)
plt.show()
"""
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()
""" Example: Daily point data access 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 Point, Daily # Set time period start = datetime(2018, 1, 1) end = datetime(2018, 12, 31) # Create Point for Vancouver, BC vancouver = Point(49.2497, -123.1193, 70) # Get daily data for 2018 data = Daily(vancouver, start, end) data = data.fetch() # Plot line chart including average, minimum and maximum temperature data.plot(y=['tavg', 'tmin', 'tmax']) 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()
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: 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 Daily
# Time period
start = datetime(2019, 1, 1)
end = datetime(2019, 12, 31)
# Get daily data
data = Daily(['71624', '72295', '68816', '94767'], start, end)
data = data.fetch()
# Plot chart
data.unstack('station')['tavg'].plot(
legend=True,
ylabel='Avg. Daily Temperature °C',
title='Average Temperature Report for 2019')
plt.show()
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()
from meteostat import Stations, Daily from datetime import datetime import matplotlib.pyplot as plt data = Daily(['10637'], start = datetime(2018, 1, 1), end = datetime(2018, 12, 31)) data = data.normalize().aggregate(freq = '1W').fetch() data.plot(x = 'time', y = ['tavg', 'tmin', 'tmax'], kind = 'line') plt.show()
from datetime import datetime
import json
import matplotlib.pyplot as plt
from meteostat import Stations, Daily
stations = Stations(lat=53.9, lon=27.5667, daily=datetime(2019, 1, 1))
#data = Daily(station, start = datetime(2018, 1, 1), end = datetime(2018, 12, 31))
station = stations.fetch(1)
data = Daily(station, start=datetime(2019, 11, 1), end=datetime(2020, 11, 30))
data = data.fetch()
print(data)
data.plot(kind='scatter', x='snow', y='tmin')
plt.show()
#data.to_csv('wlastyear.csv', encoding='utf-8', date_format='%Y/%m/%d',na_rep='NULL')