# 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')
# Ausgabe des Diagramms
plt.show()
"""
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
plt.show()
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)
