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'
)
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 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()
"""
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, 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
data['wdir'] = data['wdir'].apply(direction)
# Filter for DEC and JAN only
time = data.index.get_level_values('time')
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)
