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 test_coverage(self):
# Get data for some day at Frankfurt Airport
data = Hourly(['10637'], start = datetime(2018, 1, 1), end = datetime(2018, 1, 1, 23, 59))
coverage = data.normalize().coverage()
# Check if coverage is 100%
self.assertEqual(
coverage,
1,
'Normalized hourly data returns coverage of ' + str(coverage) + ', should be 1'
)
def test_aggregate(self):
# Get data for some days at Frankfurt Airport
data = Hourly(['10637'], start = datetime(2018, 1, 1), end = datetime(2018, 1, 3, 23, 59))
count = data.normalize().aggregate(freq = '1D').count()
# Check if count matches 3
self.assertEqual(
count,
3,
'Aggregated hourly data returns count of ' + str(count) + ', should be 3'
)
def test_normalize(self):
# Get data for some day at Frankfurt Airport
data = Hourly(['10637'], start = datetime(2018, 1, 1), end = datetime(2018, 1, 1, 23, 59))
count = data.normalize().count()
# Check if count matches 24
self.assertEqual(
count,
24,
'Normalized hourly data returns count of ' + str(count) + ', should be 24'
)
"""
Example: Interpolation
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 Hourly
# Time period
start = datetime(2018, 8, 1)
end = datetime(2018, 8, 4, 23, 59)
# Get hourly data
data = Hourly('10730', start, end)
data = data.normalize()
data = data.interpolate(6)
data = data.fetch()
# Plot chart
data.plot(y='temp')
plt.show()
def point_hourly():
"""
Return hourly point data in JSON format
"""
# Get query parameters
args = utils.get_parameters(parameters)
# Check if required parameters are set
if args['lat'] and args['lon'] 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 < 3:
cache_time = 60 * 60
elif now_diff < 30:
cache_time = 60 * 60 * 24
else:
cache_time = 60 * 60 * 24 * 3
Hourly.max_age = cache_time
# Create a point
location = Point(args['lat'], args['lon'], args['alt'])
# Get data
data = Hourly(location,
start,
end,
timezone=args['tz'],
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')
data['coco'] = data['coco'].astype('Int64')
# DateTime Index to String
data.index = data.index.strftime('%Y-%m-%d %H:%M:%S')
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')
meta['stations'] = location.stations.to_list()
# 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)