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 get_weather(station: str, start: datetime, end: datetime) -> dict:
# Get hourly data
hourly_obs = Hourly(station, start, end)
df = hourly_obs.fetch()
return {
"rain": df["prcp"].fillna(0).sum(),
"temperature": df["temp"].mean(),
"wind_speed": df["wspd"].mean(),
}
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'
)
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 get_weather(road):
#url='http://pro.openweathermap.org/data/2.5/weather?q=Birmingham,uk&APPID=b68960a402d40a497b69695725ad73af'
# Create Point for Birmingham
#Birmingham = Point(52.49, -1.86, 140)
#Bradford = Point(53.79, -1.75, 168.78)
#Leeds= Point(53.801277, -1.548567, 340)
#print('City : ',City)
month = datetime.today().month
day = datetime.today().day
year = datetime.today().year
hour = datetime.today().hour
#print(year, month, day, hour, City)
start = datetime(year, month, day, hour, 0)
end = datetime(year, month, day, hour, 59)
#print(hour)
if road == 'M606':
#City = Point(53.79, -1.75, 168.78)
data = Hourly(Point(53.79, -1.75, 168.78), start, end)
#City='Bradford'
elif road == 'M621':
#City='Leeds'
City = Point(53.801277, -1.548567, 340)
data = Hourly(Point(53.801277, -1.548567, 340), start, end)
elif road == 'A38(M)':
#City='Birmingham'
City = Point(52.49, -1.86, 140)
data = Hourly(Point(52.49, -1.86, 140), start, end)
#data = Hourly(City, start, end)
weather_df = data.fetch()
print(start)
print(weather_df)
coco = list(weather_df['coco'])
#print(coco[0])
#str1=str(round(coco[0]))
#print(str1)
#print(weather[coco])
weather = {
'1': 'Clear',
'2': 'Fair',
'3': 'Cloudy',
'4': 'Overcast',
'5': 'Fog',
'6': 'Freezing Fog',
'7': 'Light Rain',
'8': 'Rain',
'9': 'Heavy Rain',
'10': 'Freezing Rain',
'11': 'Heavy Freezing Rain',
'12': 'Sleet',
'13': 'Heavy Sleet',
'14': 'Light Snowfall',
'15': 'Snowfall',
'16': 'Heavy Snowfall',
'17': 'Rain Shower',
'18': 'Heavy Rain Shower',
'19': 'Sleet Shower',
'20': 'Heavy Sleet Shower',
'21': 'Snow Shower',
'22': 'Heavy Snow Shower',
'23': 'Lightning',
'24': 'Hail',
'25': 'Thunderstorm',
'26': 'Heavy Thunderstorm',
'27': 'Storm'
}
weather_df = weather_df.reset_index()
#print(weather[str1])
return coco
under the terms of the Creative Commons Attribution-NonCommercial
4.0 International Public License.
The code is licensed under the MIT license.
"""
if __name__ == '__main__':
from timeit import default_timer as timer
# Get start time
s = timer()
# Run script
from datetime import datetime
from meteostat import Hourly
Hourly.cores = 12
start = datetime(1960, 1, 1)
end = datetime(2021, 1, 1, 23, 59)
data = Hourly('10637', start, end, timezone='Europe/Berlin')
data = data.fetch()
# Get end time
e = timer()
# Print performance
print(e - s)
""" Example: Hourly 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 from meteostat import Point, Hourly # Time period start = datetime(2021, 1, 1) end = datetime(2021, 1, 1, 23, 59) # The point point = Point(50.3167, 8.5, 320) # Get hourly data data = Hourly(point, start, end, timezone='Europe/Berlin') # Print to console data = data.fetch() print(data)
"""
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()
""" 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 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: 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)
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())
"""
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 Hourly
# Time period
start = datetime(2018, 1, 1)
end = datetime(2018, 1, 1, 23, 59)
# Get hourly data & aggregate daily
data = Hourly('10637', start, end)
data = data.aggregate('1D')
data = data.fetch()
# Print
print(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
from meteostat import Stations, Hourly
from meteostat.units import fahrenheit, direction, condition
# Time period
start = datetime(2018, 1, 1)
end = datetime(2018, 1, 1, 23, 59)
# Get nearby weather station
stations = Stations()
stations = stations.nearby(50, 8)
station = stations.fetch(1)
# Get hourly data
data = Hourly(station, start, end, timezone='Europe/Berlin')
# Convert data units
data = data.convert({'temp': fahrenheit, 'wdir': direction, 'coco': condition})
# Print to console
data = data.fetch()
print(data)
""" 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()
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)
from meteostat import Stations, Hourly from datetime import datetime # Hourly stations = Stations(lat = 50, lon = 8) station = stations.fetch(1) data = Hourly(station, start = datetime(2020, 1, 1), end = datetime(2020, 1, 1, 23, 59)) print(data.fetch())
from meteostat import Stations, Hourly
from datetime import datetime
import matplotlib.pyplot as plt
# Hourly
station = ['10637']
data = Hourly(station,
start=datetime(2020, 8, 1),
end=datetime(2020, 8, 4, 23, 59))
data = data.normalize()
data = data.interpolate().fetch()
data.plot(x='time', y=['temp'], kind='line')
plt.show()
