def test_point(self):
"""
Test: Point Data
"""
# Create Point for Vancouver, BC
point = Point(49.2497, -123.1193, 70)
# Get count of weather stations
stations = point.get_stations('daily', datetime(2020, 1, 1),
datetime(2020, 1, 31))
# Check if three stations are returned
self.assertEqual(
len(stations.index), 4,
f'{len(stations.index)} weather stations returned, should be 4')
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
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
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()
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
""" 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)
def point_monthly():
"""
Return monthly 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:
# Bad request
abort(400)
# Caching
Monthly.max_age = 60 * 60 * 24 * 7
# Create a point
location = Point(args['lat'], args['lon'], args['alt'])
# Get data
data = Monthly(location, 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')
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)
""" 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 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