def forecast(request):
if not 'city' in request.GET:
return JsonResponse({'message': 'Parameter "city" is required'}, status=400)
if not 'days' in request.GET:
return JsonResponse({'message': 'Parameter "days" is required'}, status=400)
try:
days = int(request.GET['days'])
except ValueError:
return JsonResponse({'message': 'Parameter "days" must be between 1 and 10'}, status=400)
if not 0 < days <= 10:
return JsonResponse({'message': 'Parameter "days" must be between 1 and 10'}, status=400)
# Fetch (lat, lon) for given city.
try:
city = City.objects.get(name=request.GET['city'])
except City.MultipleObjectsReturned:
# XXX: Unfortunately the data we're using contains what appears to be
# duplicates. Use the first result as a workaround for now.
city = City.objects.filter(name=request.GET['city']).first()
except City.DoesNotExist:
return JsonResponse(
{'message': 'City "{}" could not be found'.format(request.GET['city'])}, status=400)
# Get weather for the given city.
city = Place(city.name, city.lat, city.lon)
forecast = Forecast(city,
'metno-locationforecast/1.0 https://github.com/Rory-Sullivan/metno-locationforecast')
forecast.update()
# Collect temperature and humidity values for calculations and limit data
# to the number of days given in the request.
t, h = [], []
for interval in forecast.data.intervals:
if interval.end_time <= datetime.datetime.now() + datetime.timedelta(days=days):
t.append(interval.variables['air_temperature'].value)
h.append(interval.variables['relative_humidity'].value)
temperature_min = min(t)
temperature_max = max(t)
temperature_average = statistics.mean(t)
temperature_median = statistics.median(t)
humidity_min = min(h)
humidity_max = max(h)
humidity_average = statistics.mean(h)
humidity_median = statistics.median(h)
return JsonResponse({
'temperature_min': temperature_min,
'temperature_max': temperature_max,
'temperature_average': round(temperature_average, 2),
'temperature_median': round(temperature_median, 2),
'humidity_min': humidity_min,
'humidity_max': humidity_max,
'humidity_average': round(humidity_average, 2),
'humidity_median': round(humidity_median, 2),
})
def __init__(self, place_name, lat, long, altitude=0):
self.place_name = place_name
self.lat = lat
self.long = long
self.altitude = altitude
self.home = Place(place_name, self.lat, self.long, self.altitude)
self.forecast = Forecast(
home, "github.com/FutureSharks/rpi-weather-display 1.0")
self.forecast.update()
def fetch_weather(lat, lon, address=""):
''' Get forecast data using metno-locationforecast'''
location = Place(address, lat, lon)
forecast = Forecast(location, user_agent=user_agent)
updated = forecast.update()
if forecast.json['status_code'] != 200:
logger.error('Forecast response: %s', forecast.json['status_code'])
return forecast, updated
class metNorway(object):
"""
An interface to the Norwegian Meteorological Institute API
Example:
home = Place("Berlin", 52.52437, 13.41053, 34)
forecast = Forecast(home, "github.com/FutureSharks/rpi-weather-display 1.0")
forecast.update()
"""
def __init__(self, place_name, lat, long, altitude=0):
self.place_name = place_name
self.lat = lat
self.long = long
self.altitude = altitude
self.home = Place(place_name, self.lat, self.long, self.altitude)
self.forecast = Forecast(
home, "github.com/FutureSharks/rpi-weather-display 1.0")
self.forecast.update()
def get_hourly_data(self, hours=24):
"""
Returns a list of hourly rain and temperature values
"""
self.forecast.update()
results = []
for hour in forecast.data.intervals[0:hours]:
h = {
"date":
hour.start_time,
"air_temperature":
hour.variables["air_temperature"].value,
"precipitation_amount":
hour.variables["precipitation_amount"].value,
}
results.append(h)
return results
def get_weather_data(self,
county='Dublin',
latitude=53.33,
longitude=-6.24,
altitude=10):
self.dublin = Place(county, latitude, longitude, altitude)
self.forecast = Forecast(self.dublin, self.USER_AGENT, 'complete')
self.forecast.update()
df = pd.DataFrame()
for interval in self.forecast.data.intervals:
row = pd.DataFrame.from_dict(interval.variables, orient='index')
row['start_time'] = interval.start_time
row['end_time'] = interval.end_time
df = df.append(row)
df.columns = ['weather_feeds', 'start_time', 'end_time']
df['weather_feeds'] = df['weather_feeds'].apply(str)
df[['header', 'value']] = df.weather_feeds.str.split(expand=True)
df['header'] = df['header'].str.replace(':', '')
df2 = df.pivot(index='start_time', columns='header', values='value')
self.df2 = df2
def værdata():
USER_AGENT = "metno_locationforecast/1.0 [email protected]"
Trondheim = Place("Trondheim", 63.42024, 10.40122)
Trondheim_forecast = Forecast(Trondheim, USER_AGENT, "complete")
Trondheim_forecast.update()
#print(Trondheim_forecast)
First_interval = Trondheim_forecast.data.intervals[0]
Trondheim_forecast.update()
tomorrow = dt.date.today() - dt.timedelta(days=30)
print(dt.date.today())
print(dt.timedelta(days=30))
print(tomorrow)
tomorrow_intervals = Trondheim_forecast.data.intervals_for(tomorrow)
print(tomorrow_intervals)
print("Forecast for tomorrow in Trondheim:\n")
for interval in tomorrow_intervals:
print(interval)
#print(First_interval)
#print(f"Duration: {First_interval.duration}")
rain = First_interval.variables["precipitation_amount"]
#print(rain)
#print(f"Rain values: {rain.value}")
#print(f"Rain units: {rain.units}")
print(First_interval.variables.keys())
"""Basic usage."""
from metno_locationforecast import Place, Forecast
# Add your own user agent here.
USER_AGENT = "metno_locationforecast/1.0 https://github.com/Rory-Sullivan/yrlocationforecast"
# Create a Place instance.
# Place(name, latitude, longitude, altitude)
new_york = Place("New York", 40.7, -74.0, 10)
# Create a Forecast instance for the place.
# Forecast(place, forecast_type, user_agent, save_location="./data/")
new_york_forecast = Forecast(new_york, USER_AGENT)
# Update the forecast. This requests data from the MET API and saves data to the
# save location.
new_york_forecast.update()
print(new_york_forecast)
def Future_forecast():
USER_AGENT = "metno_locationforecast/1.0 [email protected]"
Trondheim = Place("Trondheim", 63.42024, 10.40122)
Trondheim_forecast = Forecast(Trondheim, USER_AGENT)
Trondheim_forecast.update()
return (Trondheim_forecast)
suntime = (sunset-sunrise)
print(suntime, "timer er solen oppe i løpet av en hel dag")
sun_mid = (suntime/2)
suntop = sun_mid + sunrise
suntop = suntop.strftime("%Y-%m-%d %H:%M:%S")
print("Solen er på sitt høyeste", suntop)
"""
Henter værdata fra metno!
"""
user_agent = "metno_locationforecast/1.0 [email protected]"
ntnu = Place("NTNU", 63.418, 10.406, 50)
ntnu_forecast = Forecast(ntnu, user_agent)
ntnu_forecast.update()
interval = ntnu_forecast.data.intervals[1]
clod_frac = interval.variables["cloud_area_fraction"]
clod_frac = clod_frac.value
print(clod_frac, "% skyer er det nå ")
"""
150 kvm med tak. Heller 20 grader nord --> sør. Beregn oppladning av dette ved utnyttelse av sola.
Med 75 kvm takflate beregner man 44 solcellepaneler. Effekt i kW er 13 420. Estimert årsproduksjon er 10 736 kWh.
Premium svart 305 W panel. Ganger dette med 2 fordi det er 150 kvm tilgjengelig takoverflate.
Setter forskjellige konstanter avhengig av hvor mye solenergi som produserers med x antall timer +/- når solen er på sitt høyeste.
Dette beregnes utifra skyprosenten.
"""
class WeatherDataFeed:
'''
Loading Current Weather Data from Met.no API
Parameters
----------
links : Locations
symbol - Symbol of the Stock to extract the data
Returns
-------
Integer
DataFrame
'''
def __init__(self):
self.USER_AGENT = 'metno_locationforecast/1.0'
self.check_extention = ['.csv']
self.sourceFolder = '/path/to/your/repo/weather_feeds/src'
def get_weather_data(self,
county='Dublin',
latitude=53.33,
longitude=-6.24,
altitude=10):
self.dublin = Place(county, latitude, longitude, altitude)
self.forecast = Forecast(self.dublin, self.USER_AGENT, 'complete')
self.forecast.update()
df = pd.DataFrame()
for interval in self.forecast.data.intervals:
row = pd.DataFrame.from_dict(interval.variables, orient='index')
row['start_time'] = interval.start_time
row['end_time'] = interval.end_time
df = df.append(row)
df.columns = ['weather_feeds', 'start_time', 'end_time']
df['weather_feeds'] = df['weather_feeds'].apply(str)
df[['header', 'value']] = df.weather_feeds.str.split(expand=True)
df['header'] = df['header'].str.replace(':', '')
df2 = df.pivot(index='start_time', columns='header', values='value')
self.df2 = df2
def save_weather_data(self, file_name='default.csv'):
weather_data = self.df2
weather_data['Date_Time'] = weather_data.index
weather_data = weather_data[[
'Date_Time', 'air_pressure_at_sea_level', 'air_temperature',
'air_temperature_max', 'air_temperature_min',
'cloud_area_fraction', 'cloud_area_fraction_high',
'cloud_area_fraction_low', 'cloud_area_fraction_medium',
'dew_point_temperature', 'fog_area_fraction',
'precipitation_amount', 'relative_humidity',
'ultraviolet_index_clear_sky', 'wind_from_direction', 'wind_speed'
]]
# Run First time like this to include headers and second time you can use append mode and not include headers
# weather_data.to_csv(os.path.join(os.getcwd(),file_name), index = False)
weather_data.to_csv(os.path.join(os.getcwd(), file_name),
mode='a',
header=False,
index=False)
self.file_name = file_name
def clean_up_weather_data(self):
download_path = ''
for filename in os.listdir(self.sourceFolder):
if any(ext in filename for ext in self.check_extention):
if filename == self.file_name:
print(filename)
download_path = (os.path.join(self.sourceFolder, filename))
data = pd.read_csv(download_path)
df = pd.DataFrame(data)
# Sort the values by Date Time
df = df.sort_values('Date_Time', ascending=True)
# De-duping the rows based on most up to date value
df = df.drop_duplicates(subset='Date_Time', keep='last')
df.to_csv(download_path, index=False)
else:
pass
else:
pass
"""Using a custom save location.
Note there is now support for home directory ('~/') and relative notation ('../').
"""
import os
from metno_locationforecast import Place, Forecast
# Add your own user agent here.
USER_AGENT = "metno_locationforecast/1.0 https://github.com/Rory-Sullivan/yrlocationforecast"
new_york = Place("New York", 40.7, -74.0, 10)
# Provide a custom save location
new_york_forecast = Forecast(new_york, USER_AGENT, save_location="~/forecast_data/")
# Here we can view the actual save location
save_location = str(new_york_forecast.save_location)
print(save_location)
# Update the forecast. This requests data from the MET API and saves data to the
# save location.
new_york_forecast.update()
print(os.listdir(save_location))
from metno_locationforecast import Place, Forecast
import datetime as dt
new_york = Place("Bergen/Norway", 60.3, 5.3)
ny_forecast = Forecast(new_york, forecast_type="complete", user_agent="fingr/1.0 https://graph.no")
ny_forecast.update()
# print(ny_forecast)
# variables ['air_pressure_at_sea_level', 'air_temperature',
# 'cloud_area_fraction', 'relative_humidity', 'wind_from_direction',
# 'wind_speed', 'precipitation_amount'])
first_interval = ny_forecast.data.intervals[0]
print(first_interval)
# Access the interval's duration attribute.
print(f"Duration: {first_interval.duration} - {first_interval.symbol_code}")
print() # Blank line
# Access a particular variable from the interval.
rain = first_interval.variables["precipitation_amount"]
print(rain)
# Access the variables value and unit attributes.
print(f"Rain value: {rain.value}")
print(f"Rain units: {rain.units}")
# Get a full list of variables available in the interval.
print(first_interval.variables.keys())
"""An example of accessing individual forecast variables."""
from metno_locationforecast import Place, Forecast
USER_AGENT = "metno_locationforecast/1.0 https://github.com/Rory-Sullivan/yrlocationforecast"
new_york = Place("New York", 40.7, -74.0, 10)
new_york_forecast = Forecast(new_york, USER_AGENT, "complete")
new_york_forecast.update()
# Access a particular interval.
first_interval = new_york_forecast.data.intervals[0]
print(first_interval)
# Access the interval's duration attribute.
print(f"Duration: {first_interval.duration}")
print() # Blank line
# Access a particular variable from the interval.
rain = first_interval.variables["precipitation_amount"]
print(rain)
# Access the variables value and unit attributes.
print(f"Rain value: {rain.value}")
print(f"Rain units: {rain.units}")
# Get a full list of variables available in the interval.
print(first_interval.variables.keys())
"""Get forecast for a particular day."""
from metno_locationforecast import Place, Forecast
import datetime as dt
USER_AGENT = "metno_locationforecast/1.0 https://github.com/Rory-Sullivan/yrlocationforecast"
beijing = Place("Beijing", 39.9, 116.4)
beijing_forecast = Forecast(beijing, USER_AGENT)
beijing_forecast.update()
# Create a datetime.date object for the day in question.
tomorrow = dt.date.today() + dt.timedelta(days=1)
# Get intervals for that date.
tomorrows_intervals = beijing_forecast.data.intervals_for(tomorrow)
# Iterate through each of the returned intervals and print it.
print("Forecast for tomorrow in Beijing:\n")
for interval in tomorrows_intervals:
print(interval)
"""How to change the units of certain variables."""
from metno_locationforecast import Place, Forecast
USER_AGENT = "metno_locationforecast/1.0 https://github.com/Rory-Sullivan/yrlocationforecast"
london = Place("London", 51.5, -0.1, 25)
london_forecast = Forecast(london, USER_AGENT)
london_forecast.update()
# Iterate over all variables of every interval.
for interval in london_forecast.data.intervals:
for variable in interval.variables.values():
# Change temperature units to Fahrenheit
if variable.name == "air_temperature":
variable.convert_to("fahrenheit")
# Change wind speed units to miles per hour.
elif variable.name == "wind_speed":
variable.convert_to("mph")
# Print forecast, notice the new variables.
print(london_forecast)
