def test_Location_SolarDepression():
c = Location(("Heidelberg", "Germany", 49.412, -8.71, "Europe/Berlin"))
c.solar_depression = 'nautical'
assert c.solar_depression == 12
c.solar_depression = 18
assert c.solar_depression == 18
def CalcAstralDayTime(Date,Time,Latitude,Longitude):
"""
Calcule la position du soleil pour l'heure donnée.
:param Date: Date UTC
:param Time: Heure UTC
:param Latitude: Latitude
:param Longitude: Longitude
:return: D pour Day, U pour Dusk/crépuscule, N pour Night/Nuit, A pour Aube/Dawn
"""
from astral import Location
l = Location()
l.solar_depression= 'nautical'
l.latitude = Latitude
l.longitude = Longitude
s = l.sun(date=Date, local=False)
# print(Date,Time,Latitude,Longitude,s,)
Result = '?'
Inter=( {'d': 'sunrise', 'f': 'sunset' , 'r': 'D'}
, {'d': 'sunset' , 'f': 'dusk' , 'r': 'U'}
, {'d': 'dusk' , 'f': 'dawn' , 'r': 'N'}
, {'d': 'dawn' , 'f': 'sunrise', 'r': 'A'}
)
for I in Inter:
if s[I['d']].time()<s[I['f']].time() and (Time>=s[I['d']].time() and Time<=s[I['f']].time() ) :
Result=I['r']
elif s[I['d']].time() > s[I['f']].time() and (Time >= s[I['d']].time() or Time <= s[I['f']].time()):
Result = I['r'] # Changement de jour entre les 2 parties de l'intervalle
return Result
def get_sun_times(dt=datetime.datetime.now()):
loc = Location()
loc.name = 'Melbourne'
loc.region = 'Oceania'
loc.latitude = -37.787027
loc.longitude = 145.110013
loc.timezone = 'Australia/Melbourne'
loc.elevation = 75
loc.solar_depression = 'civil'
resp = {}
for k, v in loc.sun(dt).items():
resp[k] = arrow.get(v).timestamp
return resp
def calc_sunset_times(stops_df, latitude, longitude, timezone, date_col = 'date'):
"""
Calculates the sunset times for all unique dates in stops_df using the provided latitude and longitude using the given
timezone.
INPUTS
=======
stops_df: A pandas DataFrame that contains stops observations.
latitude: An object that can be converted to a float that represents the latitude
longitude: An object that can be converted to a float that represents the longitude
timezone: A string indicating the timezone to calculate the times in.
For a list of accepted arguments for timezone, use the follow code:
from pytz import all_timezones
for timezone in all_timezones:
print(timezone)
date_col: A string indicating the date column on stops_df. By default assumes it is 'date'.
RETURNS
========
A pandas DataFrame in which each row contains information about a date, with column 'sunset' representing sunset
time, 'dusk' representing dusk time, 'sunset_minutes' representing sunset time in minutes, and 'dusk_minutes' representing
dusk time in minutes.
"""
l = Location()
l.solar_depression = 'civil'
l.latitude = float(latitude)
l.longitude = float(longitude)
l.timezone = timezone
l.elevation = 0
unique_dates = list(stops_df[date_col].unique())
sunset = [l.sun(pd.Timestamp(date), local = True)['sunset'].time() for date in unique_dates]
dusk = [l.sun(pd.Timestamp(date), local = True)['dusk'].time() for date in unique_dates]
sunset_minutes = [time.hour * 60 + time.minute for time in sunset]
dusk_minutes = [time.hour * 60 + time.minute for time in dusk]
sunset_times = pd.DataFrame(zip(unique_dates, sunset, dusk, sunset_minutes, dusk_minutes))
sunset_times.columns = ['date', 'sunset', 'dusk', 'sunset_minute', 'dusk_minute']
return sunset_times
#input files: weather_irradiation = 'input/weather/solarirradiation_twenthe.csv' weather_timebaseDataset = 3600 #in seconds per interval #Simulation: #number of days to simulate and skipping of initial days. Simulation starts at Sunday January 1. numDays = 5 # number of days startDay = 180 # Initial day numHouses = 30 #Select the geographic location. Refer to the Astral plugin to see available locations (or give a lon+lat) # Use e.g. https://www.latlong.net/ from astral import Location location = Location() location.solar_depression = 'civil' location.latitude = 52.239095 location.longitude = 6.857018 location.timezone = 'Europe/Amsterdam' location.elevation = 0 #Select the devices in the neighbourhood #Devices #Scale overall consumption: consumptionFactor = 1.0 #consumption was a bit too high # Penetration of emerging technology in percentages # all values must be between 0-100 # These indicate what percentage of the houses has a certain device
# create a new diel event table
table_name = 'ev_' + in_name[3:]
eventTable = arcpy.CreateTable_management(diveDir, table_name, evTemplate)
# field list
f_names = [
'ptt', 'fromlocation', 'tolocation', 'diel', 'moon', 'startdate',
'enddate'
]
with arcpy.da.InsertCursor(eventTable, f_names) as iCur:
rows = []
# define diel events for first day
loc = Location(('loc', '', locs[0][1][1], locs[0][1][0], 'US/Pacific',
0)) # pass in as a tuple
loc.solar_depression = 'civil'
loc_day = startDate # as datetime
sun = loc.sun(loc_day)
moon = loc.moon_phase(loc_day)
events = {
1: sun['dawn'].replace(tzinfo=None),
2: sun['sunrise'].replace(tzinfo=None),
3: sun['sunset'].replace(tzinfo=None),
4: sun['dusk'].replace(tzinfo=None)
}
diel_name = {1: 'Night', 2: 'Dawn', 3: 'Day', 4: 'Dusk'}
per = 3 # first timevalue (deploy) will always be sometime before sunset, RIGHT?
seg = 0
cur_hour = startDate.hour # integer
endDate = datetime.datetime.combine(startDate.date(),
datetime.time(cur_hour + 1))
#!/usr/bin/env python
import datetime
from astral import Astral, Location
import pytz
def dt2min(dt):
return dt.hour * 60 + dt.minute
loc = Location(("Santa Clara", "California", 37.21, -121.58, "US/Pacific", 22))
loc.solar_depression = 'civil'
oneday = datetime.timedelta(days=1)
soy = datetime.date(2016, 1, 1)
for i in (range(1, 366)):
sun = loc.sun(date=soy, local=True)
print("{{ 0x{0:X}, 0x{1:X} }},".format(dt2min(sun['sunrise']), dt2min(sun['sunset'])))
soy = soy + oneday
