def test_vs_noaa_sydney(self):
sydney = Location('Sydney', 'AUS', latitude=-33.87, longitude=151.22,
time_zone=10)
sp = Sunpath.from_location(sydney)
sun = sp.calculate_sun(month=11, day=15, hour=11.0)
assert round(sun.altitude, 2) == 72.26
assert round(sun.azimuth, 2) == 32.37
def test_vs_noaa_new_york(self):
nyc = Location('New_York', 'USA', latitude=40.72, longitude=-74.02,
time_zone=-5)
sp = Sunpath.from_location(nyc)
sun = sp.calculate_sun(month=9, day=15, hour=11.0)
assert round(sun.altitude, 2) == 50.35
assert round(sun.azimuth, 2) == 159.72
def test_from_location(self):
sydney = Location('Sydney', 'AUS', latitude=-33.87, longitude=151.22,
time_zone=10)
sunpath = Sunpath.from_location(sydney)
assert sunpath.latitude == -33.87
assert sunpath.longitude == 151.22
assert sunpath.time_zone == 10
def _calculate_solar_values(self):
"""Calculate solar values for requested hours of the year.
This method is called everytime that output type is set.
"""
wea = self.wea
hoys_set = set(wea.hoys)
output_type = self.output_type
month_date_time = (DateTime.from_hoy(idx) for idx in self.hoys)
sp = Sunpath.from_location(wea.location, self.north)
# use gendaylit to calculate radiation values for each hour.
print('Calculating solar values...')
for timecount, dt in enumerate(month_date_time):
if dt.hoy not in hoys_set:
print('Warn: Wea data for {} is not available!'.format(dt))
continue
month, day, hour = dt.month, dt.day, dt.float_hour
dnr, dhr = wea.get_radiation_values(month, day, hour)
sun = sp.calculate_sun(month, day, hour)
if sun.altitude < 0:
continue
if dnr == 0:
solarradiance = 0
else:
solarradiance = \
int(gendaylit(sun.altitude, month, day, hour, dnr, dhr, output_type))
self._solar_values.append(solarradiance)
# keep the number of hour relative to hoys in this sun matrix
self._sun_up_hours_indices.append(timecount)
def from_json(cls, rec_json):
"""Create the solar access recipe from json.
{
"id": "solar_access",
"type": "gridbased",
"location": null, // a honeybee location - see below
"hoys": [], // list of hours of the year
"surfaces": [], // list of honeybee surfaces
"analysis_grids": [] // list of analysis grids
"sun_vectors": [] // list of sun vectors if location is not provided
}
"""
hoys = rec_json["hoys"]
if 'sun_vectors' not in rec_json or not rec_json['sun_vectors']:
# create sun vectors from location inputs
loc = Location.from_json(rec_json['location'])
sp = Sunpath.from_location(loc)
suns = (sp.calculate_sun_from_hoy(hoy) for hoy in hoys)
sun_vectors = tuple(s.sun_vector for s in suns if s.is_during_day)
else:
sun_vectors = rec_json['sun_vectors']
analysis_grids = \
tuple(AnalysisGrid.from_json(ag) for ag in rec_json["analysis_grids"])
hb_objects = tuple(HBSurface.from_json(srf) for srf in rec_json["surfaces"])
return cls(sun_vectors, hoys, analysis_grids, 1, hb_objects)
def test_daylight_saving(self):
nyc = Location('New_York', 'USA', latitude=40.72, longitude=-74.02,
time_zone=-5)
sp = Sunpath.from_location(nyc)
dt1 = DateTime(6, 21, 12, 0)
dt2 = DateTime(12, 21, 12, 0)
# TODO(mostapha): This is not implemented yet
# assert sp.is_daylight_saving_hour(dt1) is True
assert sp.is_daylight_saving_hour(dt1) is False
assert sp.is_daylight_saving_hour(dt2) is False
def test_leap_year(self):
nyc = Location('New_York', 'USA', latitude=40.72, longitude=-74.02,
time_zone=-5)
sp = Sunpath.from_location(nyc)
sp.is_leap_year = True
sun = sp.calculate_sun(month=2, day=29, hour=11.0)
assert sun.datetime == DateTime(2, 29, 11, leap_year=True)
assert sun.datetime.year == 2016
assert sun.datetime.month == 2
assert sun.datetime.day == 29
assert sun.datetime.hour == 11
def from_location_and_hoys(cls, location, hoys, point_groups, vector_groups=[],
timestep=1, hb_objects=None, sub_folder='sunlighthour'):
"""Create sunlighthours recipe from Location and hours of year."""
sp = Sunpath.from_location(location)
suns = (sp.calculate_sun_from_hoy(hoy) for hoy in hoys)
sun_vectors = tuple(s.sun_vector for s in suns if s.is_during_day)
analysis_grids = cls.analysis_grids_from_points_and_vectors(point_groups,
vector_groups)
return cls(sun_vectors, hoys, analysis_grids, timestep, hb_objects, sub_folder)
def test_daylight_saving():
nyc = Location('New_York',
'USA',
latitude=40.72,
longitude=-74.02,
time_zone=-5)
sp = Sunpath.from_location(nyc)
dt1 = DateTime(6, 21, 12, 0)
dt2 = DateTime(12, 21, 12, 0)
# TODO(mostapha): This is not implemented yet
# assert sp.is_daylight_saving_hour(dt1) is True
assert sp.is_daylight_saving_hour(dt1) is False
assert sp.is_daylight_saving_hour(dt2) is False
def test_calculate_sun():
"""Test the varios calculate_sun methods to be sure they all give the same result."""
nyc = Location('New_York', 'USA', latitude=40.72, longitude=-74.02, time_zone=-5)
sp = Sunpath.from_location(nyc)
dt1 = DateTime(3, 21, 9, 30)
sun1 = sp.calculate_sun(dt1.month, dt1.day, dt1.float_hour)
sun2 = sp.calculate_sun_from_hoy(dt1.hoy)
sun3 = sp.calculate_sun_from_moy(dt1.moy)
sun4 = sp.calculate_sun_from_date_time(dt1)
assert sun1 == sun2 == sun3 == sun4
assert round(sun1.altitude, 2) == 36.90
assert round(sun1.azimuth, 2) == 129.23
def test_leap_year(self):
nyc = Location('New_York',
'USA',
latitude=40.72,
longitude=-74.02,
time_zone=-5)
sp = Sunpath.from_location(nyc)
sp.is_leap_year = True
sun = sp.calculate_sun(month=2, day=29, hour=11.0)
assert sun.datetime == DateTime(2, 29, 11, leap_year=True)
assert sun.datetime.year == 2016
assert sun.datetime.month == 2
assert sun.datetime.day == 29
assert sun.datetime.hour == 11
def test_north_angle():
"""Test the north_angle property on the sun path."""
nyc = Location('New_York', country='USA', latitude=40.72, longitude=-74.02,
time_zone=-5)
sp = Sunpath.from_location(nyc)
sp.north_angle = 90
assert sp.north_angle == 90
sun = sp.calculate_sun(3, 21, 6, True)
assert sun.azimuth == approx(90, rel=1e-2)
assert sun.is_solar_time
assert sun.north_angle == 90
assert sun.position_3d(radius=1).y == approx(1, rel=1e-2)
assert sun.position_3d(radius=1).x < 1e-9
def test_daylight_saving():
nyc = Location('New_York',
'USA',
latitude=40.72,
longitude=-74.02,
time_zone=-5)
daylight_saving = AnalysisPeriod(st_month=3,
st_day=8,
end_month=11,
end_day=1)
sp = Sunpath.from_location(nyc, daylight_saving_period=daylight_saving)
dt1 = DateTime(6, 21, 12, 0)
dt2 = DateTime(12, 21, 12, 0)
assert sp.is_daylight_saving_hour(dt1)
assert not sp.is_daylight_saving_hour(dt2)
def test_vs_noaa_sydney():
"""Test to be sure that the sun positions align with the NOAA formula."""
sydney = Location('Sydney',
country='AUS',
latitude=-33.87,
longitude=151.22,
time_zone=10)
sp = Sunpath.from_location(sydney)
sun = sp.calculate_sun(month=11, day=15, hour=11.0)
assert round(sun.altitude, 2) == 72.26
assert round(sun.azimuth, 2) == 32.37
sun2 = sp.calculate_sun_from_date_time(datetime.datetime(2019, 6, 21, 9))
assert round(sun2.altitude, 2) == 18.99
assert round(sun2.azimuth, 2) == 42.54
def test_analemma_geometry():
"""Test the analemma_suns method."""
nyc = Location('New_York', country='USA', latitude=40.72, longitude=-74.02,
time_zone=-5)
sp = Sunpath.from_location(nyc)
radius = 100
analemma_geo = sp.hourly_analemma_polyline3d(radius=radius)
assert len(analemma_geo) == 15
for pline in analemma_geo:
assert isinstance(pline, Polyline3D)
analemma_geo = sp.hourly_analemma_polyline2d(radius=radius)
assert len(analemma_geo) == 15
for pline in analemma_geo:
assert isinstance(pline, Polyline2D)
def test_analemma_daytime_with_linesegment():
"""Test the hourly_analemma_polyline3d with a latitude that yields a line segment."""
test_loc = Location('Test Location', latitude=51, longitude=0)
sp = Sunpath.from_location(test_loc)
l_seg_count = 0
for pline in sp.hourly_analemma_polyline3d():
if isinstance(pline, LineSegment3D):
l_seg_count += 1
assert l_seg_count == 0
l_seg_count = 0
for pline in sp.hourly_analemma_polyline2d():
if isinstance(pline, LineSegment2D):
l_seg_count += 1
assert l_seg_count == 0
def from_location_and_analysis_period(
cls, location, analysis_period, point_groups, vector_groups=None,
hb_objects=None, sub_folder='sunlighthour'):
"""Create sunlighthours recipe from Location and analysis period."""
vector_groups = vector_groups or ()
sp = Sunpath.from_location(location)
suns = (sp.calculate_sun_from_hoy(hoy) for hoy in analysis_period.float_hoys)
sun_vectors = tuple(s.sun_vector for s in suns if s.is_during_day)
hoys = tuple(s.hoy for s in suns if s.is_during_day)
analysis_grids = cls.analysis_grids_from_points_and_vectors(point_groups,
vector_groups)
return cls(sun_vectors, hoys, analysis_grids, analysis_period.timestep,
hb_objects, sub_folder)
def test_body_dir_from_dir_normal():
"""Test body_solar_flux_from_parts gainst its horizontal counterpart."""
wea_obj = Wea.from_epw_file('./tests/epw/chicago.epw')
diff_hr = wea_obj.diffuse_horizontal_irradiance.values
dir_nr = wea_obj.direct_normal_irradiance.values
dir_hr = wea_obj.direct_horizontal_irradiance.values
dts = wea_obj.datetimes
sp = Sunpath.from_location(wea_obj.location)
for i in xrange(8760):
sun = sp.calculate_sun_from_date_time(dts[i])
alt, az = sun.altitude, sun.azimuth
sharp = sharp_from_solar_and_body_azimuth(az, 180)
sflux1 = body_solar_flux_from_parts(diff_hr[i], dir_nr[i], alt, sharp)
sflux2 = body_solar_flux_from_horiz_solar(diff_hr[i], dir_hr[i], alt,
sharp)
assert sflux1 == pytest.approx(sflux2, rel=1e-2)
def _solar_calc(self,
hoys,
wea,
output_type,
leap_year=False,
reverse_vectors=False):
"""Calculate sun vectors and radiance values from the properties."""
solar_calc = LBSunpath.from_location(self.location, self.north)
solar_calc.is_leap_year = leap_year
if not hoys:
# set hours to an annual hourly sunpath
hoys = range(8760) if not leap_year else range(8760 + 24)
sun_up_hours = []
sun_vectors = []
radiance_values = []
altitudes = []
for hour in hoys:
sun = solar_calc.calculate_sun_from_hoy(hour)
if sun.altitude < 0:
continue
sun_vectors.append(sun.sun_vector)
sun_up_hours.append(hour)
altitudes.append(sun.altitude)
# calculate irradiance value
if wea:
# this is a climate_based sunpath. Get the values from wea
assert isinstance(wea, Wea), 'Expected Wea not %s' % type(wea)
for altitude, hoy in zip(altitudes, sun_up_hours):
dnr, dhr = wea.get_irradiance_value_for_hoy(hoy)
if dnr == 0:
radiance_value = 0
else:
radiance_value = gendaylit(altitude, hoy, dnr, dhr,
output_type, leap_year)
radiance_values.append(int(radiance_value))
else:
radiance_values = [1e6] * len(sun_up_hours)
if reverse_vectors:
sun_vectors = [v.reverse() for v in sun_vectors]
return sun_vectors, sun_up_hours, radiance_values
def draw_sunpath(self):
"""Calculate and draw sun path to Rhino."""
self.clear_conduit()
_location = self.epw.location
north_ = 0
_centerPt_ = None
_scale_ = 1
_sunScale_ = 1
_annual_ = True
daylight_saving_period = None # temporary until we fully implement it
_hoys_ = range(23)
# initiate sunpath based on location
sp = Sunpath.from_location(_location, north_, daylight_saving_period)
# draw sunpath geometry
sunpath_geo = \
sp.draw_sunpath(_hoys_, _centerPt_, _scale_, _sunScale_, _annual_)
analemma = sunpath_geo.analemma_curves
compass = sunpath_geo.compass_curves
daily = sunpath_geo.daily_curves
sun_pts = sunpath_geo.sun_geos
suns = sunpath_geo.suns
vectors = (geo.vector(*sun.sun_vector) for sun in suns)
altitudes = (sun.altitude for sun in suns)
azimuths = (sun.azimuth for sun in suns)
center_pt = _centerPt_ or geo.point(0, 0, 0)
hoys = (sun.hoy for sun in suns)
datetimes = (sun.datetime for sun in suns)
# draw the curves to canvas
self.conduit = DrawSunPath(
list(analemma) + list(compass) + list(daily), sun_pts)
self.conduit.Enabled = True
# add lights
for sun in suns:
light = Rhino.Geometry.Light.CreateSunLight(
north_, sun.azimuth - 90, sun.altitude)
sc.doc.Lights.Add(light)
sc.doc.Views.Redraw()
def _get_altitudes_and_sharps(self):
"""Get altitudes and sharps from solar position."""
sp = Sunpath.from_location(self._location)
_altitudes = []
if self._body_par.body_azimuth is None:
_sharps = [self._body_par.sharp] * self._calc_length
for t_date in self._base_collection.datetimes:
sun = sp.calculate_sun_from_date_time(t_date)
_altitudes.append(sun.altitude)
else:
_sharps = []
for t_date in self._base_collection.datetimes:
sun = sp.calculate_sun_from_date_time(t_date)
sharp = sharp_from_solar_and_body_azimuth(
sun.azimuth, self._body_par.body_azimuth)
_sharps.append(sharp)
_altitudes.append(sun.altitude)
return _altitudes, _sharps
def test_sunrise_sunset():
"""Test to be sure that the sunrise/sunset aligns with the NOAA formula."""
sydney = Location('Sydney', country='AUS', latitude=-33.87, longitude=151.22,
time_zone=10)
sp = Sunpath.from_location(sydney)
# use the depression for apparent sunrise/sunset
srss_dict = sp.calculate_sunrise_sunset(6, 21, depression=0.8333)
assert srss_dict['sunrise'] == DateTime(6, 21, 7, 0)
assert srss_dict['noon'] == DateTime(6, 21, 11, 57)
assert srss_dict['sunset'] == DateTime(6, 21, 16, 54)
# use the depression for actual sunrise/sunset
srss_dict = sp.calculate_sunrise_sunset(6, 21, depression=0.5334)
assert srss_dict['sunrise'] == DateTime(6, 21, 7, 2)
assert srss_dict['noon'] == DateTime(6, 21, 11, 57)
assert srss_dict['sunset'] == DateTime(6, 21, 16, 52)
def from_location_and_analysis_period(
cls, location, analysis_period, point_groups, vector_groups=None,
hb_objects=None, sub_folder='sunlighthour'
):
"""Create sunlighthours recipe from Location and analysis period."""
vector_groups = vector_groups or ()
sp = Sunpath.from_location(location)
suns = (sp.calculate_sun_from_hoy(hoy) for hoy in analysis_period.float_hoys)
sun_vectors = tuple(s.sun_vector for s in suns if s.is_during_day)
hoys = tuple(s.hoy for s in suns if s.is_during_day)
analysis_grids = cls.analysis_grids_from_points_and_vectors(point_groups,
vector_groups)
return cls(sun_vectors, hoys, analysis_grids, analysis_period.timestep,
hb_objects, sub_folder)
def from_location_and_hoys(cls,
location,
hoys,
point_groups,
vector_groups=[],
timestep=1,
hb_objects=None,
sub_folder='sunlighthour'):
"""Create sunlighthours recipe from Location and hours of year."""
sp = Sunpath.from_location(location)
suns = tuple(sp.calculate_sun_from_hoy(hoy) for hoy in hoys)
sun_vectors = tuple(s.sun_vector for s in suns if s.is_during_day)
sun_up_hoys = tuple(s.hoy for s in suns if s.is_during_day)
analysis_grids = cls.analysis_grids_from_points_and_vectors(
point_groups, vector_groups)
return cls(sun_vectors, sun_up_hoys, analysis_grids, timestep,
hb_objects, sub_folder)
def test_analemma_suns():
"""Test the analemma_suns method."""
nyc = Location('New_York', country='USA', latitude=40.72, longitude=-74.02,
time_zone=-5)
sp = Sunpath.from_location(nyc)
assert len(sp.analemma_suns(Time(12), True, True)) == 12
assert len(sp.analemma_suns(Time(6), True, True)) == 7
assert len(sp.analemma_suns(Time(0), True, True)) == 0
assert len(sp.analemma_suns(Time(6), False, True)) == 12
assert len(sp.analemma_suns(Time(0), False, True)) == 12
for sun in sp.analemma_suns(Time(12)):
assert isinstance(sun, Sun)
assert len(sp.hourly_analemma_suns()) == 24
for hr in sp.hourly_analemma_suns():
for sun in hr:
assert isinstance(sun, Sun)
def test_daylight_saving():
"""Test the applicaiton of daylight saving time."""
nyc = Location('New_York',
country='USA',
latitude=40.72,
longitude=-74.02,
time_zone=-5)
daylight_saving = AnalysisPeriod(st_month=3,
st_day=8,
st_hour=2,
end_month=11,
end_day=1,
end_hour=2)
sp = Sunpath.from_location(nyc, daylight_saving_period=daylight_saving)
dt1 = DateTime(6, 21, 12, 0)
dt2 = DateTime(12, 21, 12, 0)
dt3 = DateTime(6, 21, 0)
dt4 = DateTime(12, 21, 0)
assert sp.is_daylight_saving_hour(dt1)
assert not sp.is_daylight_saving_hour(dt2)
assert sp.is_daylight_saving_hour(dt3)
assert not sp.is_daylight_saving_hour(dt4)
sun1ds = sp.calculate_sun_from_date_time(dt1)
sun2ds = sp.calculate_sun_from_date_time(dt2)
sun3ds = sp.calculate_sun_from_date_time(dt3)
sun4ds = sp.calculate_sun_from_date_time(dt4)
sp.daylight_saving_period = None
assert sun1ds != sp.calculate_sun_from_date_time(dt1)
assert sun3ds != sp.calculate_sun_from_date_time(dt3)
assert sun1ds.altitude == \
approx(sp.calculate_sun_from_date_time(dt1.sub_hour(1)).altitude, rel=1e-2)
assert sun3ds.altitude == \
approx(sp.calculate_sun_from_date_time(dt3.sub_hour(1)).altitude, rel=1e-2)
sun2 = sp.calculate_sun_from_date_time(dt2)
sun4 = sp.calculate_sun_from_date_time(dt4)
assert sun2 == sun2ds
assert sun4 == sun4ds
def process(self):
if not any(socket.is_linked for socket in self.outputs):
return
location_s = self.inputs['Location'].sv_get()
month_s = self.inputs['Month'].sv_get()
day_s = self.inputs['Day'].sv_get()
hour_s = self.inputs['Hour'].sv_get()
altitude_s, azimuth_s = [], []
sun_pos = []
for location, months, days, hours in zip_long_repeat(
location_s, month_s, day_s, hour_s):
sp = Sunpath.from_location(location)
# sp = Sunpath.from_location(location, north_angle=40)
altitude, azimuth = [], []
for mo, day, ho in zip_long_repeat(months, days, hours):
sun = sp.calculate_sun(month=mo, day=day, hour=ho)
alt_l = sun.altitude
azi_l = sun.azimuth
altitude.append(sun.altitude)
azimuth.append(sun.azimuth)
angles = (alt_l * pi / 180, 0,
-azi_l * pi / 180 + self.north_angle * pi / 180)
euler = Euler(angles, 'XYZ')
mat_r = euler.to_quaternion().to_matrix().to_4x4()
pos = mat_r @ Vector((0, self.sun_dist, 0))
mat_t = Matrix.Translation(pos)
angles = ((90 - alt_l) * pi / 180, 0,
(180 - azi_l) * pi / 180 +
self.north_angle * pi / 180)
euler = Euler(angles, 'XYZ')
mat_r = euler.to_quaternion().to_matrix().to_4x4()
m = mat_t @ mat_r
sun_pos.append(m)
altitude_s.append(altitude)
azimuth_s.append(azimuth)
self.outputs['Altitude'].sv_set(altitude_s)
self.outputs['Azimuth'].sv_set(azimuth_s)
self.outputs['Sun Position'].sv_set(sun_pos)
def from_location_sun_up_hours(cls, location, sun_up_hours, north=0,
is_leap_year=False):
"""Generate a radiance-based analemma for a location.
Args:
location: A ladybug location.
sun_up_hours: A list of hours of the year to be included in analemma.
north: North angle from Y direction (default: 0).
is_leap_year: A boolean to indicate if hours are for a leap year
(default: False).
"""
sun_vectors = []
north = north or 0
sp = Sunpath.from_location(location, north)
sp.is_leap_year = is_leap_year
for hour in sun_up_hours:
sun = sp.calculate_sun_from_hoy(hour)
sun_vectors.append(sun.sun_vector)
return cls(sun_vectors, sun_up_hours)
def from_location(cls, location, hoys=None, north=0):
"""Generate a radiance-based analemma for a location.
Args:
location: A ladybug location.
hoys: A list of hours of the year (default: range(8760)).
north: North angle from Y direction (default: 0).
"""
sun_vectors = []
sun_up_hours = []
hoys = hoys or range(8760)
north = north or 0
sp = Sunpath.from_location(location, north)
for hour in hoys:
sun = sp.calculate_sun_from_hoy(hour)
if sun.altitude < 0:
continue
sun_vectors.append(sun.sun_vector)
sun_up_hours.append(hour)
return cls(sun_vectors, sun_up_hours)
def _calculate_solar_values(wea,
hoys,
output_type,
north=0,
is_leap_year=False):
"""Calculate solar values for requested hours of the year.
This method is called everytime that output type is set.
"""
month_date_time = (DateTime.from_hoy(idx, is_leap_year)
for idx in hoys)
sp = Sunpath.from_location(wea.location, north)
sp.is_leap_year = is_leap_year
solar_values = []
sun_up_hours = []
# use gendaylit to calculate radiation values for each hour.
print('Calculating solar values...')
for timecount, dt in enumerate(month_date_time):
month, day, hour = dt.month, dt.day, dt.float_hour
sun = sp.calculate_sun(month, day, hour)
if sun.altitude < 0:
continue
else:
dnr, dhr = wea.get_irradiance_value(month, day, hour)
if dnr == 0:
solarradiance = 0
else:
solarradiance = \
int(gendaylit(sun.altitude, month, day, hour, dnr, dhr,
output_type))
solar_values.append(solarradiance)
# keep the number of hour relative to hoys in this sun matrix
sun_up_hours.append(dt.hoy)
return solar_values, sun_up_hours
def test_day_arc_geometry():
"""Test the day_arc3d method."""
nyc = Location('New_York', country='USA', latitude=40.72, longitude=-74.02,
time_zone=-5)
sp = Sunpath.from_location(nyc)
radius = 100
arc_geo = sp.day_arc3d(3, 21, radius=radius)
assert isinstance(arc_geo, Arc3D)
assert arc_geo.length == approx(math.pi * radius, rel=1e-2)
arc_geo = sp.day_polyline2d(3, 21, radius=radius)
assert isinstance(arc_geo, Polyline2D)
arc_geo = sp.monthly_day_arc3d(radius=radius)
assert len(arc_geo) == 12
for pline in arc_geo:
assert isinstance(pline, Arc3D)
arc_geo = sp.monthly_day_polyline2d(radius=radius)
assert len(arc_geo) == 12
for pline in arc_geo:
assert isinstance(pline, Polyline2D)
def test_split_modifiers():
ap = AnalysisPeriod(timestep=4)
sp = Sunpath(location)
lb_sp = LBSunpath.from_location(location)
folder = './tests/assets/temp'
filename = 'sunpath_timestep_4'
sp.to_file(folder, filename, hoys=ap.hoys)
sp_file = os.path.join(folder, '%s.rad' % filename)
sp_mod_files = [
os.path.join(folder, '%s_%d.mod' % (filename, count))
for count in range(2)
]
assert os.path.isfile(sp_file)
lb_tot = [
i for i in ap.hoys if lb_sp.calculate_sun_from_hoy(i).is_during_day
]
sun_count = [int(len(lb_tot) / 2) - 1, int(len(lb_tot) / 2)]
for sp_count, sp_mod_file in enumerate(sp_mod_files):
assert os.path.isfile(sp_mod_file)
with open(sp_mod_file) as inf:
for count, _ in enumerate(inf):
pass
assert count == sun_count[sp_count]
def oiko_make_epw(api_key, city, country, latitude, longitude, year):
'''
Args:}
api_key: User API key for OikoLab.
city: city name as string
country: country name as string.
latitude: Location latitude between -90 and 90
longitude: Location longitude between -180 (west) and 180 (east)
year: year between 1980 and 2019
'''
parameters = [
'temperature', 'dewpoint_temperature', 'surface_solar_radiation',
'surface_thermal_radiation', 'surface_direct_solar_radiation',
'surface_diffuse_solar_radiation', 'relative_humidity', 'wind_speed',
'surface_pressure', 'total_cloud_cover'
]
location = Location(city=city,
country=country,
latitude=latitude,
longitude=longitude)
# create the payload
payload = {
'param': parameters,
'start': '{}-01-01T00:00:00'.format(year),
'end': '{}-12-31T23:00:00'.format(year),
'lat': location.latitude,
'lon': location.longitude,
}
# make the request
r = requests.get('https://api.oikolab.com/weather',
params=payload,
headers={
'content-encoding': 'gzip',
'Connection': 'close',
'api-key': '{}'.format(api_key)
})
if r.status_code == 200:
attributes = r.json()['attributes']
weather_data = json.loads(r.json()['data'])
else:
print(r.text)
return None
# set the UTC offset on the location based on the request
location.time_zone = attributes['utc_offset']
leap_yr = True if year % 4 == 0 else False
# create a dictionary of timeseries data streams
data_dict = {}
data_values = zip(*weather_data['data'])
for param, data in zip(parameters, data_values):
data_dict[param] = data
# compute solar radiation components and estimate illuminance from irradiance
datetimes = AnalysisPeriod(is_leap_year=leap_yr).datetimes
direct_normal_irr = []
gh_ill = []
dn_ill = []
dh_ill = []
sp = Sunpath.from_location(location)
sp.is_leap_year = leap_yr
for dt, glob_hr, dir_hr, dif_hr, dp in zip(
datetimes, data_dict['surface_solar_radiation'],
data_dict['surface_direct_solar_radiation'],
data_dict['surface_diffuse_solar_radiation'],
data_dict['dewpoint_temperature']):
sun = sp.calculate_sun_from_date_time(dt)
alt = sun.altitude
dir_nr = dir_hr / math.sin(math.radians(alt)) if alt > 1 else 0
direct_normal_irr.append(dir_nr)
gh, dn, dh, z = estimate_illuminance_from_irradiance(
alt, glob_hr, dir_nr, dif_hr, dp)
gh_ill.append(gh)
dn_ill.append(dn)
dh_ill.append(dh)
# create the EPW object and set properties
epw = EPW.from_missing_values(is_leap_year=leap_yr)
epw.location = location
epw.years.values = [year] * 8760 if not leap_yr else [year] * 8784
epw.dry_bulb_temperature.values = data_dict['temperature']
epw.dew_point_temperature.values = data_dict['dewpoint_temperature']
epw.global_horizontal_radiation.values = data_dict[
'surface_solar_radiation']
epw.direct_normal_radiation.values = direct_normal_irr
epw.diffuse_horizontal_radiation.values = data_dict[
'surface_diffuse_solar_radiation']
epw.global_horizontal_illuminance.values = gh_ill
epw.direct_normal_illuminance.values = dn_ill
epw.diffuse_horizontal_illuminance.values = dh_ill
epw.horizontal_infrared_radiation_intensity.values = data_dict[
'surface_thermal_radiation']
epw.relative_humidity.values = [
val * 100.0 for val in data_dict['relative_humidity']
]
epw.atmospheric_station_pressure.values = data_dict['surface_pressure']
epw.total_sky_cover.values = data_dict['total_cloud_cover']
epw.wind_speed.values = data_dict['wind_speed']
# write EPW to a file
file_path = os.path.join(folders.default_epw_folder,
'{}_{}.epw'.format(location.city, year))
epw.save(file_path)
print('EPW file generated and saved to - %s' % file_path)
return file_path
projection_ = projection_.title() if projection_ is not None else None
# create a intersection of the input hoys_ and the data hoys
if len(data_) > 0 and len(hoys_) > 0:
all_aligned = all(data_[0].is_collection_aligned(d) for d in data_[1:])
assert all_aligned, 'All collections input to data_ must be aligned for ' \
'each Sunpath.\nGrafting the data_ and suplying multiple grafted ' \
'_center_pt_ can be used to view each data on its own path.'
if statement_ is not None:
data_ = HourlyContinuousCollection.filter_collections_by_statement(
data_, statement_)
data_hoys = set(dt.hoy for dt in data_[0].datetimes)
hoys_ = list(data_hoys.intersection(set(hoys_)))
# initialize sunpath based on location
sp = Sunpath.from_location(_location, north_, dl_saving_)
# process all of the input hoys into altitudes, azimuths and vectors
altitudes, azimuths, datetimes, moys, hoys, vectors, suns = [], [], [], [], [], [], []
for hoy in hoys_:
sun = sp.calculate_sun_from_hoy(hoy, solar_time_)
if sun.is_during_day:
altitudes.append(sun.altitude)
azimuths.append(sun.azimuth)
datetimes.append(sun.datetime)
moys.append(sun.datetime.moy)
hoys.append(sun.datetime.hoy)
vectors.append(from_vector3d(sun.sun_vector))
suns.append(sun)
if len(data_) > 0 and len(
def sun_altitude(epw_file):
epw = EPW(epw_file)
sun_path = Sunpath.from_location(epw.location)
return np.array(
[sun_path.calculate_sun_from_hoy(i).altitude for i in range(8760)])
def execute(self, working_dir, reuse=True):
"""Generate sun matrix.
Args:
working_dir: Folder to execute and write the output.
reuse: Reuse the matrix if already existed in the folder.
Returns:
Full path to analemma, sunlist and sun_matrix.
"""
fp = os.path.join(working_dir, self.analemmafile)
lfp = os.path.join(working_dir, self.sunlistfile)
mfp = os.path.join(working_dir, self.sunmtxfile)
hrf = os.path.join(working_dir, self.name + '.hrs')
output_type = self.sky_type
if reuse:
if self.hours_match(hrf):
for f in (fp, lfp, mfp):
if not os.path.isfile(f):
break
else:
return fp, lfp, mfp
with open(hrf, 'wb') as outf:
outf.write(','.join(str(h) for h in self.hoys) + '\n')
wea = self.wea
month_date_time = (DateTime.from_hoy(idx) for idx in self.hoys)
latitude, longitude = wea.location.latitude, -wea.location.longitude
sp = Sunpath.from_location(wea.location, self.north)
solarradiances = []
sun_values = []
sun_up_hours = [] # collect hours that sun is up
solarstring = \
'void light solar{0} 0 0 3 {1} {1} {1} ' \
'solar{0} source sun 0 0 4 {2:.6f} {3:.6f} {4:.6f} 0.533'
# use gendaylit to calculate radiation values for each hour.
print('Calculating sun positions and radiation values.')
count = 0
for timecount, timeStamp in enumerate(month_date_time):
month, day, hour = timeStamp.month, timeStamp.day, timeStamp.hour + 0.5
dnr, dhr = int(wea.direct_normal_radiation[timeStamp.int_hoy]), \
int(wea.diffuse_horizontal_radiation[timeStamp.int_hoy])
if dnr == 0:
continue
count += 1
sun = sp.calculate_sun(month, day, hour)
if sun.altitude < 0:
continue
x, y, z = sun.sun_vector
solarradiance = \
int(gendaylit(sun.altitude, month, day, hour, dnr, dhr, output_type))
cur_sun_definition = solarstring.format(count, solarradiance, -x,
-y, -z)
solarradiances.append(solarradiance)
sun_values.append(cur_sun_definition)
# keep the number of hour relative to hoys in this sun matrix
sun_up_hours.append(timecount)
sun_count = len(sun_up_hours)
assert sun_count > 0, ValueError('There is 0 sun up hours!')
print('# Number of sun up hours: %d' % sun_count)
print('Writing sun positions and radiation values to {}'.format(fp))
# create solar discs.
with open(fp, 'w') as annfile:
annfile.write("\n".join(sun_values))
annfile.write('\n')
print('Writing list of suns to {}'.format(lfp))
# create list of suns.
with open(lfp, 'w') as sunlist:
sunlist.write("\n".join(
("solar%s" % (idx + 1) for idx in xrange(sun_count))))
sunlist.write('\n')
# Start creating header for the sun matrix.
file_header = ['#?RADIANCE']
file_header += ['Sun matrix created by Honeybee']
file_header += ['LATLONG= %s %s' % (latitude, -longitude)]
file_header += ['NROWS=%s' % sun_count]
file_header += ['NCOLS=%s' % len(self.hoys)]
file_header += ['NCOMP=3']
file_header += ['FORMAT=ascii']
print('Writing sun matrix to {}'.format(mfp))
# Write the matrix to file.
with open(mfp, 'w') as sunMtx:
sunMtx.write('\n'.join(file_header) + '\n' + '\n')
for idx, sunValue in enumerate(solarradiances):
sun_rad_list = ['0 0 0'] * len(self.hoys)
sun_rad_list[sun_up_hours[idx]] = '{0} {0} {0}'.format(
sunValue)
sunMtx.write('\n'.join(sun_rad_list) + '\n\n')
sunMtx.write('\n')
return fp, lfp, mfp
def post_process_solar_tracking(result_folders,
sun_up_file,
location,
north=0,
tracking_increment=5,
destination_folder=None):
"""Postprocess a list of result folders to account for dynamic solar tracking.
This function essentially takes .ill files for each state of a dynamic tracking
system and produces a single .ill file that models the tracking behavior.
Args:
result_folders: A list of folders containing .ill files and each representing
a state of the dynamic solar tracking system. These file should be
ordered from eastern-most to wester-most, tracing the path of the
tracking system over the day. The names of the .ill files should be
the same in each folder (representing the same sensor grid in a
different state).
sun_up_file: Path to a sun-up-hours.txt that contains the sun-up hours of
the simulation.
location: A Ladybug Location object to be used to generate sun poisitions.
north_: A number between -360 and 360 for the counterclockwise difference
between the North and the positive Y-axis in degrees. (Default: 0).
tracking_increment: An integer for the increment angle of each state in
degrees. (Default: 5).
destination_folder: A path to a destination folder where the final .ill
files of the dynamic tracking system will be written. If None, all
files will be written into the directory above the first result_folder.
(Default: None).
"""
# get the orientation angles of the panels for each model
st_angle = int(90 - (len(result_folders) * tracking_increment / 2)) + 1
end_angle = int(90 + (len(result_folders) * tracking_increment / 2))
angles = list(range(st_angle, end_angle, tracking_increment))
# create a sun path ang get the sun-up hours to be used to get solar positions
sp = Sunpath.from_location(location, north)
with open(sun_up_file) as suh_file:
sun_up_hours = [float(hour) for hour in suh_file.readlines()]
# for each hour of the sun_up_hours, figure out which file is the one to use
mtx_to_use, ground_vec = [], Vector3D(1, 0, 0)
for hoy in sun_up_hours:
sun = sp.calculate_sun_from_hoy(hoy)
vec = Vector3D(sun.sun_vector_reversed.x, 0, sun.sun_vector_reversed.z)
orient = math.degrees(ground_vec.angle(vec))
for i, ang in enumerate(angles):
if ang > orient:
mtx_to_use.append(i)
break
else:
mtx_to_use.append(-1)
# parse the grids_info in the first folder to understand the sensor grids
grids_info_file = os.path.join(result_folders[0], 'grids_info.json')
with open(grids_info_file) as gi_file:
grids_data = json.load(gi_file)
grid_ids = [g['full_id'] for g in grids_data]
# prepare the destination folder and copy the grids_info to it
if destination_folder is None:
destination_folder = os.path.dirname(result_folders[0])
if not os.path.isdir(destination_folder):
os.mkdir(destination_folder)
shutil.copyfile(grids_info_file,
os.path.join(destination_folder, 'grids_info.json'))
# convert the .ill files of each sensor grid into a single .ill file
for grid_id in grid_ids:
grid_mtx = []
for i, model in enumerate(result_folders):
grid_file = os.path.join(model, '{}.ill'.format(grid_id))
with open(grid_file) as ill_file:
grid_mtx.append([lin.split() for lin in ill_file])
grid_ill = []
for i, hoy_mtx in enumerate(mtx_to_use):
hoy_vals = []
for pt in range(len(grid_mtx[0])):
hoy_vals.append(grid_mtx[hoy_mtx][pt][i])
grid_ill.append(hoy_vals)
dest_file = os.path.join(destination_folder, '{}.ill'.format(grid_id))
with open(dest_file, 'w') as ill_file:
for row in zip(*grid_ill):
ill_file.write(' '.join(row) + '\n')
)
# create the points representing the human geometry
human_points = []
human_line = []
for pos in _position:
hpts, hlin = human_height_points(pos, _height_, _pt_count_)
human_points.extend(hpts)
human_line.append(hlin)
if _run:
# mesh the context for the intersection calculation
shade_mesh = join_geometry_to_mesh(_context)
# generate the sun vectors for each sun-up hour of the year
sp = Sunpath.from_location(_location, north_)
sun_vecs = []
day_pattern = []
for hoy in range(8760):
sun = sp.calculate_sun_from_hoy(hoy)
day_pattern.append(sun.is_during_day)
if sun.is_during_day:
sun_vecs.append(from_vector3d(sun.sun_vector_reversed))
# intersect the sun vectors with the context and compute fraction exposed
sun_int_matrix, angles = intersect_mesh_rays(shade_mesh,
human_points,
sun_vecs,
cpu_count=workers)
fract_body_exp = []
for i in range(0, len(human_points), _pt_count_):
north_, _location, _hoys_, _centerPt_, _scale_, _sunScale_, _annual_ = IN
vectors = altitudes = azimuths = sunPts = analemma = compass = daily = centerPt = hoys = datetimes = None
try:
from ladybug.sunpath import Sunpath
import ladybug.geometry as geo
except ImportError as e:
raise ImportError('\nFailed to import ladybug:\n\t{}'.format(e))
if _location:
daylightSavingPeriod = None # temporary until we fully implement it
_hoys_ = _hoys_ or ()
# initiate sunpath based on location
sp = Sunpath.from_location(_location, north_, daylightSavingPeriod)
# draw sunpath geometry
sunpath_geo = \
sp.draw_sunpath(_hoys_, _centerPt_, _scale_, _sunScale_, _annual_)
analemma = sunpath_geo.analemma_curves
compass = sunpath_geo.compass_curves
daily = sunpath_geo.daily_curves
sunPts = sunpath_geo.sun_geos
suns = sunpath_geo.suns
vectors = (geo.vector(*sun.sun_vector) for sun in suns)
altitudes = (sun.altitude for sun in suns)
azimuths = (sun.azimuth for sun in suns)
def shortwave_mrt_map(location,
longwave_data,
sun_up_hours,
total_ill,
direct_ill,
ref_ill=None,
solarcal_par=None):
"""Get MRT data collections adjusted for shortwave using Radiance .ill files.
Args:
location: A ladybug Location object to dictate the solar positions used
in the calculation.
longwave_data: An array of data collections for each point within a thermal
map. All collections must be aligned with one another. The analysis
period on the collections does not have to be annual.
sun_up_hours: File path to a sun-up-hours.txt file output by Radiance.
total_ill: Path to an .ill file output by Radiance containing total
irradiance for each longwave_data collection.
direct_ill: Path to an .ill file output by Radiance containing direct
irradiance for each longwave_data collection.
ref_ill: Path to an .ill file output by Radiance containing total ground-
reflected irradiance for each longwave_data collection. If None, a
default ground reflectance of 0.25 will be assumed.
solarcal_par: Optional SolarCalParameter object to account for
properties of the human geometry.
"""
# determine the analysis period and open the sun_up_hours file
a_per = longwave_data[0].header.analysis_period
is_annual, t_step, lp_yr = a_per.is_annual, a_per.timestep, a_per.is_leap_year
with open(sun_up_hours) as soh_f:
sun_indices = [int(float(h) * t_step) for h in soh_f]
# parse each of the .ill files into annual irradiance data collections
total = _ill_file_to_data(total_ill, sun_indices, t_step, lp_yr)
direct = _ill_file_to_data(direct_ill, sun_indices, t_step, lp_yr)
ref = _ill_file_to_data(ref_ill, sun_indices, t_step, lp_yr) \
if ref_ill is not None else None
# if the analysis is not annual, apply analysis periods
if not is_annual:
total = [data.filter_by_analysis_period(a_per) for data in total]
direct = [data.filter_by_analysis_period(a_per) for data in direct]
if ref is not None:
ref = [data.filter_by_analysis_period(a_per) for data in ref]
# convert total irradiance into indirect irradiance
indirect = [t_rad - d_rad for t_rad, d_rad in zip(total, direct)]
# compute solar altitudes and sharps
body_par = SolarCalParameter() if solarcal_par is None else solarcal_par
sp = Sunpath.from_location(location)
_altitudes = []
if body_par.body_azimuth is None:
_sharps = [body_par.sharp] * len(a_per)
for t_date in a_per.datetimes:
sun = sp.calculate_sun_from_date_time(t_date)
_altitudes.append(sun.altitude)
else:
_sharps = []
for t_date in a_per.datetimes:
sun = sp.calculate_sun_from_date_time(t_date)
sharp = sharp_from_solar_and_body_azimuth(sun.azimuth,
body_par.body_azimuth)
_sharps.append(sharp)
_altitudes.append(sun.altitude)
# pass all data through the solarcal collections and return MRT data collections
mrt_data = []
if ref is not None: # fully-detailed SolarCal with ground reflectance
for l_mrt, d_rad, i_rad, r_rad in zip(longwave_data, direct, indirect,
ref):
scl_obj = _HorizontalRefSolarCalMap(_altitudes, _sharps, d_rad,
i_rad, r_rad, l_mrt, None,
body_par)
mrt_data.append(scl_obj.mean_radiant_temperature)
else: # simpler SolarCal assuming default ground reflectance
for l_mrt, d_rad, i_rad in zip(longwave_data, direct, indirect):
scl_obj = _HorizontalSolarCalMap(_altitudes, _sharps, d_rad, i_rad,
l_mrt, None, None, body_par)
mrt_data.append(scl_obj.mean_radiant_temperature)
return mrt_data