def test_simplified_solis_series_elevation():
expected = pd.DataFrame(np.array([[959.335463, 1064.653145, 129.125602]]),
columns=['dni', 'ghi', 'dhi'])
expected = expected[['ghi', 'dni', 'dhi']]
out = clearsky.simplified_solis(pd.Series(80))
assert_frame_equal(expected, out)
def test_simplified_solis_dni_extra():
expected = pd.DataFrame(np.array([[963.555414, 1069.33637, 129.693603]]),
columns=['dni', 'ghi', 'dhi'])
expected = expected[['ghi', 'dni', 'dhi']]
out = clearsky.simplified_solis(80, dni_extra=pd.Series(1370))
assert_frame_equal(expected, out)
def test_simplified_solis_series_elevation():
expected = pd.DataFrame(np.array([[959.335463, 1064.653145, 129.125602]]),
columns=['dni', 'ghi', 'dhi'])
expected = expected[['ghi', 'dni', 'dhi']]
out = clearsky.simplified_solis(pd.Series(80))
assert_frame_equal(expected, out)
def solar_input_calculator(latitude=55.571831046,
longitude=12.822830042,
tz='Europe/Vatican',
name='Oresund',
start_date='2014-04-01',
end_date='2014-10-01',
freq='15Min',
normalized=True):
altitude = 0
times = pd.date_range(start=start_date, end=end_date, freq=freq, tz=tz)
solpos = pvlib.solarposition.get_solarposition(times, latitude, longitude)
apparent_elevation = solpos['apparent_elevation']
aod700 = 0.1
precipitable_water = 1
pressure = pvlib.atmosphere.alt2pres(altitude)
dni_extra = pvlib.irradiance.get_extra_radiation(times)
# an input is a Series, so solis is a DataFrame
solis = clearsky.simplified_solis(apparent_elevation, aod700,
precipitable_water, pressure, dni_extra)
if normalized is True:
return solis.dhi.values / np.max(solis.dhi.values)
else:
return solis.dhi.values
def test_simplified_solis_dni_extra():
expected = pd.DataFrame(np.array([[963.555414, 1069.33637, 129.693603]]),
columns=['dni', 'ghi', 'dhi'])
expected = expected[['ghi', 'dni', 'dhi']]
out = clearsky.simplified_solis(80, dni_extra=pd.Series(1370))
assert_frame_equal(expected, out)
def get_clearsky(self, times, model='ineichen', **kwargs):
"""
Calculate the clear sky estimates of GHI, DNI, and/or DHI
at this location.
Parameters
----------
times : DatetimeIndex
model : str
The clear sky model to use. Must be one of
'ineichen', 'haurwitz', 'simplified_solis'.
kwargs passed to the relevant functions. Climatological values
are assumed in many cases. See code for details.
Returns
-------
clearsky : DataFrame
Column names are: ``ghi, dni, dhi``.
"""
if model == 'ineichen':
cs = clearsky.ineichen(times, latitude=self.latitude,
longitude=self.longitude,
altitude=self.altitude,
**kwargs)
elif model == 'haurwitz':
solpos = self.get_solarposition(times, **kwargs)
cs = clearsky.haurwitz(solpos['apparent_zenith'])
elif model == 'simplified_solis':
# these try/excepts define default values that are only
# evaluated if necessary. ineichen does some of this internally
try:
dni_extra = kwargs.pop('dni_extra')
except KeyError:
dni_extra = irradiance.extraradiation(times.dayofyear)
try:
pressure = kwargs.pop('pressure')
except KeyError:
pressure = atmosphere.alt2pres(self.altitude)
try:
apparent_elevation = kwargs.pop('apparent_elevation')
except KeyError:
solpos = self.get_solarposition(
times, pressure=pressure, **kwargs)
apparent_elevation = solpos['apparent_elevation']
cs = clearsky.simplified_solis(
apparent_elevation, pressure=pressure, dni_extra=dni_extra,
**kwargs)
else:
raise ValueError('{} is not a valid clear sky model'
.format(model))
return cs
def test_simplified_solis_scalar_elevation():
expected = OrderedDict()
expected['ghi'] = 1064.653145
expected['dni'] = 959.335463
expected['dhi'] = 129.125602
out = clearsky.simplified_solis(80)
for k, v in expected.items():
assert_allclose(expected[k], out[k])
def test_simplified_solis_scalar_elevation():
expected = OrderedDict()
expected['ghi'] = 1064.653145
expected['dni'] = 959.335463
expected['dhi'] = 129.125602
out = clearsky.simplified_solis(80)
for k, v in expected.items():
assert_allclose(expected[k], out[k])
def test_simplified_solis_scalar_neg_elevation():
expected = OrderedDict()
expected['ghi'] = 0
expected['dni'] = 0
expected['dhi'] = 0
out = clearsky.simplified_solis(-10)
for k, v in expected.items():
assert_allclose(expected[k], out[k])
def test_simplified_solis_scalar_neg_elevation():
expected = OrderedDict()
expected['ghi'] = 0
expected['dni'] = 0
expected['dhi'] = 0
out = clearsky.simplified_solis(-10)
for k, v in expected.items():
assert_allclose(expected[k], out[k])
def test_simplified_solis_small_scalar_pw():
expected = OrderedDict()
expected['ghi'] = 1107.84678941
expected['dni'] = 1001.15353307
expected['dhi'] = 128.58887606
out = clearsky.simplified_solis(80, precipitable_water=0.1)
for k, v in expected.items():
assert_allclose(expected[k], out[k])
def test_simplified_solis_small_scalar_pw():
expected = OrderedDict()
expected['ghi'] = 1107.84678941
expected['dni'] = 1001.15353307
expected['dhi'] = 128.58887606
out = clearsky.simplified_solis(80, precipitable_water=0.1)
for k, v in expected.items():
assert_allclose(expected[k], out[k])
def test_simplified_solis_pressure():
expected = pd.DataFrame(np.
array([[ 964.26930718, 1067.96543669, 127.22841797],
[ 961.88811874, 1066.36847963, 128.1402539 ],
[ 959.58112234, 1064.81837558, 129.0304193 ]]),
columns=['dni', 'ghi', 'dhi'])
expected = expected[['ghi', 'dni', 'dhi']]
out = clearsky.simplified_solis(
80, pressure=pd.Series([95000, 98000, 101000]))
assert_frame_equal(expected, out)
def test_simplified_solis_pressure():
expected = pd.DataFrame(np.
array([[ 964.26930718, 1067.96543669, 127.22841797],
[ 961.88811874, 1066.36847963, 128.1402539 ],
[ 959.58112234, 1064.81837558, 129.0304193 ]]),
columns=['dni', 'ghi', 'dhi'])
expected = expected[['ghi', 'dni', 'dhi']]
out = clearsky.simplified_solis(
80, pressure=pd.Series([95000, 98000, 101000]))
assert_frame_equal(expected, out)
def test_simplified_solis_aod700():
expected = pd.DataFrame(np.
array([[ 1056.61710493, 1105.7229086 , 64.41747323],
[ 1007.50558875, 1085.74139063, 102.96233698],
[ 959.3354628 , 1064.65314509, 129.12560167],
[ 342.45810926, 638.63409683, 77.71786575],
[ 55.24140911, 7.5413313 , 0. ]]),
columns=['dni', 'ghi', 'dhi'])
expected = expected[['ghi', 'dni', 'dhi']]
aod700 = pd.Series([0.0, 0.05, 0.1, 1, 10])
out = clearsky.simplified_solis(80, aod700=aod700)
assert_frame_equal(expected, out)
def test_simplified_solis_precipitable_water():
expected = pd.DataFrame(np.
array([[ 1001.15353307, 1107.84678941, 128.58887606],
[ 1001.15353307, 1107.84678941, 128.58887606],
[ 983.51027357, 1089.62306672, 129.08755996],
[ 959.3354628 , 1064.65314509, 129.12560167],
[ 872.02335029, 974.18046717, 125.63581346]]),
columns=['dni', 'ghi', 'dhi'])
expected = expected[['ghi', 'dni', 'dhi']]
out = clearsky.simplified_solis(
80, precipitable_water=pd.Series([0.0, 0.2, 0.5, 1.0, 5.0]))
assert_frame_equal(expected, out)
def test_simplified_solis_precipitable_water():
expected = pd.DataFrame(np.
array([[ 1001.15353307, 1107.84678941, 128.58887606],
[ 1001.15353307, 1107.84678941, 128.58887606],
[ 983.51027357, 1089.62306672, 129.08755996],
[ 959.3354628 , 1064.65314509, 129.12560167],
[ 872.02335029, 974.18046717, 125.63581346]]),
columns=['dni', 'ghi', 'dhi'])
expected = expected[['ghi', 'dni', 'dhi']]
out = clearsky.simplified_solis(
80, precipitable_water=pd.Series([0.0, 0.2, 0.5, 1.0, 5.0]))
assert_frame_equal(expected, out)
def test_simplified_solis_aod700():
expected = pd.DataFrame(np.
array([[ 1056.61710493, 1105.7229086 , 64.41747323],
[ 1007.50558875, 1085.74139063, 102.96233698],
[ 959.3354628 , 1064.65314509, 129.12560167],
[ 342.45810926, 638.63409683, 77.71786575],
[ 55.24140911, 7.5413313 , 0. ]]),
columns=['dni', 'ghi', 'dhi'])
expected = expected[['ghi', 'dni', 'dhi']]
aod700 = pd.Series([0.0, 0.05, 0.1, 1, 10])
out = clearsky.simplified_solis(80, aod700=aod700)
assert_frame_equal(expected, out)
def setup(self, ndays):
self.times = pd.date_range(start='20180601', freq='1min',
periods=1440*ndays)
self.lat = 35.1
self.lon = -106.6
self.solar_position = solarposition.get_solarposition(
self.times, self.lat, self.lon)
clearsky_df = clearsky.simplified_solis(
self.solar_position['apparent_elevation'])
self.clearsky = clearsky_df['ghi']
measured_dni = clearsky_df['dni'].where(
(self.times.hour % 2).astype(bool), 0)
cos_zen = np.cos(np.deg2rad(self.solar_position['apparent_zenith']))
self.measured = measured_dni * cos_zen + clearsky_df['dhi']
self.measured *= 0.98
self.window_length = 10
def test_simplified_solis_series_elevation():
expected = pd.DataFrame(
np.array([[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ],
[ 377.80060035, 79.91931339, 42.77453223],
[ 869.47538184, 706.37903999, 110.05635962],
[ 958.89448856, 1062.44821373, 129.02349236],
[ 913.3209839 , 860.48978599, 118.94598678],
[ 634.01066762, 256.00505836, 72.18396705],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ]]),
columns=['dni', 'ghi', 'dhi'],
index=times_localized)
expected = expected[['dhi', 'dni', 'ghi']]
out = clearsky.simplified_solis(ephem_data['apparent_elevation'])
assert_frame_equal(expected, out)
def test_simplified_solis_series_elevation():
tus = Location(32.2, -111, 'US/Arizona', 700)
times = pd.date_range(start='2014-06-24', end='2014-06-25', freq='3h')
times_localized = times.tz_localize(tus.tz)
ephem_data = solarposition.get_solarposition(times_localized, tus.latitude,
tus.longitude)
expected = pd.DataFrame(
np.array([[0., 0., 0.], [0., 0., 0.],
[377.80060035, 79.91931339, 42.77453223],
[869.47538184, 706.37903999, 110.05635962],
[958.89448856, 1062.44821373, 129.02349236],
[913.3209839, 860.48978599, 118.94598678],
[634.01066762, 256.00505836, 72.18396705], [0., 0., 0.],
[0., 0., 0.]]),
columns=['dni', 'ghi', 'dhi'],
index=times_localized)
expected = expected[['dhi', 'dni', 'ghi']]
out = clearsky.simplified_solis(ephem_data['apparent_elevation'])
assert_frame_equal(expected, out)
def test_simplified_solis_return_arrays():
expected = OrderedDict()
expected['ghi'] = np.array([[ 1148.40081325, 913.42330823],
[ 965.48550828, 760.04527609]])
expected['dni'] = np.array([[ 1099.25706525, 656.24601381],
[ 915.31689149, 530.31697378]])
expected['dhi'] = np.array([[ 64.1063074 , 254.6186615 ],
[ 62.75642216, 232.21931597]])
aod700 = np.linspace(0, 0.5, 2)
precipitable_water = np.linspace(0, 10, 2)
aod700, precipitable_water = np.meshgrid(aod700, precipitable_water)
out = clearsky.simplified_solis(80, aod700, precipitable_water)
for k, v in expected.items():
assert_allclose(expected[k], out[k])
def test_simplified_solis_return_arrays():
expected = OrderedDict()
expected['ghi'] = np.array([[ 1148.40081325, 913.42330823],
[ 965.48550828, 760.04527609]])
expected['dni'] = np.array([[ 1099.25706525, 656.24601381],
[ 915.31689149, 530.31697378]])
expected['dhi'] = np.array([[ 64.1063074 , 254.6186615 ],
[ 62.75642216, 232.21931597]])
aod700 = np.linspace(0, 0.5, 2)
precipitable_water = np.linspace(0, 10, 2)
aod700, precipitable_water = np.meshgrid(aod700, precipitable_water)
out = clearsky.simplified_solis(80, aod700, precipitable_water)
for k, v in expected.items():
assert_allclose(expected[k], out[k])
def test_simplified_solis_series_elevation():
tus = Location(32.2, -111, 'US/Arizona', 700)
times = pd.date_range(start='2014-06-24', end='2014-06-25', freq='3h')
times_localized = times.tz_localize(tus.tz)
ephem_data = solarposition.get_solarposition(times_localized, tus.latitude,
tus.longitude)
expected = pd.DataFrame(
np.array([[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ],
[ 377.80060035, 79.91931339, 42.77453223],
[ 869.47538184, 706.37903999, 110.05635962],
[ 958.89448856, 1062.44821373, 129.02349236],
[ 913.3209839 , 860.48978599, 118.94598678],
[ 634.01066762, 256.00505836, 72.18396705],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ]]),
columns=['dni', 'ghi', 'dhi'],
index=times_localized)
expected = expected[['dhi', 'dni', 'ghi']]
out = clearsky.simplified_solis(ephem_data['apparent_elevation'])
assert_frame_equal(expected, out)
def test_simplified_solis_nans_series():
# construct input arrays that each have 1 nan offset from each other,
# the last point is valid for all arrays
length = 6
apparent_elevation = pd.Series(np.full(length, 80.))
apparent_elevation[0] = np.nan
aod700 = np.full(length, 0.1)
aod700[1] = np.nan
precipitable_water = np.full(length, 0.5)
precipitable_water[2] = np.nan
pressure = np.full(length, 98000.)
pressure[3] = np.nan
dni_extra = np.full(length, 1370.)
dni_extra[4] = np.nan
expected = OrderedDict()
expected['ghi'] = np.full(length, np.nan)
expected['dni'] = np.full(length, np.nan)
expected['dhi'] = np.full(length, np.nan)
expected['ghi'][length-1] = 1096.022736
expected['dni'][length-1] = 990.306854
expected['dhi'][length-1] = 128.664594
expected = pd.DataFrame.from_dict(expected)
out = clearsky.simplified_solis(apparent_elevation, aod700,
precipitable_water, pressure, dni_extra)
assert_frame_equal(expected, out)
def test_simplified_solis_nans_series():
# construct input arrays that each have 1 nan offset from each other,
# the last point is valid for all arrays
length = 6
apparent_elevation = pd.Series(np.full(length, 80.))
apparent_elevation[0] = np.nan
aod700 = np.full(length, 0.1)
aod700[1] = np.nan
precipitable_water = np.full(length, 0.5)
precipitable_water[2] = np.nan
pressure = np.full(length, 98000.)
pressure[3] = np.nan
dni_extra = np.full(length, 1370.)
dni_extra[4] = np.nan
expected = OrderedDict()
expected['ghi'] = np.full(length, np.nan)
expected['dni'] = np.full(length, np.nan)
expected['dhi'] = np.full(length, np.nan)
expected['ghi'][length-1] = 1096.022736
expected['dni'][length-1] = 990.306854
expected['dhi'][length-1] = 128.664594
expected = pd.DataFrame.from_dict(expected)
out = clearsky.simplified_solis(apparent_elevation, aod700,
precipitable_water, pressure, dni_extra)
assert_frame_equal(expected, out)
def test_simplified_solis_nans_arrays():
# construct input arrays that each have 1 nan offset from each other,
# the last point is valid for all arrays
length = 6
apparent_elevation = np.full(length, 80.)
apparent_elevation[0] = np.nan
aod700 = np.full(length, 0.1)
aod700[1] = np.nan
precipitable_water = np.full(length, 0.5)
precipitable_water[2] = np.nan
pressure = np.full(length, 98000.)
pressure[3] = np.nan
dni_extra = np.full(length, 1370.)
dni_extra[4] = np.nan
expected = OrderedDict()
expected['ghi'] = np.full(length, np.nan)
expected['dni'] = np.full(length, np.nan)
expected['dhi'] = np.full(length, np.nan)
expected['ghi'][length-1] = 1096.022736
expected['dni'][length-1] = 990.306854
expected['dhi'][length-1] = 128.664594
out = clearsky.simplified_solis(apparent_elevation, aod700,
precipitable_water, pressure, dni_extra)
for k, v in expected.items():
assert_allclose(expected[k], out[k])
def test_simplified_solis_nans_arrays():
# construct input arrays that each have 1 nan offset from each other,
# the last point is valid for all arrays
length = 6
apparent_elevation = np.full(length, 80.)
apparent_elevation[0] = np.nan
aod700 = np.full(length, 0.1)
aod700[1] = np.nan
precipitable_water = np.full(length, 0.5)
precipitable_water[2] = np.nan
pressure = np.full(length, 98000.)
pressure[3] = np.nan
dni_extra = np.full(length, 1370.)
dni_extra[4] = np.nan
expected = OrderedDict()
expected['ghi'] = np.full(length, np.nan)
expected['dni'] = np.full(length, np.nan)
expected['dhi'] = np.full(length, np.nan)
expected['ghi'][length-1] = 1096.022736
expected['dni'][length-1] = 990.306854
expected['dhi'][length-1] = 128.664594
out = clearsky.simplified_solis(apparent_elevation, aod700,
precipitable_water, pressure, dni_extra)
for k, v in expected.items():
assert_allclose(expected[k], out[k])
def get_clearsky(self,
times,
model='ineichen',
solar_position=None,
dni_extra=None,
**kwargs):
"""
Calculate the clear sky estimates of GHI, DNI, and/or DHI
at this location.
Parameters
----------
times: DatetimeIndex
model: str
The clear sky model to use. Must be one of
'ineichen', 'haurwitz', 'simplified_solis'.
solar_position : None or DataFrame
DataFrame with with columns 'apparent_zenith', 'zenith',
'apparent_elevation'.
dni_extra: None or numeric
If None, will be calculated from times.
kwargs passed to the relevant functions. Climatological values
are assumed in many cases. See source code for details!
Returns
-------
clearsky : DataFrame
Column names are: ``ghi, dni, dhi``.
"""
if dni_extra is None:
dni_extra = irradiance.extraradiation(times)
try:
pressure = kwargs.pop('pressure')
except KeyError:
pressure = atmosphere.alt2pres(self.altitude)
if solar_position is None:
solar_position = self.get_solarposition(times,
pressure=pressure,
**kwargs)
apparent_zenith = solar_position['apparent_zenith']
apparent_elevation = solar_position['apparent_elevation']
if model == 'ineichen':
try:
linke_turbidity = kwargs.pop('linke_turbidity')
except KeyError:
interp_turbidity = kwargs.pop('interp_turbidity', True)
linke_turbidity = clearsky.lookup_linke_turbidity(
times,
self.latitude,
self.longitude,
interp_turbidity=interp_turbidity)
try:
airmass_absolute = kwargs.pop('airmass_absolute')
except KeyError:
airmass_absolute = self.get_airmass(
times, solar_position=solar_position)['airmass_absolute']
cs = clearsky.ineichen(apparent_zenith,
airmass_absolute,
linke_turbidity,
altitude=self.altitude,
dni_extra=dni_extra)
elif model == 'haurwitz':
cs = clearsky.haurwitz(apparent_zenith)
elif model == 'simplified_solis':
cs = clearsky.simplified_solis(apparent_elevation,
pressure=pressure,
dni_extra=dni_extra,
**kwargs)
else:
raise ValueError(
('{} is not a valid clear sky model. Must be ' +
'one of ineichen, simplified_solis, haurwitz').format(model))
return cs
def get_clearsky(self, times, model='ineichen', **kwargs):
"""
Calculate the clear sky estimates of GHI, DNI, and/or DHI
at this location.
Parameters
----------
times : DatetimeIndex
model : str
The clear sky model to use. Must be one of
'ineichen', 'haurwitz', 'simplified_solis'.
kwargs passed to the relevant functions. Climatological values
are assumed in many cases. See code for details.
Returns
-------
clearsky : DataFrame
Column names are: ``ghi, dni, dhi``.
"""
if model == 'ineichen':
cs = clearsky.ineichen(times,
latitude=self.latitude,
longitude=self.longitude,
altitude=self.altitude,
**kwargs)
elif model == 'haurwitz':
solpos = self.get_solarposition(times, **kwargs)
cs = clearsky.haurwitz(solpos['apparent_zenith'])
elif model == 'simplified_solis':
# these try/excepts define default values that are only
# evaluated if necessary. ineichen does some of this internally
try:
dni_extra = kwargs.pop('dni_extra')
except KeyError:
dni_extra = irradiance.extraradiation(times.dayofyear)
try:
pressure = kwargs.pop('pressure')
except KeyError:
pressure = atmosphere.alt2pres(self.altitude)
try:
apparent_elevation = kwargs.pop('apparent_elevation')
except KeyError:
solpos = self.get_solarposition(times,
pressure=pressure,
**kwargs)
apparent_elevation = solpos['apparent_elevation']
cs = clearsky.simplified_solis(apparent_elevation,
pressure=pressure,
dni_extra=dni_extra,
**kwargs)
else:
raise ValueError('{} is not a valid clear sky model'.format(model))
return cs
def get_clearsky(self, times, model='ineichen', solar_position=None,
dni_extra=None, **kwargs):
"""
Calculate the clear sky estimates of GHI, DNI, and/or DHI
at this location.
Parameters
----------
times: DatetimeIndex
model: str, default 'ineichen'
The clear sky model to use. Must be one of
'ineichen', 'haurwitz', 'simplified_solis'.
solar_position : None or DataFrame, default None
DataFrame with columns 'apparent_zenith', 'zenith',
'apparent_elevation'.
dni_extra: None or numeric, default None
If None, will be calculated from times.
kwargs passed to the relevant functions. Climatological values
are assumed in many cases. See source code for details!
Returns
-------
clearsky : DataFrame
Column names are: ``ghi, dni, dhi``.
"""
if dni_extra is None:
dni_extra = irradiance.get_extra_radiation(times)
try:
pressure = kwargs.pop('pressure')
except KeyError:
pressure = atmosphere.alt2pres(self.altitude)
if solar_position is None:
solar_position = self.get_solarposition(times, pressure=pressure,
**kwargs)
apparent_zenith = solar_position['apparent_zenith']
apparent_elevation = solar_position['apparent_elevation']
if model == 'ineichen':
try:
linke_turbidity = kwargs.pop('linke_turbidity')
except KeyError:
interp_turbidity = kwargs.pop('interp_turbidity', True)
linke_turbidity = clearsky.lookup_linke_turbidity(
times, self.latitude, self.longitude,
interp_turbidity=interp_turbidity)
try:
airmass_absolute = kwargs.pop('airmass_absolute')
except KeyError:
airmass_absolute = self.get_airmass(
times, solar_position=solar_position)['airmass_absolute']
cs = clearsky.ineichen(apparent_zenith, airmass_absolute,
linke_turbidity, altitude=self.altitude,
dni_extra=dni_extra, **kwargs)
elif model == 'haurwitz':
cs = clearsky.haurwitz(apparent_zenith)
elif model == 'simplified_solis':
cs = clearsky.simplified_solis(
apparent_elevation, pressure=pressure, dni_extra=dni_extra,
**kwargs)
else:
raise ValueError('{} is not a valid clear sky model. Must be '
'one of ineichen, simplified_solis, haurwitz'
.format(model))
return cs
