temps = np.array([0, 10, 5])

irrads = np.array([0, 500, 0])

winds = np.array([10, 5, 0])

cell_temps = temperature.sapm_cell(irrads, temps, winds, sapm_default['a'],

sapm_default['b'],

sapm_default['deltaT'])

module_temps = temperature.sapm_module(irrads, temps, winds,

sapm_default['a'],

sapm_default['b'])

expected_cell = np.array([0., 23.06066166, 5.])

expected_module = np.array([0., 21.56066166, 5.])

assert_allclose(expected_cell, cell_temps, 1e-3)

times = pd.date_range(start='2015-01-01', end='2015-01-02', freq='12h')

temps = pd.Series([0, 10, 5], index=times)

irrads = pd.Series([0, 500, 0], index=times)

winds = pd.Series([10, 5, 0], index=times)

cell_temps = temperature.sapm_cell(irrads, temps, winds, sapm_default['a'],

sapm_default['b'],

sapm_default['deltaT'])

module_temps = temperature.sapm_module(irrads, temps, winds,

sapm_default['a'],

sapm_default['b'])

expected_cell = pd.Series([0., 23.06066166, 5.], index=times)

expected_module = pd.Series([0., 21.56066166, 5.], index=times)

assert_series_equal(expected_cell, cell_temps)

temps = np.array([0, 10, 5])

irrads = np.array([0, 500, 0])

winds = np.array([10, 5, 0])

result = temperature.pvsyst_cell(irrads, temps, wind_speed=winds)

expected = np.array([0.0, 23.965517, 5.0])

times = pd.date_range(start="2015-01-01", end="2015-01-02", freq="12h")

temps = pd.Series([0, 10, 5], index=times)

irrads = pd.Series([0, 500, 0], index=times)

winds = pd.Series([10, 5, 0], index=times)

result = temperature.pvsyst_cell(irrads, temps, wind_speed=winds)

expected = pd.Series([0.0, 23.965517, 5.0], index=times)

temps = [0, 10, 5]

irrads = [0, 500, 0]

winds = [10, 5, 0]

result = temperature.faiman(irrads, temps, wind_speed=winds)

expected = np.array([0.0, 18.446, 5.0])

temps = np.array([0, 10, 5])

irrads = np.array([0, 500, 0])

winds = np.array([10, 5, 0])

result = temperature.faiman(irrads, temps, wind_speed=winds)

expected = np.array([0.0, 18.446, 5.0])

ir_down = np.array([0, 100, 200, 315.6574, 400])

expected = [-11.111, -7.591, -4.071, -0.000, 2.969]

result = temperature.faiman_rad(0, 0, 0, ir_down)

assert_allclose(result, expected, atol=0.001)

sky_view = np.array([1.0, 0.5, 0.0])

expected = [-4.071, -2.036, 0.000]

result = temperature.faiman_rad(0, 0, 0, ir_down=200,

sky_view=sky_view)

assert_allclose(result, expected, atol=0.001)

emissivity = np.array([1.0, 0.88, 0.5, 0.0])

expected = [-4.626, -4.071, -2.313, 0.000]

result = temperature.faiman_rad(0, 0, 0, ir_down=200,

emissivity=emissivity)

result = temperature.ross(np.array([1000., 600., 1000.]),

np.array([20., 40., 60.]),

np.array([40., 100., 20.]))

expected = np.array([45., 100., 60.])

times = pd.date_range(start="2015-01-01", end="2015-01-02", freq="12h")

temps = pd.Series([0, 10, 5], index=times)

irrads = pd.Series([0, 500, 0], index=times)

winds = pd.Series([10, 5, 0], index=times)

result = temperature.faiman(irrads, temps, wind_speed=winds)

expected = pd.Series([0.0, 18.446, 5.0], index=times)

params = temperature._temperature_model_params('sapm',

'open_rack_glass_glass')

assert params == temperature.TEMPERATURE_MODEL_PARAMETERS['sapm'][

'open_rack_glass_glass']

with pytest.raises(KeyError):

df = pd.read_csv(filename,

skiprows=17, # ignore location/simulation metadata

skipfooter=1, # ignore "Totals" row

engine='python')

df['Year'] = 2019

df.index = pd.to_datetime(df[['Year', 'Month', 'Day', 'Hour']])

# Test against data exported from pvwatts.nrel.gov

data = _read_pvwatts_8760(TESTS_DATA_DIR / filename)

data = data.iloc[:24*7, :] # just use one week

inputs = {

'poa_global': data['Plane of Array Irradiance (W/m^2)'],

'temp_air': data['Ambient Temperature (C)'],

'wind_speed': data['Wind Speed (m/s)'],

'noct_installed': inoct,

}

expected_tcell = data['Cell Temperature (C)']

expected_tcell.name = 'tmod'

actual_tcell = temperature.fuentes(**inputs)

# the SSC implementation of PVWatts diverges from the Fuentes model at

# at night by setting Tcell=Tamb when POA=0. This not only means that

# nighttime values are slightly different (Fuentes models cooling to sky

# at night), but because of the thermal inertia, there is a transient

# error after dawn as well. Test each case separately:

is_night = inputs['poa_global'] == 0

is_dawn = is_night.shift(1) & ~is_night

is_daytime = (inputs['poa_global'] > 0) & ~is_dawn

# the accuracy is probably higher than 3 digits here, but the PVWatts

# export data has low precision so can only test up to 3 digits

assert_series_equal(expected_tcell[is_daytime].round(3),

actual_tcell[is_daytime].round(3))

# use lower precision for dawn times to accommodate the dawn transient

error = actual_tcell[is_dawn] - expected_tcell[is_dawn]

assert (error.abs() < 0.1).all()

# sanity check on night values -- Fuentes not much lower than PVWatts

night_difference = expected_tcell[is_night] - actual_tcell[is_night]

assert night_difference.max() < 6

index = pd.date_range('2019-01-01', freq='h', periods=3, tz=tz)

df = pd.DataFrame({'poa_global': 1000, 'temp_air': 20, 'wind_speed': 1},

index)

out = temperature.fuentes(df['poa_global'], df['temp_air'],

df['wind_speed'], noct_installed=45)

assert_series_equal(out, pd.Series([47.85, 50.85, 50.85], index=index,

poa_global, temp_air, wind_speed, noct, module_efficiency = (

1000., 25., 1., 45., 0.2)

expected = 55.230790492

result = temperature.noct_sam(poa_global, temp_air, wind_speed, noct,

module_efficiency)

assert_allclose(result, expected)

# test with different types

result = temperature.noct_sam(np.array(poa_global), np.array(temp_air),

np.array(wind_speed), np.array(noct),

np.array(module_efficiency))

assert_allclose(result, expected)

dr = pd.date_range(start='2020-01-01 12:00:00', end='2020-01-01 13:00:00',

freq='1h')

result = temperature.noct_sam(pd.Series(index=dr, data=poa_global),

pd.Series(index=dr, data=temp_air),

pd.Series(index=dr, data=wind_speed),

pd.Series(index=dr, data=noct),

module_efficiency)

# test is constructed to reproduce output from SAM v2020.11.29.

# SAM calculation is the default Detailed PV System model (CEC diode model,

# NOCT cell temperature model), with the only change being the soiling

# loss is set to 0. Weather input is TMY3 for Phoenix AZ.

# Values are taken from the Jan 1 12:00:00 timestamp.

poa_total, temp_air, wind_speed, noct, module_efficiency = (

860.673, 25, 3, 46.4, 0.20551)

poa_total_after_refl = 851.458 # from SAM output

# compute effective irradiance

# spectral loss coefficients fixed in lib_cec6par.cpp

a = np.flipud([0.918093, 0.086257, -0.024459, 0.002816, -0.000126])

# reproduce SAM air mass calculation

zen = 56.4284

elev = 358

air_mass = 1. / (tools.cosd(zen) + 0.5057 * (96.080 - zen)**-1.634)

air_mass *= np.exp(-0.0001184 * elev)

f1 = np.polyval(a, air_mass)

effective_irradiance = f1 * poa_total_after_refl

transmittance_absorptance = 0.9

array_height = 1

mount_standoff = 4.0

result = temperature.noct_sam(poa_total, temp_air, wind_speed, noct,

module_efficiency, effective_irradiance,

transmittance_absorptance, array_height,

mount_standoff)

expected = 43.0655

# rtol from limited SAM output precision

poa_global, temp_air, wind_speed, noct, module_efficiency = (

1000., 25., 1., 45., 0.2)

effective_irradiance = 1100.

transmittance_absorptance = 0.8

array_height = 2

mount_standoff = 2.0

result = temperature.noct_sam(poa_global, temp_air, wind_speed, noct,

module_efficiency, effective_irradiance,

transmittance_absorptance, array_height,

mount_standoff)

expected = 60.477703576

# test against values calculated using pvl_MAmodel_2, see pvlib #1081

times = pd.date_range('2019-01-01', freq='5min', periods=8)

cell_temperature = pd.Series([0, 1, 3, 6, 10, 15, 21, 27], index=times)

wind_speed = pd.Series([0, 1, 2, 3, 2, 1, 2, 3])

# default coeffs

expected = pd.Series([0, 0, 0.7047457, 2.21176412, 4.45584299, 7.63635512,

12.26808265, 18.00305776], index=times)

actual = temperature.prilliman(cell_temperature, wind_speed, unit_mass=10)

assert_series_equal(expected, actual)

# custom coeffs

coefficients = [0.0046, 4.5537e-4, -2.2586e-4, -1.5661e-5]

expected = pd.Series([0, 0, 0.70716941, 2.2199537, 4.47537694, 7.6676931,

12.30423167, 18.04215198], index=times)

actual = temperature.prilliman(cell_temperature, wind_speed, unit_mass=10,

coefficients=coefficients)

assert_series_equal(expected, actual)

# even very short inputs < 20 minutes total still work

times = pd.date_range('2019-01-01', freq='1min', periods=8)

cell_temperature = pd.Series([0, 1, 3, 6, 10, 15, 21, 27], index=times)

wind_speed = pd.Series([0, 1, 2, 3, 2, 1, 2, 3])

expected = pd.Series([0, 0, 0.53557976, 1.49270094, 2.85940173,

4.63914366, 7.09641845, 10.24899272], index=times)

actual = temperature.prilliman(cell_temperature, wind_speed, unit_mass=12)

# if the input series time step is >= 20 min, input is returned unchanged,

# and a warning is emitted

times = pd.date_range('2019-01-01', freq='30min', periods=3)

cell_temperature = pd.Series([0, 1, 3], index=times)

wind_speed = pd.Series([0, 1, 2])

msg = re.escape("temperature.prilliman only applies smoothing when the "

"sampling interval is shorter than 20 minutes (input "

"sampling interval: 30.0 minutes); returning "

"input temperature series unchanged")

with pytest.warns(UserWarning, match=msg):

actual = temperature.prilliman(cell_temperature, wind_speed)

# nans in inputs are handled appropriately; nans in input tcell

# are ignored but nans in wind speed cause nan in output

times = pd.date_range('2019-01-01', freq='1min', periods=8)

cell_temperature = pd.Series([0, 1, 3, 6, 10, np.nan, 21, 27], index=times)

wind_speed = pd.Series([0, 1, 2, 3, 2, 1, np.nan, 3])

actual = temperature.prilliman(cell_temperature, wind_speed)

expected = pd.Series([True, True, True, True, True, True, False, True],

index=times)

assert_series_equal(actual.notnull(), expected)

# check that nan temperatures do not mess up the weighted average;

# the original implementation did not set weight=0 for nan values,

# so the numerator of the weighted average ignored nans but the

# denominator (total weight) still included the weight for the nan.

cell_temperature = pd.Series([1, 1, 1, 1, 1, np.nan, 1, 1], index=times)

wind_speed = pd.Series(1, index=times)

actual = temperature.prilliman(cell_temperature, wind_speed)

# original implementation would return some values < 1 here

expected = pd.Series(1., index=times)

# it is easiest and sufficient to test conversion from & to the same model

glm = temperature.GenericLinearModel(module_efficiency=0.1,

absorptance=0.9)

inp = {'u0': 25.0, 'u1': 6.84}

glm.use_faiman(**inp)

out = glm.to_faiman()

for k, v in inp.items():

assert np.isclose(out[k], v)

inp = {'u_c': 25, 'u_v': 4}

glm.use_pvsyst(**inp)

out = glm.to_pvsyst()

for k, v in inp.items():

assert np.isclose(out[k], v)

# test with optional parameters

inp = {'u_c': 25, 'u_v': 4,

'module_efficiency': 0.15,

'alpha_absorption': 0.95}

glm.use_pvsyst(**inp)

out = glm.to_pvsyst()

for k, v in inp.items():

assert np.isclose(out[k], v)

inp = {'noct': 47}

glm.use_noct_sam(**inp)

out = glm.to_noct_sam()

for k, v in inp.items():

assert np.isclose(out[k], v)

# test with optional parameters

inp = {'noct': 47,

'module_efficiency': 0.15,

'transmittance_absorptance': 0.95}

glm.use_noct_sam(**inp)

out = glm.to_noct_sam()

for k, v in inp.items():

assert np.isclose(out[k], v)

inp = {'a': -3.5, 'b': -0.1}

glm.use_sapm(**inp)

out = glm.to_sapm()

for k, v in inp.items():

glm = temperature.GenericLinearModel(module_efficiency=0.1,

absorptance=0.9)

wind = np.array([1.4, 1/.51, 5.4])

weather = (800, 20, wind)

inp = {'u0': 20.0, 'u1': 5.0}

glm.use_faiman(**inp)

out = glm(*weather)

expected = temperature.faiman(*weather, **inp)

assert np.allclose(out, expected)

out = glm(*weather)

assert np.allclose(out, expected)

out = glm(*weather, module_efficiency=0.1)

assert np.allclose(out, expected)

inp = glm.get_generic_linear()

out = temperature.generic_linear(*weather, **inp)

glm = temperature.GenericLinearModel(module_efficiency=0.1,

absorptance=0.9)

inp = {'u0': 20.0, 'u1': 5.0}

glm.use_faiman(**inp)

expected = ("GenericLinearModel: {"

"'u_const': 16.0, "

"'du_wind': 4.0, "

"'eta': 0.1, "

"'alpha': 0.9}")