def test_mrt_direct_exposed():
# TODO: test output
mean_radiant_temperature(
{"rlds": 150, "rlus": 350, "rsdsdiff": 400, "rsus": 100, "rsds": 700},
cos_zenith=0.5,
direct_exposed=0.7,
)
def test_mrt_raises():
with pytest.raises(ValueError):
mean_radiant_temperature({})
with pytest.raises(ValueError):
mean_radiant_temperature(
{
"rlds": 150,
"rlus": np.array([350, 150]),
"rsdsdiff": np.array([400, 200]),
"rsus": np.array([100, 50]),
"rsds": np.array([700, 400]),
}
)
def test_mrt_array():
EXPECTED_RESULT = np.array([313.33875095746555, 291.2519186818613])
TEST_RESULT = mean_radiant_temperature(
{
"rlds": np.array([150, 50]),
"rlus": np.array([350, 150]),
"rsdsdiff": np.array([400, 200]),
"rsus": np.array([100, 50]),
"rsds": np.array([700, 400]),
},
cos_zenith=np.array([0.5, 0.2]),
)
assert np.allclose(TEST_RESULT, EXPECTED_RESULT)
def test_integration_array():
EXPECTED_RESULT = 273.15 + np.array([22.32159032, 18.02267449])
mrt = mean_radiant_temperature(
{
"rlds": np.array([150, 50]),
"rlus": np.array([350, 150]),
"rsdsdiff": np.array([400, 200]),
"rsus": np.array([100, 50]),
"rsds": np.array([700, 400]),
},
cos_zenith=np.array([0.5, 0.2]),
lit=np.array([1, 1]),
)
TEST_RESULT = universal_thermal_climate_index(
{
"tas": np.array([295, 296]),
"sfcWind": np.array([6.0, 6.0]),
"hurs": np.array([100, 100]),
},
mrt,
)
assert np.allclose(TEST_RESULT, EXPECTED_RESULT)
# calculate 3hr-mean solar zenith
mean_cosz = np.ones((ntime, nlat, nlon)) * np.nan
lit = np.ones((ntime, nlat, nlon)) * np.nan
for i in range(ntime):
mean_cosz[i, ...], lit[i, ...] = cos_mean_solar_zenith_angle(mjd[i], 3, lat, lon)
# calculate mean radiant temperature
mrt = mean_radiant_temperature(
{
"rlds": rlds_cube.data,
"rlus": rlus_cube.data,
"rsdsdiff": rsdsdiff_cube.data,
"rsus": rsus_cube.data,
"rsds": rsds_cube.data,
},
angle_factor_down=0.5,
angle_factor_up=0.5,
absorption=0.7,
emissivity=0.97,
cos_zenith=mean_cosz,
lit=lit
)
# regrid wind to temperature grid
# don't think area-weighting makes sense, use bi-linear
uas_cube_regrid = uas_cube.regrid(tas_cube, iris.analysis.Linear())
vas_cube_regrid = vas_cube.regrid(tas_cube, iris.analysis.Linear())
wind = np.sqrt(uas_cube_regrid.data**2 + vas_cube_regrid.data**2)
# calculate UTCI