def test_bad_frequency(self):
with pytest.raises(ValueError):
n = 365
times = pd.date_range("2000-01-01", freq="12H", periods=n)
da = xr.DataArray(np.arange(n), [("time", times)],
attrs={"units": "K"})
tg_mean(da)
def test_Tmean_3d_data(self):
ds_tmax = xr.open_dataset(self.nc_files[0])
ds_tmin = xr.open_dataset(self.nc_files[1])
tas = atmos.tg(ds_tmin.tasmin, ds_tmax.tasmax)
tas_C = atmos.tg(ds_tmin.tasmin, ds_tmax.tasmax)
tas_C.values -= K2C
tas_C.attrs["units"] = "C"
# put a nan somewhere
tas.values[180, 1, 0] = np.nan
tas_C.values[180, 1, 0] = np.nan
tmmean = atmos.tg_mean(tas)
tmmeanC = atmos.tg_mean(tas_C)
x1 = tas.values[:, 0, 0]
tmmean1 = x1.mean()
# TODO: Investigate the differences between the two outputs.
# The conversion to K is done after / before the mean.
np.testing.assert_array_almost_equal(tmmeanC, tmmean, 3)
# test single point vs manual
assert np.allclose(tmmean1, tmmean.values[0, 0, 0],
tmmeanC.values[0, 0, 0])
# test single nan point
assert np.isnan(tmmean.values[0, 1, 0])
# test all nan point
assert np.isnan(tmmean.values[0, -1, -1])
def test_bad_frequency(self):
with pytest.raises(ValueError):
n = 365
times = pd.date_range('2000-01-01', freq='12H', periods=n)
da = xr.DataArray(np.arange(n), [('time', times)],
attrs={'units': 'K'})
tg_mean(da)
def test_duplicate_dates(self):
with pytest.raises(ValueError):
n = 365
times = pd.date_range("2000-01-01", freq="1D", periods=n)
times = times.append(
pd.date_range("2000-12-29", freq="1D", periods=n))
da = xr.DataArray(np.arange(2 * n), [("time", times)],
attrs={"units": "K"})
tg_mean(da)
def test_missing_one_day_between_two_years(self):
with pytest.raises(ValueError):
n = 365
times = pd.date_range("2000-01-01", freq="1D", periods=n)
times = times.append(
pd.date_range("2001-01-01", freq="1D", periods=n))
da = xr.DataArray(np.arange(2 * n), [("time", times)],
attrs={"units": "K"})
tg_mean(da)
def test_duplicate_dates(self):
with pytest.raises(ValueError):
n = 365
times = pd.date_range('2000-01-01', freq='1D', periods=n)
times = times.append(
pd.date_range('2000-12-29', freq='1D', periods=n))
da = xr.DataArray(np.arange(2 * n), [('time', times)],
attrs={'units': 'K'})
tg_mean(da)
def test_missing_one_day_between_two_years(self):
with pytest.raises(ValueError):
n = 365
times = pd.date_range('2000-01-01', freq='1D', periods=n)
times = times.append(
pd.date_range('2001-01-01', freq='1D', periods=n))
da = xr.DataArray(np.arange(2 * n), [('time', times)],
attrs={'units': 'K'})
tg_mean(da)
def test_indicator_output(tas_series):
tas = tas_series(np.zeros(365))
with set_options(metadata_locales=["fr"]):
tgmean = atmos.tg_mean(tas, freq="YS")
assert "long_name_fr" in tgmean.attrs
assert (tgmean.attrs["description_fr"] ==
"Moyenne annuelle de la température journalière moyenne")
def test_assert_daily(self):
n = 365 # one day short of a full year
times = pd.date_range("2000-01-01", freq="1D", periods=n)
da = xr.DataArray(np.arange(n), [("time", times)],
attrs={"units": "K"})
tg_mean(da)
def test_assert_daily(self):
n = 365 # one day short of a full year
times = pd.date_range('2000-01-01', freq='1D', periods=n)
da = xr.DataArray(np.arange(n), [('time', times)],
attrs={'units': 'K'})
tg_mean(da)
