def test_single_year(self, tas_series):
a = np.zeros(366) + K2C
ts = tas_series(a, start="1/1/2000")
tt = (ts.time.dt.month >= 5) & (ts.time.dt.month <= 8)
offset = np.random.uniform(low=5.5, high=23, size=(tt.sum().values, ))
ts[tt] = ts[tt] + offset
out = atmos.growing_season_length(ts)
np.testing.assert_array_equal(out, tt.sum())
def test_convert_units(self, tas_series):
a = np.zeros(366)
ts = tas_series(a, start='1/1/2000')
ts.attrs['units'] = 'C'
tt = (ts.time.dt.month >= 5) & (ts.time.dt.month <= 8)
offset = np.random.uniform(low=5.5, high=23, size=(tt.sum().values, ))
ts[tt] = ts[tt] + offset
out = atmos.growing_season_length(ts)
np.testing.assert_array_equal(out, tt.sum())
def test_multiyear(self, tas_series):
a = np.zeros(366 * 10)
ts = tas_series(a, start="1/1/2000")
ts.attrs["units"] = "C"
tt = (ts.time.dt.month >= 5) & (ts.time.dt.month <= 8)
offset = np.random.uniform(low=5.5, high=23, size=(tt.sum().values,))
ts[tt] = ts[tt] + offset
out = atmos.growing_season_length(ts)
np.testing.assert_array_equal(out[3], tt[0:366].sum().values)
def test_nan_presence(self, tas_series):
a = np.zeros(366)
a[50] = np.nan
ts = tas_series(a, start="1/1/2000")
ts.attrs["units"] = "C"
tt = (ts.time.dt.month >= 5) & (ts.time.dt.month <= 8)
offset = np.random.uniform(low=5.5, high=23, size=(tt.sum().values,))
ts[tt] = ts[tt] + offset
out = atmos.growing_season_length(ts)
np.testing.assert_array_equal(out, [np.nan])
