def test_to_annual_accuracy(ts_monthly_da):
"""Tests that weighted sum correctly takes the annual mean."""
data = ts_monthly_da().isel(time=slice(0, 12))
MONTH_LENGTHS = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
manual_sum = []
for i in range(len(data)):
manual_sum.append(data[i].values * MONTH_LENGTHS[i] / 365)
expected = sum(manual_sum)
actual = to_annual(data)
assert np.abs(actual.values - expected) < 1e-5
def test_to_annual_retains_nans(gridded_da_landmask):
"""Tests that `to_annual` function retains nans where the original dataset had nans
.. note::
Previous versions of `esmtools` did not do this, since xarray automatically
skips nans with the grouped sum operator, returning zeroes where there used
to be nans.
"""
data = gridded_da_landmask
data['time'] = xr.cftime_range(
start='1990-01', freq='MS', periods=data['time'].size
)
result = to_annual(data)
assert result.isel(lat=0, lon=0).isnull().all()
def test_to_annual(dataset):
"""General checks that `to_annual` time conversion is working as expected."""
data = dataset()
result = to_annual(data)
assert result.notnull().all()
assert 'year' in result.dims