def test_compute_climatology_daisy_chain(ds):
dset = open_dataset(ds, decode_times=False)
computed_dset = compute_mon_climatology(dset)
assert isinstance(computed_dset, xr.Dataset)
computed_dset2 = compute_mon_anomaly(computed_dset)
assert isinstance(computed_dset2, xr.Dataset)
computed_dset3 = compute_ann_mean(computed_dset)
assert isinstance(computed_dset3, xr.Dataset)
computed_dset3 = compute_ann_mean(computed_dset2)
assert isinstance(computed_dset3, xr.Dataset)
def test_compute_climatology_multi_drop_time_bound(ds):
dset = open_dataset(ds, decode_times=False)
dset_time_bound = dset.time.attrs['bounds']
dset = dset.drop(dset_time_bound)
del dset.time.attrs['bounds']
computed_dset = compute_mon_climatology(dset)
assert isinstance(computed_dset, xr.Dataset)
assert computed_dset.time.dtype == dset.time.dtype
for key, value in dset.time.attrs.items():
assert key in computed_dset.time.attrs
assert value == computed_dset.time.attrs[key]
computed_dset = compute_ann_mean(dset)
assert isinstance(computed_dset, xr.Dataset)
assert computed_dset.time.dtype == dset.time.dtype
for key, value in dset.time.attrs.items():
assert key in computed_dset.time.attrs
assert value == computed_dset.time.attrs[key]
computed_dset = compute_mon_anomaly(dset)
assert isinstance(computed_dset, xr.Dataset)
assert computed_dset.time.dtype == dset.time.dtype
assert (dset.time.values == computed_dset.time.values).all()
for key, value in dset.time.attrs.items():
assert key in computed_dset.time.attrs
assert value == computed_dset.time.attrs[key]
def test_compute_ann_mean_values_missing(dset):
dset.variable_1.values[0:3, :, :] = np.nan
computed_dset = compute_ann_mean(dset)
assert isinstance(computed_dset, xr.Dataset)
assert computed_dset.time.dtype == dset.time.dtype
expected = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0])
np.testing.assert_equal(computed_dset.variable_1.values.ravel(), expected)
def test_compute_climatology_multi(ds):
dset = open_dataset(ds, decode_times=False)
print(dset)
computed_dset = compute_mon_climatology(dset)
print(computed_dset)
print()
assert isinstance(computed_dset, xr.Dataset)
computed_dset = compute_ann_mean(dset)
print(computed_dset)
print()
assert isinstance(computed_dset, xr.Dataset)
computed_dset = compute_mon_anomaly(dset)
print(computed_dset)
print()
assert isinstance(computed_dset, xr.Dataset)
def test_compute_climatology_multi_decoded(ds):
dset = open_dataset(ds, decode_times=True)
computed_dset = compute_mon_climatology(dset)
assert isinstance(computed_dset, xr.Dataset)
assert computed_dset.time.dtype == dset.time.dtype
for key, value in dset.time.attrs.items():
assert key in computed_dset.time.attrs
assert value == computed_dset.time.attrs[key]
computed_dset = compute_ann_mean(dset)
assert isinstance(computed_dset, xr.Dataset)
assert computed_dset.time.dtype == dset.time.dtype
for key, value in dset.time.attrs.items():
assert key in computed_dset.time.attrs
assert value == computed_dset.time.attrs[key]
computed_dset = compute_mon_anomaly(dset)
assert isinstance(computed_dset, xr.Dataset)
assert computed_dset.time.dtype == dset.time.dtype
assert (dset.time.values == computed_dset.time.values).all()
for key, value in dset.time.attrs.items():
assert key in computed_dset.time.attrs
assert value == computed_dset.time.attrs[key]
def test_compute_ann_mean(dset):
computed_dset = compute_ann_mean(dset)
assert isinstance(computed_dset, xr.Dataset)
expected = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0])
np.testing.assert_equal(computed_dset.variable_1.values.ravel(), expected)