def test_unsupported_args(dset):
with pytest.raises(ValueError):
esmlab.resample(dset, freq='hr')
with pytest.raises(ValueError):
esmlab.climatology(dset, freq='hr')
with pytest.raises(ValueError):
esmlab.anomaly(dset, clim_freq='ann')
def test_resample_mon_mean(dset):
for method in {'left', 'right'}:
ds = esmlab.resample(dset, freq='mon', method=method)
assert len(ds.time) == 12
computed_dset = esmlab.resample(dset, freq='mon')
res = computed_dset.variable_1.data
expected = np.full(shape=(12, 2, 2), fill_value=0.5, dtype=np.float32)
np.testing.assert_allclose(res, expected)
def test_resample_ann_mean(dset):
weights = np.ones(24)
computed_dset = esmlab.resample(dset, freq='ann', weights=weights)
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, 1, 1, 1], dtype=np.float32)
np.testing.assert_allclose(computed_dset.variable_1.values.ravel().astype(
np.float32),
expected,
rtol=1e-6)
computed_dset = esmlab.resample(dset, freq='ann', weights=None)
assert isinstance(computed_dset, xr.Dataset)
with pytest.raises(ValueError):
esmlab.resample(dset, freq='ann', weights=[1, 2])
def test_resample_ann_mean_values_missing(ds, weights):
dset = open_dataset(ds, decode_times=False, decode_coords=False)
dset.variable_2.data[0:3, :, :] = np.nan
computed_dset = esmlab.resample(dset, freq='ann', weights=weights)
assert isinstance(computed_dset, xr.Dataset)
assert computed_dset.time.dtype == dset.time.dtype
expected = np.array([1.0, 1.0, 1.0, 1.0, 0, 0, 0, 0], dtype=np.float32)
np.testing.assert_allclose(computed_dset.variable_2.data.ravel().astype(
np.float32),
expected,
rtol=1e-6)
def compute_ann_mean(ds):
"""esmlab wrapper"""
# return esmlab.climatology.compute_ann_mean(ds)
# ignore certain warnings
with warnings.catch_warnings():
warnings.filterwarnings(action="ignore", module=".*nanops")
ds_out = resample(ds, freq="ann")
# esmlab appears to corrupt xarray indexes wrt time
# the following seems to reset them
ds_out["time"] = ds_out["time"]
# ensure time dim is first on time.bounds variable
tb_name = ds_out.time.bounds
if ds_out[tb_name].dims[0] != "time":
ds_out[tb_name] = ds_out[tb_name].transpose()
# reset time to midpoint
ds_out = time_set_mid(ds_out, "time")
# propagate particular encoding values
for key in ["unlimited_dims"]:
if key in ds.encoding:
ds_out.encoding[key] = ds.encoding[key]
# copy file attributes
for key in ds.attrs:
if key != "history":
ds_out.attrs[key] = ds.attrs[key]
# append to history file attribute if it already exists, otherwise set it
key = "history"
datestamp = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S %Z")
msg = (
f"{datestamp}: created by esmlab.resample, with modifications from esmlab_wrap"
)
if key in ds.attrs:
ds_out.attrs[key] = "\n".join([msg, ds.attrs[key]])
else:
ds_out.attrs[key] = msg
return ds_out
def test_datetime_cftime():
ds = esmlab.datasets.open_dataset('mpi_som_ffn_gcb2018',
decode_times=False)
res = esmlab.resample(ds, freq='ann')
assert isinstance(res, xr.Dataset)
def test_datetime_cftime_exception():
ds = esmlab.datasets.open_dataset('mpi_som_ffn_gcb2018', decode_times=True)
with pytest.raises(TypeError):
esmlab.resample(ds, freq='ann')
def test_resample_mon_mean(dset):
computed_dset = esmlab.resample(dset, freq='mon')
res = computed_dset.variable_1.data
expected = np.full(shape=(12, 2, 2), fill_value=0.5, dtype=np.float32)
np.testing.assert_allclose(res, expected)