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_mon_climatology_drop_time_bounds(ds_name, decoded, variables,
time_coord_name, time_bound_name):
ds = esmlab.datasets.open_dataset(ds_name, decode_times=decoded)
ds_time_bound = ds[time_coord_name].attrs[time_bound_name]
ds = ds.drop(ds_time_bound)
del ds[time_coord_name].attrs[time_bound_name]
esm = ds.esmlab.set_time(time_coord_name=time_coord_name)
computed_dset = esmlab.climatology(ds, freq='mon')
esmlab_res = computed_dset.drop(esm.static_variables).to_dataframe()
esmlab_res = esmlab_res.groupby('month').mean()[variables]
df = (esm._ds_time_computed.drop(
esm.static_variables).to_dataframe().reset_index(
level=[esm.time_coord_name]))
df[esm.time_coord_name] = df[esm.time_coord_name].values.astype(
'datetime64[ns]')
pd_res = df.groupby(df[esm.time_coord_name].dt.month).mean()
pd_res.index.name = 'month'
pd_res = pd_res[variables]
assert_both_frames_equal(esmlab_res, pd_res)
assert computed_dset[esm.time_coord_name].dtype == ds[
esm.time_coord_name].dtype
for key, value in ds[esm.time_coord_name].attrs.items():
assert key in computed_dset[esm.time_coord_name].attrs
assert value == computed_dset[esm.time_coord_name].attrs[key]
def test_mon_climatology(ds_name, decoded, variables, time_coord_name):
ds = esmlab.datasets.open_dataset(ds_name, decode_times=decoded)
esm = ds.esmlab.set_time(time_coord_name=time_coord_name)
xr.testing._assert_internal_invariants(esm._ds_time_computed)
computed_dset = esmlab.climatology(ds, freq='mon')
esmlab_res = computed_dset.to_dataframe()
esmlab_res = esmlab_res.groupby('month').mean()[variables]
df = (esm._ds_time_computed.drop(
esm.static_variables).to_dataframe().reset_index(
level=[esm.time_coord_name]))
df[esm.time_coord_name] = df[esm.time_coord_name].values.astype(
'datetime64[ns]')
pd_res = df.groupby(df[esm.time_coord_name].dt.month).mean()
pd_res.index.name = 'month'
pd_res = pd_res[variables]
assert_both_frames_equal(esmlab_res, pd_res)
def compute_clim_mon(ds_in, yr_lo, yr_hi, time_name='time'):
"""compute and return monthly climatology"""
yyyymmdd_lo = yr_lo + '-01-01'
yyyymmdd_hi = yr_hi + '-12-31'
ds_clim = esmlab.climatology(
ds_in.sel(time=slice(yyyymmdd_lo, yyyymmdd_hi)), freq='mon')
# set essential time encoding values from ds_in
# as esmlab.climatology only sets dtype and _FillValue
for attr_name in ['units', 'calendar']:
ds_clim[time_name].encoding[attr_name] = ds_in[time_name].encoding[
attr_name]
# copy global attributes from ds_in
ds_clim.attrs.update(ds_in.attrs)
return ds_clim