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_regrid_regrid():
R = regridder(name_grid_src="T62",
name_grid_dst="CAM_f09",
method="bilinear",
overwrite_existing=False)
ds = open_dataset(name="ncep_forecast_tseries")
dao = R(ds.t_10)
print(dao)
assert isinstance(dao, xr.DataArray)
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 test_weighted_sum_float32():
from esmlab.datasets import open_dataset
with open_dataset('cesm_pop_monthly') as ds:
weights = ds['TAREA'].astype(np.float32)
tmp_data = ds['TLAT']
tmp_data.values = np.where(np.greater_equal(ds['KMT'].values, 1),
ds['TLAT'], np.nan)
tmp_data = tmp_data.astype(np.float32)
w_sum = esmlab.weighted_sum(tmp_data, dim=None, weights=weights)
assert tmp_data.attrs == w_sum.attrs
assert tmp_data.encoding == w_sum.encoding
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(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_regrid_regrid(grid_src, grid_dst, method, data_in, xr_type):
R = regridder(name_grid_src=grid_src,
name_grid_dst=grid_dst,
method=method,
overwrite_existing=False)
ds = open_dataset(name=data_in)
dao = R(ds.t_10)
assert isinstance(dao, xr_type)
assert dao.data.shape == (1, 192, 288)
with pytest.raises(NotImplementedError):
dao = R(ds)
with pytest.raises(ValueError):
a = np.ones(10)
dao = R(a)
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_open_dataset_cache():
ds = open_dataset('ncep_forecast_tseries', cache=False)
assert isinstance(ds, xr.Dataset)
filepath = _default_cache_dir + '/ncep_forecast_tseries.nc'
assert not os.path.exists(os.path.abspath(filepath))
def test_open_dataset():
ds = open_dataset('cesm_pop_yearly')
assert isinstance(ds, xr.Dataset)
def test_open_dataset():
ds = open_dataset('cesm_cice_daily')
assert isinstance(ds, xr.Dataset)
