def test_grid_transform_noname_targetdata():
"""Check handling of a `target_data` input without name"""
(
source,
grid_kwargs,
target,
transform_kwargs,
_,
_,
) = construct_test_source_data(cases["linear_depth_dens"])
axis = list(grid_kwargs["coords"].keys())[0]
grid = Grid(source, periodic=False, **grid_kwargs)
source_da = source.data
target_data = transform_kwargs.pop("target_data")
target_data.name = None
# the name of target_data is only used if `target` is provided as numpy array
target = target.data
# the high level routines should be able to deal with all cases (no error flag exception like in the mid level)
with pytest.warns(UserWarning):
transformed = grid.transform(source_da,
axis,
target,
target_data=target_data,
**transform_kwargs)
"TRANSFORMED_DIMENSION" in transformed.dims
def test_grid_transform_multidim_other_dims_error(request, multidim_cases):
# broadcast the 1d column agains some other dims and make sure that the 1d results are still valid
source, grid_kwargs, target, transform_kwargs, expected, error_flag = multidim_cases
na = 3
source = source * xr.DataArray(np.ones([na]), dims=["a"])
# broadcast the target, but in this case
# rename one of the dimensions of the target array, which is not along the
# axis of transformation (this could be the case if e.g. temperature is on a different
# x grid than velocity)
target_data = transform_kwargs.pop("target_data", None)
if target_data is not None:
target_data = target_data * xr.DataArray(np.ones([na]),
dims=["a_other"])
# calculate the multidimensional result
axis = list(grid_kwargs["coords"].keys())[0]
grid = Grid(source, periodic=False, **grid_kwargs)
with pytest.raises(ValueError):
_ = grid.transform(source.data,
axis,
target,
target_data=target_data,
**transform_kwargs)
else:
# When target_data is none its taken as a 1D-coordinate, no checking needed
pytest.skip()
def test_grid_transform_multidim(request, client, multidim_cases):
# broadcast the 1d column agains some other dims and make sure that the 1d results are still valid
source, grid_kwargs, target, transform_kwargs, expected, error_flag = multidim_cases
na = 8
source = source.expand_dims(a=na).copy(deep=True)
# broadcast the target_data manually
target_data = transform_kwargs.pop("target_data", None)
if target_data is not None:
target_data = target_data.expand_dims(a=na).copy(deep=True)
if client != "no_client":
target_data = target_data.chunk({"a": 1})
if client != "no_client":
source = source.chunk({"a": 1})
# calculate the multidimensional result
axis = list(grid_kwargs["coords"].keys())[0]
grid = Grid(source, periodic=False, **grid_kwargs)
# the high level tests should deal with all error cases
client = request.getfixturevalue(client)
transformed = grid.transform(source.data,
axis,
target,
target_data=target_data,
**transform_kwargs).load()
_, expected_broadcasted = xr.broadcast(transformed, expected)
xr.testing.assert_allclose(transformed, expected_broadcasted.data)
def test_grid_transform_bypass_checks(bypass_checks):
"""Check that the bypass checks option still delivers the right results for monotonically increasing data"""
(
source,
grid_kwargs,
target,
transform_kwargs,
expected,
_,
) = construct_test_source_data(cases["linear_depth_dens"])
axis = list(grid_kwargs["coords"].keys())[0]
grid = Grid(source, periodic=False, **grid_kwargs)
target_data = transform_kwargs.pop("target_data", None)
transformed = grid.transform(
source.data,
axis,
target,
target_data=target_data,
bypass_checks=bypass_checks,
**transform_kwargs
)
xr.testing.assert_allclose(transformed, expected.data)
def test_transform_error_periodic(multidim_cases):
source, grid_kwargs, target, transform_kwargs, expected, error_flag = multidim_cases
axis = list(grid_kwargs["coords"].keys())[0]
grid = Grid(source, **grid_kwargs)
with pytest.raises(ValueError):
_ = grid.transform(source.data, axis, target, **transform_kwargs)
def test_grid_transform_input_check():
(
source,
grid_kwargs,
target,
transform_kwargs,
_,
_,
) = construct_test_source_data(cases["linear_depth_dens"])
axis = list(grid_kwargs["coords"].keys())[0]
grid = Grid(source, periodic=False, **grid_kwargs)
# construct output name
transform_kwargs.setdefault("suffix", "")
# Make sure that a sensible error is raised if xr.Dataset is provided
# for either one of `source`, `target` or `target_data` input arguments.
match_msg = r"needs to be a"
with pytest.raises(ValueError, match=r"`da` " + match_msg):
grid.transform(source, axis, target, **transform_kwargs)
with pytest.raises(ValueError, match=match_msg):
grid.transform(source.data, axis, target.to_dataset(name="dummy"),
**transform_kwargs)
transform_kwargs["target_data"] = transform_kwargs[
"target_data"].to_dataset(name="dummy")
with pytest.raises(ValueError, match=match_msg):
grid.transform(source.data, axis, target, **transform_kwargs)
def test_grid_transform_noname(multidim_cases):
source, grid_kwargs, target, transform_kwargs, expected, error_flag = multidim_cases
axis = list(grid_kwargs["coords"].keys())[0]
grid = Grid(source, periodic=False, **grid_kwargs)
source_da = source.data
source_da.name = None
# the high level routines should be able to deal with all cases (no error flag exception like in the mid level)
transformed = grid.transform(source_da, axis, target, **transform_kwargs)
assert transformed.name is None
def test_grid_transform(all_cases):
source, grid_kwargs, target, transform_kwargs, expected, error_flag = all_cases
axis = list(grid_kwargs["coords"].keys())[0]
grid = Grid(source, periodic=False, **grid_kwargs)
# construct output name
transform_kwargs.setdefault("suffix", "")
output_name = "data" + transform_kwargs["suffix"]
# the high level routines should be able to deal with all cases (no error flag exception like in the mid level)
transformed = grid.transform(source.data, axis, target, **transform_kwargs)
xr.testing.assert_allclose(transformed, expected[output_name])
def test_conservative_interp_warn():
(
source,
grid_kwargs,
target,
transform_kwargs,
_,
_,
) = construct_test_source_data(cases["conservative_depth_temp"])
axis = list(grid_kwargs["coords"].keys())[0]
grid = Grid(source, periodic=False, **grid_kwargs)
with pytest.warns(UserWarning):
_ = grid.transform(source.data, axis, target, **transform_kwargs)
def test_chunking_dim_error():
"""Assure that error is raised when we chunk along the 'vertical' dimension"""
(
source,
grid_kwargs,
target,
transform_kwargs,
_,
_,
) = construct_test_source_data(cases["linear_depth_dens"])
source = source.chunk({"depth": 1})
axis = list(grid_kwargs["coords"].keys())[0]
grid = Grid(source, periodic=False, **grid_kwargs)
with pytest.raises(ValueError):
_ = grid.transform(source.data, axis, target, **transform_kwargs)
def test_grid_transform_auto_naming(multidim_cases): # only test a few cases
"""Check that the naming for the new dimension is adapted for the output if the target is not passed as xr.Dataarray"""
source, grid_kwargs, target, transform_kwargs, expected, error_flag = multidim_cases
axis = list(grid_kwargs["coords"].keys())[0]
grid = Grid(source, periodic=False, **grid_kwargs)
# modify the expected naming and convert target to numpy array
target_data = transform_kwargs.setdefault("target_data", None)
if transform_kwargs["target_data"] is None:
# When no target_data is provided default to axis coordinates depending on method
if transform_kwargs["method"] == "linear":
expected_data_coord = grid.axes[axis].coords["center"]
elif transform_kwargs["method"] == "conservative":
expected_data_coord = grid.axes[axis].coords["outer"]
else:
# When target_data is provided check against the name of the dataarray
expected_data_coord = target_data.name
target = target.data
transformed = grid.transform(source.data, axis, target, **transform_kwargs)
assert expected_data_coord in transformed.coords
