def test_asarraylike_array():
"""
Passing in a list should return a np.ndarray.
"""
arr = np.array([1, 2, 3, 4])
result = utilities.asarraylike(arr)
assert result is arr
def test_asarraylike_list():
"""
Passing in a list should return a np.ndarray.
"""
lst = [1, 2, 3, 4]
result = utilities.asarraylike(lst)
assert isinstance(result, np.ndarray)
assert np.array_equal(result, lst)
def data(self, d):
d = asarraylike(d)
# Fixme: maybe all this checking should be done when it gets added to the Dataset??
if self.time is not None and len(d) != len(self.time):
raise ValueError("Data/time interval mismatch")
## fixme: we should check Depth, too.
# if self.grid is not None and self.grid.infer_location(d) is None:
# raise ValueError("Data/grid shape mismatch. Data shape is {0}, Grid shape is {1}".format(d.shape, self.grid.node_lon.shape))
if self.grid is not None: # if there is not a grid, we can't check this
if self.location is None: # not set, let's try to figure it out
self.location = self.grid.infer_location(d)
if self.location is None:
raise ValueError(
"Data/grid shape mismatch: Data shape is {0}, "
"Grid shape is {1}".format(d.shape,
self.grid.node_lon.shape))
self._data = d
def test_as_test_asarraylike_dummy():
dum = DummyArrayLike()
result = utilities.asarraylike(dum)
assert result is dum