Testing#
testing.assert_equal(a, b[, check_dim_order])
Like numpy.testing.assert_array_equal(), but for xarray objects.
testing.assert_identical(a, b)
Like xarray.testing.assert_equal(), but also matches the objects' names and attributes.
testing.assert_allclose(a, b[, rtol, atol, ...])
Like numpy.testing.assert_allclose(), but for xarray objects.
testing.assert_chunks_equal(a, b)
Assert that chunksizes along chunked dimensions are equal.
Test that two DataTree objects are similar.
testing.assert_isomorphic(a, b)
Two DataTrees are considered isomorphic if the set of paths to their descendent nodes are the same.
testing.assert_equal(a, b[, check_dim_order])
Like numpy.testing.assert_array_equal(), but for xarray objects.
testing.assert_identical(a, b)
Like xarray.testing.assert_equal(), but also matches the objects' names and attributes.
Hypothesis Testing Strategies#
See the documentation page on testing for a guide on how to use these strategies.
Warning
These strategies should be considered highly experimental, and liable to change at any time.
testing.strategies.supported_dtypes()
Generates only those numpy dtypes which xarray can handle.
Generates arbitrary string names for dimensions / variables.
testing.strategies.dimension_names(*[, ...])
Generates an arbitrary list of valid dimension names.
testing.strategies.dimension_sizes(*[, ...])
Generates an arbitrary mapping from dimension names to lengths.
Generates arbitrary valid attributes dictionaries for xarray objects.
testing.strategies.variables(*[, ...])
Generates arbitrary xarray.Variable objects.
testing.strategies.unique_subset_of(objs, *)
Return a strategy which generates a unique subset of the given objects.