def test_squeeze():
a = generate_test_dataarray(dims={'y': 1})
value = a.values[0]
squeezed = utils.squeeze(a)
assert isinstance(squeezed, float)
assert value == squeezed
b = generate_test_dataarray(dims={'y': 2})
xr_assert_identical(b, utils.squeeze(b))
def test_disassemble_complex_dataarray():
# Create complex dataset
da = generate_test_dataarray(name='data')
complex_data = np.random.rand(*da.shape) + 1j * np.random.rand(*da.shape)
da.values = complex_data
ds_real = disassemble_complex(da)
assert_equal(set(ds_real.data_vars), {'data__re', 'data__im'})
xr_assert_equal(da.real, ds_real['data__re'])
xr_assert_equal(da.imag, ds_real['data__im'])
def test_expand_var_to_xy(extra, dims):
ref = generate_test_dataarray(
dims=OrderedDict([('y', 30), ('x', 20), ('time', 5)])
)
da = generate_test_dataarray(dims=dims)
expanded = warp._expand_var_to_xy(da, ref.coords)
# Check that the variance along added dimensions is zero
assert np.all(
expanded.var(extra) < 1e-16
)
# Check that the new DataArray contains x and y coordinates
xr_assert_equal(
expanded.coords['x'], ref.coords['x'])
xr_assert_equal(
expanded.coords['y'], ref.coords['y'])
# Check that the DataArray is unchanged if x and y were already
# dimensions
if 'x' in dims and 'y' in dims:
xr_assert_equal(da, expanded)
def test_get_extent_dataarray():
extent = (-10.0, 50.0, 0.0, 60.0)
da = generate_test_dataarray(extent=extent, crs=epsg4326)
assert_equal(extent, get_extent(da))
def test_get_bounds_dataarray():
bounds = (-10.0, 50.0, 0.0, 60.0)
da = generate_test_dataarray(extent=bounds)
assert_equal(bounds, get_bounds(da))