def test_no_dim(self):
dat = np.array([5, 7, 3.2, -1, -4.4])
orig = DataArray(dat, dims=["x"])
# Need to supply dimension
with pytest.raises(ValueError):
rms(orig, dim=None)
def test_reduce_2d(self, dim, axis):
dat = np.array([[5, 7, 3.2, -1, -4.4], [-1, -2.5, 0, 8, 3.0]])
orig = DataArray(dat, dims=["x", "t"])
sum_squares = np.sum(dat**2, axis=axis)
mean_squares = sum_squares / dat.shape[axis]
rootmeansquare = np.sqrt(mean_squares)
expected = rootmeansquare
actual = rms(orig, dim=dim).values
npt.assert_equal(actual, expected)
def test_1d(self):
dat = np.array([5, 7, 3.2, -1, -4.4])
orig = DataArray(dat, dims=["x"])
sum_squares = np.sum(dat**2)
mean_squares = sum_squares / dat.size
rootmeansquare = np.sqrt(mean_squares)
expected = rootmeansquare
actual = rms(orig, dim="x").values
npt.assert_equal(actual, expected)
def test_reduce_2d_dask(self):
dat = np.array([[5, 7, 3.2, -1, -4.4], [-1, -2.5, 0, 8, 3.0]])
orig = DataArray(dat, dims=["x", "t"])
chunked = orig.chunk({"x": 1})
axis = 1
sum_squares = np.sum(dat**2, axis=axis)
mean_squares = sum_squares / dat.shape[axis]
rootmeansquare = np.sqrt(mean_squares)
expected = rootmeansquare
actual = rms(chunked, dim="t").values
npt.assert_equal(actual, expected)