def test_pearson_r_xr_dask(a_dask, b_dask, dim, weight, weights_ones_dask,
weights_latitude_dask):
# Generates subsetted weights to pass in as arg to main function and for the numpy testing.
weights_arg, weights_np = adjust_weights(weight, dim, weights_ones_dask,
weights_latitude_dask)
actual = pearson_r(a_dask, b_dask, dim, weights=weights_arg)
assert actual.chunks is not None
dim, _ = _preprocess_dims(dim)
if len(dim) > 1:
new_dim = '_'.join(dim)
_a_dask = a_dask.stack(**{new_dim: dim})
_b_dask = b_dask.stack(**{new_dim: dim})
_weights_np = weights_np.stack(**{new_dim: dim})
else:
new_dim = dim[0]
_a_dask = a_dask
_b_dask = b_dask
_weights_np = weights_np
_weights_np = _preprocess_weights(_a_dask, dim, new_dim, _weights_np)
axis = _a_dask.dims.index(new_dim)
res = _pearson_r(_a_dask.values, _b_dask.values, _weights_np.values, axis)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
def test_pearson_r_accessor(ds_dask):
ds = ds_dask.load()
dim = 'time'
actual = pearson_r(ds['a'], ds['b'], dim)
expected = ds.xs.pearson_r('a', 'b', dim)
assert_allclose(actual, expected)
def test_pearsonr_same_as_scipy(a, b):
"""Tests that pearson r correlation and pvalue is same as computed from
scipy."""
xs_corr = pearson_r(a, b, 'time')
xs_p = pearson_r_p_value(a, b, 'time')
scipy_corr, scipy_p = pearsonr(a, b)
assert np.allclose(xs_corr, scipy_corr)
assert np.allclose(xs_p, scipy_p)
def test_keep_attrs(a, b, metrics, keep_attrs):
"""Test keep_attrs for all metrics."""
metric, _metric = metrics
# ths tests only copying attrs from a
res = metric(a, b, "time", keep_attrs=keep_attrs)
if keep_attrs:
assert res.attrs == a.attrs
else:
assert res.attrs == {}
da = xr.DataArray([0, 1, 2], dims=["time"])
assert pearson_r(da, da, dim="time") == 1
def test_pearson_r_xr_dask(a_dask, b_dask, dim):
actual = pearson_r(a_dask, b_dask, dim)
dim, _ = _preprocess(dim)
if len(dim) > 1:
new_dim = '_'.join(dim)
_a_dask = a_dask.stack(**{new_dim: dim})
_b_dask = b_dask.stack(**{new_dim: dim})
else:
new_dim = dim[0]
_a_dask = a_dask
_b_dask = b_dask
axis = _a_dask.dims.index(new_dim)
res = _pearson_r(_a_dask.values, _b_dask.values, axis)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
def test_pearson_r_xr(a, b, dim):
actual = pearson_r(a, b, dim)
dim, _ = _preprocess(dim)
if len(dim) > 1:
new_dim = '_'.join(dim)
_a = a.stack(**{new_dim: dim})
_b = b.stack(**{new_dim: dim})
else:
new_dim = dim[0]
_a = a
_b = b
axis = _a.dims.index(new_dim)
res = _pearson_r(_a.values, _b.values, axis)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
def test_nan_skipna(a, b):
# Randomly add some nans to a
a = a.where(np.random.random(a.shape) < 0.5)
with raise_if_dask_computes():
pearson_r(a, b, dim="lat", skipna=True)
def test_pearson_r_integer():
"""Test whether arrays as integers work."""
da = xr.DataArray([0, 1, 2], dims=['time'])
assert pearson_r(da, da, dim='time') == 1
def test_nan_skipna(a, b):
# Randomly add some nans to a
a = a.where(np.random.random(a.shape) < 0.5)
pearson_r(a, b, dim="lat", skipna=True)
