def test_distance_metrics_xr(a, b, dim, weight_bool, weights, metrics):
"""Test whether distance-based metric for xarray functions (from
deterministic.py) give save numerical results as for numpy functions from
np_deterministic.py)."""
# unpack metrics
metric, _metric = metrics
# Generates subsetted weights to pass in as arg to main function and for
# the numpy testing.
weights = adjust_weights(dim, weight_bool, weights)
# median absolute error has no weights argument
if metric is median_absolute_error:
actual = metric(a, b, dim)
else:
actual = metric(a, b, dim, weights=weights)
assert actual.chunks is None
dim, axis = _preprocess_dims(dim)
_a = a
_b = b
_weights = _preprocess_weights(_a, dim, dim, weights)
axis = tuple(a.dims.index(d) for d in dim)
if metric is median_absolute_error:
res = _metric(_a.values, _b.values, axis, skipna=False)
else:
# ensure _weights.values or None
_weights = None if _weights is None else _weights.values
res = _metric(_a.values, _b.values, _weights, axis, skipna=False)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
def test_correlation_metrics_xr(a, b, dim, weight_bool, weights, metrics):
"""Test whether correlation metric for xarray functions (from
deterministic.py) give save numerical results as for numpy functions from
np_deterministic.py)."""
# unpack metrics
metric, _metric = metrics
# Generates subsetted weights to pass in as arg to main function and for
# the numpy testing.
_weights = adjust_weights(dim, weight_bool, weights)
actual = metric(a, b, dim, weights=_weights)
# check that no chunks for no chunk inputs
assert actual.chunks is None
dim, _ = _preprocess_dims(dim)
if len(dim) > 1:
new_dim = '_'.join(dim)
_a = a.stack(**{new_dim: dim})
_b = b.stack(**{new_dim: dim})
if weight_bool:
_weights = _weights.stack(**{new_dim: dim})
else:
new_dim = dim[0]
_a = a
_b = b
_weights = _preprocess_weights(_a, dim, new_dim, _weights)
axis = _a.dims.index(new_dim)
res = _metric(_a.values, _b.values, _weights.values, axis, skipna=False)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
def test_pearson_r_p_value_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_p_value(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_p_value(_a_dask.values, _b_dask.values,
_weights_np.values, axis)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
def test_correlation_metrics_ufunc_same_np(a, b, dim, weight_bool,
weights_cos_lat, metrics, skipna,
has_nan):
"""Test whether correlation metric for xarray functions (from
deterministic.py) give save numerical results as for numpy functions (from
np_deterministic.py)."""
if has_nan:
a[0] = np.nan
# unpack metrics
metric, _metric = metrics
# Only apply over time dimension for effective p value.
if (dim != "time") and (metric in temporal_only_metrics):
dim = "time"
# Generates subsetted weights to pass in as arg to main function and for
# the numpy testing.
_weights = adjust_weights(dim, weight_bool, weights_cos_lat)
if metric in temporal_only_metrics:
actual = metric(a, b, dim, skipna=skipna)
else:
actual = metric(a, b, dim, weights=_weights, skipna=skipna)
# check that no chunks for no chunk inputs
assert actual.chunks is None
dim, _ = _preprocess_dims(dim, a)
if len(dim) > 1:
new_dim = "_".join(dim)
_a = a.stack(**{new_dim: dim})
_b = b.stack(**{new_dim: dim})
if weight_bool:
_weights = _weights.stack(**{new_dim: dim})
else:
new_dim = dim[0]
_a = a
_b = b
_weights = _preprocess_weights(_a, dim, new_dim, _weights)
# ensure _weights.values or None
_weights = None if _weights is None else _weights.values
axis = _a.dims.index(new_dim)
if metric in temporal_only_metrics:
res = _metric(_a.values, _b.values, axis, skipna=skipna)
else:
res = _metric(_a.values, _b.values, _weights, axis, skipna=skipna)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
def test_mae_r_xr(a, b, dim, weight, weights_ones, weights_latitude):
# 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,
weights_latitude)
actual = mae(a, b, dim, weights=weights_arg)
assert actual.chunks is None
dim, axis = _preprocess_dims(dim)
_a = a
_b = b
_weights_np = _preprocess_weights(_a, dim, dim, weights_np)
axis = tuple(a.dims.index(d) for d in dim)
res = _mae(_a.values, _b.values, _weights_np.values, axis)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
def test_mse_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 = mse(a_dask, b_dask, dim, weights=weights_arg)
assert actual.chunks is not None
dim, axis = _preprocess_dims(dim)
_a_dask = a_dask
_b_dask = b_dask
_weights_np = _preprocess_weights(_a_dask, dim, dim, weights_np)
axis = tuple(a_dask.dims.index(d) for d in dim)
res = _mse(_a_dask.values, _b_dask.values, _weights_np.values, axis)
expected = actual.copy()
expected.values = res
assert_allclose(actual, expected)
