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import pickle
import numpy as np
import pytest
import xarray as xr
import xarray.ufuncs as xu
from . import assert_array_equal
from . import assert_identical as assert_identical_
from . import mock, raises_regex
def assert_identical(a, b):
assert type(a) is type(b) or float(a) == float(b)
if isinstance(a, (xr.DataArray, xr.Dataset, xr.Variable)):
assert_identical_(a, b)
else:
assert_array_equal(a, b)
def test_unary():
args = [
0,
np.zeros(2),
xr.Variable(["x"], [0, 0]),
xr.DataArray([0, 0], dims="x"),
xr.Dataset({"y": ("x", [0, 0])}),
]
for a in args:
assert_identical(a + 1, np.cos(a))
def test_binary():
args = [
0,
np.zeros(2),
xr.Variable(["x"], [0, 0]),
xr.DataArray([0, 0], dims="x"),
xr.Dataset({"y": ("x", [0, 0])}),
]
for n, t1 in enumerate(args):
for t2 in args[n:]:
assert_identical(t2 + 1, np.maximum(t1, t2 + 1))
assert_identical(t2 + 1, np.maximum(t2, t1 + 1))
assert_identical(t2 + 1, np.maximum(t1 + 1, t2))
assert_identical(t2 + 1, np.maximum(t2 + 1, t1))
def test_binary_out():
args = [
1,
np.ones(2),
xr.Variable(["x"], [1, 1]),
xr.DataArray([1, 1], dims="x"),
xr.Dataset({"y": ("x", [1, 1])}),
]
for arg in args:
actual_mantissa, actual_exponent = np.frexp(arg)
assert_identical(actual_mantissa, 0.5 * arg)
assert_identical(actual_exponent, arg)
def test_groupby():
ds = xr.Dataset({"a": ("x", [0, 0, 0])}, {"c": ("x", [0, 0, 1])})
ds_grouped = ds.groupby("c")
group_mean = ds_grouped.mean("x")
arr_grouped = ds["a"].groupby("c")
assert_identical(ds, np.maximum(ds_grouped, group_mean))
assert_identical(ds, np.maximum(group_mean, ds_grouped))
assert_identical(ds, np.maximum(arr_grouped, group_mean))
assert_identical(ds, np.maximum(group_mean, arr_grouped))
assert_identical(ds, np.maximum(ds_grouped, group_mean["a"]))
assert_identical(ds, np.maximum(group_mean["a"], ds_grouped))
assert_identical(ds.a, np.maximum(arr_grouped, group_mean.a))
assert_identical(ds.a, np.maximum(group_mean.a, arr_grouped))
with raises_regex(ValueError, "mismatched lengths for dimension"):
np.maximum(ds.a.variable, ds_grouped)
def test_alignment():
ds1 = xr.Dataset({"a": ("x", [1, 2])}, {"x": [0, 1]})
ds2 = xr.Dataset({"a": ("x", [2, 3]), "b": 4}, {"x": [1, 2]})
actual = np.add(ds1, ds2)
expected = xr.Dataset({"a": ("x", [4])}, {"x": [1]})
assert_identical_(actual, expected)
with xr.set_options(arithmetic_join="outer"):
actual = np.add(ds1, ds2)
expected = xr.Dataset(
{"a": ("x", [np.nan, 4, np.nan]), "b": np.nan}, coords={"x": [0, 1, 2]}
)
assert_identical_(actual, expected)
def test_kwargs():
x = xr.DataArray(0)
result = np.add(x, 1, dtype=np.float64)
assert result.dtype == np.float64
def test_xarray_defers_to_unrecognized_type():
class Other:
def __array_ufunc__(self, *args, **kwargs):
return "other"
xarray_obj = xr.DataArray([1, 2, 3])
other = Other()
assert np.maximum(xarray_obj, other) == "other"
assert np.sin(xarray_obj, out=other) == "other"
def test_xarray_handles_dask():
da = pytest.importorskip("dask.array")
x = xr.DataArray(np.ones((2, 2)), dims=["x", "y"])
y = da.ones((2, 2), chunks=(2, 2))
result = np.add(x, y)
assert result.chunks == ((2,), (2,))
assert isinstance(result, xr.DataArray)
def test_dask_defers_to_xarray():
da = pytest.importorskip("dask.array")
x = xr.DataArray(np.ones((2, 2)), dims=["x", "y"])
y = da.ones((2, 2), chunks=(2, 2))
result = np.add(y, x)
assert result.chunks == ((2,), (2,))
assert isinstance(result, xr.DataArray)
def test_gufunc_methods():
xarray_obj = xr.DataArray([1, 2, 3])
with raises_regex(NotImplementedError, "reduce method"):
np.add.reduce(xarray_obj, 1)
def test_out():
xarray_obj = xr.DataArray([1, 2, 3])
# xarray out arguments should raise
with raises_regex(NotImplementedError, "`out` argument"):
np.add(xarray_obj, 1, out=xarray_obj)
# but non-xarray should be OK
other = np.zeros((3,))
np.add(other, xarray_obj, out=other)
assert_identical(other, np.array([1, 2, 3]))
def test_gufuncs():
xarray_obj = xr.DataArray([1, 2, 3])
fake_gufunc = mock.Mock(signature="(n)->()", autospec=np.sin)
with raises_regex(NotImplementedError, "generalized ufuncs"):
xarray_obj.__array_ufunc__(fake_gufunc, "__call__", xarray_obj)
def test_xarray_ufuncs_deprecation():
with pytest.warns(PendingDeprecationWarning, match="xarray.ufuncs"):
xu.cos(xr.DataArray([0, 1]))
with pytest.warns(None) as record:
xu.angle(xr.DataArray([0, 1]))
record = [el.message for el in record if el.category == PendingDeprecationWarning]
assert len(record) == 0
@pytest.mark.filterwarnings("ignore::RuntimeWarning")
@pytest.mark.parametrize(
"name",
[
name
for name in dir(xu)
if (
not name.startswith("_")
and hasattr(np, name)
and name not in ["print_function", "absolute_import", "division"]
)
],
)
def test_numpy_ufuncs(name, request):
x = xr.DataArray([1, 1])
np_func = getattr(np, name)
if hasattr(np_func, "nin") and np_func.nin == 2:
args = (x, x)
else:
args = (x,)
y = np_func(*args)
if name in ["angle", "iscomplex"]:
# these functions need to be handled with __array_function__ protocol
assert isinstance(y, np.ndarray)
elif name in ["frexp"]:
# np.frexp returns a tuple
assert not isinstance(y, xr.DataArray)
else:
assert isinstance(y, xr.DataArray)
@pytest.mark.filterwarnings("ignore:xarray.ufuncs")
def test_xarray_ufuncs_pickle():
a = 1.0
cos_pickled = pickle.loads(pickle.dumps(xu.cos))
assert_identical(cos_pickled(a), xu.cos(a))
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