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import numpy as np
import pytest
import xarray as xr
from xarray.indexes import NDPointIndex
from xarray.tests import assert_identical
pytest.importorskip("scipy")
def test_tree_index_init() -> None:
from xarray.indexes.nd_point_index import ScipyKDTreeAdapter
xx, yy = np.meshgrid([1.0, 2.0], [3.0, 4.0])
ds = xr.Dataset(coords={"xx": (("y", "x"), xx), "yy": (("y", "x"), yy)})
ds_indexed1 = ds.set_xindex(("xx", "yy"), NDPointIndex)
assert "xx" in ds_indexed1.xindexes
assert "yy" in ds_indexed1.xindexes
assert isinstance(ds_indexed1.xindexes["xx"], NDPointIndex)
assert ds_indexed1.xindexes["xx"] is ds_indexed1.xindexes["yy"]
ds_indexed2 = ds.set_xindex(
("xx", "yy"), NDPointIndex, tree_adapter_cls=ScipyKDTreeAdapter
)
assert ds_indexed1.xindexes["xx"].equals(ds_indexed2.xindexes["yy"])
def test_tree_index_init_errors() -> None:
xx, yy = np.meshgrid([1.0, 2.0], [3.0, 4.0])
ds = xr.Dataset(coords={"xx": (("y", "x"), xx), "yy": (("y", "x"), yy)})
with pytest.raises(ValueError, match="number of variables"):
ds.set_xindex("xx", NDPointIndex)
ds2 = ds.assign_coords(yy=(("u", "v"), [[3.0, 3.0], [4.0, 4.0]]))
with pytest.raises(ValueError, match="same dimensions"):
ds2.set_xindex(("xx", "yy"), NDPointIndex)
def test_tree_index_sel() -> None:
xx, yy = np.meshgrid([1.0, 2.0], [3.0, 4.0])
ds = xr.Dataset(coords={"xx": (("y", "x"), xx), "yy": (("y", "x"), yy)}).set_xindex(
("xx", "yy"), NDPointIndex
)
# 1-dimensional labels
actual = ds.sel(
xx=xr.Variable("u", [1.1, 1.1, 1.1]),
yy=xr.Variable("u", [3.1, 3.1, 3.1]),
method="nearest",
)
expected = xr.Dataset(
coords={"xx": ("u", [1.0, 1.0, 1.0]), "yy": ("u", [3.0, 3.0, 3.0])}
)
assert_identical(actual, expected)
# 2-dimensional labels
actual = ds.sel(
xx=xr.Variable(("u", "v"), [[1.1, 1.1, 1.1], [1.9, 1.9, 1.9]]),
yy=xr.Variable(("u", "v"), [[3.1, 3.1, 3.1], [3.9, 3.9, 3.9]]),
method="nearest",
)
expected = xr.Dataset(
coords={
"xx": (("u", "v"), [[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]),
"yy": (("u", "v"), [[3.0, 3.0, 3.0], [4.0, 4.0, 4.0]]),
},
)
assert_identical(actual, expected)
# all scalar labels
actual = ds.sel(xx=1.1, yy=3.1, method="nearest")
expected = xr.Dataset(coords={"xx": 1.0, "yy": 3.0})
assert_identical(actual, expected)
# broadcast scalar to label shape and dimensions
actual = ds.sel(xx=1.1, yy=xr.Variable("u", [3.1, 3.1, 3.1]), method="nearest")
expected = ds.sel(
xx=xr.Variable("u", [1.1, 1.1, 1.1]),
yy=xr.Variable("u", [3.1, 3.1, 3.1]),
method="nearest",
)
assert_identical(actual, expected)
# broadcast orthogonal 1-dimensional labels
actual = ds.sel(
xx=xr.Variable("u", [1.1, 1.1]),
yy=xr.Variable("v", [3.1, 3.1]),
method="nearest",
)
expected = xr.Dataset(
coords={
"xx": (("u", "v"), [[1.0, 1.0], [1.0, 1.0]]),
"yy": (("u", "v"), [[3.0, 3.0], [3.0, 3.0]]),
},
)
assert_identical(actual, expected)
# implicit dimension array-like labels
actual = ds.sel(
xx=[[1.1, 1.1, 1.1], [1.9, 1.9, 1.9]],
yy=[[3.1, 3.1, 3.1], [3.9, 3.9, 3.9]],
method="nearest",
)
expected = ds.sel(
xx=xr.Variable(ds.xx.dims, [[1.1, 1.1, 1.1], [1.9, 1.9, 1.9]]),
yy=xr.Variable(ds.yy.dims, [[3.1, 3.1, 3.1], [3.9, 3.9, 3.9]]),
method="nearest",
)
assert_identical(actual, expected)
def test_tree_index_sel_errors() -> None:
xx, yy = np.meshgrid([1.0, 2.0], [3.0, 4.0])
ds = xr.Dataset(coords={"xx": (("y", "x"), xx), "yy": (("y", "x"), yy)}).set_xindex(
("xx", "yy"), NDPointIndex
)
with pytest.raises(ValueError, match="method='nearest'"):
ds.sel(xx=1.1, yy=3.1)
with pytest.raises(ValueError, match="missing labels"):
ds.sel(xx=1.1, method="nearest")
with pytest.raises(ValueError, match="invalid label value"):
# invalid array-like dimensions
ds.sel(xx=[1.1, 1.9], yy=[3.1, 3.9], method="nearest")
# error while trying to broadcast labels
with pytest.raises(xr.AlignmentError, match=".*conflicting dimension sizes"):
ds.sel(
xx=xr.Variable("u", [1.1, 1.1, 1.1]),
yy=xr.Variable("u", [3.1, 3.1]),
method="nearest",
)
def test_tree_index_equals() -> None:
xx1, yy1 = np.meshgrid([1.0, 2.0], [3.0, 4.0])
ds1 = xr.Dataset(
coords={"xx": (("y", "x"), xx1), "yy": (("y", "x"), yy1)}
).set_xindex(("xx", "yy"), NDPointIndex)
xx2, yy2 = np.meshgrid([1.0, 2.0], [3.0, 4.0])
ds2 = xr.Dataset(
coords={"xx": (("y", "x"), xx2), "yy": (("y", "x"), yy2)}
).set_xindex(("xx", "yy"), NDPointIndex)
xx3, yy3 = np.meshgrid([10.0, 20.0], [30.0, 40.0])
ds3 = xr.Dataset(
coords={"xx": (("y", "x"), xx3), "yy": (("y", "x"), yy3)}
).set_xindex(("xx", "yy"), NDPointIndex)
assert ds1.xindexes["xx"].equals(ds2.xindexes["xx"])
assert not ds1.xindexes["xx"].equals(ds3.xindexes["xx"])
def test_tree_index_rename() -> None:
xx, yy = np.meshgrid([1.0, 2.0], [3.0, 4.0])
ds = xr.Dataset(coords={"xx": (("y", "x"), xx), "yy": (("y", "x"), yy)}).set_xindex(
("xx", "yy"), NDPointIndex
)
ds_renamed = ds.rename_dims(y="u").rename_vars(yy="uu")
assert "uu" in ds_renamed.xindexes
assert isinstance(ds_renamed.xindexes["uu"], NDPointIndex)
assert ds_renamed.xindexes["xx"] is ds_renamed.xindexes["uu"]
# test via sel() with implicit dimension array-like labels, which relies on
# NDPointIndex._coord_names and NDPointIndex._dims internal attrs
actual = ds_renamed.sel(
xx=[[1.1, 1.1, 1.1], [1.9, 1.9, 1.9]],
uu=[[3.1, 3.1, 3.1], [3.9, 3.9, 3.9]],
method="nearest",
)
expected = ds_renamed.sel(
xx=xr.Variable(ds_renamed.xx.dims, [[1.1, 1.1, 1.1], [1.9, 1.9, 1.9]]),
uu=xr.Variable(ds_renamed.uu.dims, [[3.1, 3.1, 3.1], [3.9, 3.9, 3.9]]),
method="nearest",
)
assert_identical(actual, expected)
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