import pickle import sys import warnings from copy import copy, deepcopy from io import StringIO from textwrap import dedent import numpy as np import pandas as pd import pytest from pandas.core.indexes.datetimes import DatetimeIndex import xarray as xr from xarray import ( DataArray, Dataset, IndexVariable, MergeError, Variable, align, backends, broadcast, open_dataset, set_options, ) from xarray.coding.cftimeindex import CFTimeIndex from xarray.core import dtypes, indexing, utils from xarray.core.common import duck_array_ops, full_like from xarray.core.npcompat import IS_NEP18_ACTIVE from xarray.core.pycompat import integer_types from xarray.core.utils import is_scalar from . import ( InaccessibleArray, UnexpectedDataAccess, assert_allclose, assert_array_equal, assert_equal, assert_identical, has_cftime, has_dask, raises_regex, requires_bottleneck, requires_cftime, requires_dask, requires_numbagg, requires_scipy, requires_sparse, source_ndarray, ) try: import dask.array as da except ImportError: pass pytestmark = [ pytest.mark.filterwarnings("error:Mean of empty slice"), pytest.mark.filterwarnings("error:All-NaN (slice|axis) encountered"), ] def create_test_data(seed=None): rs = np.random.RandomState(seed) _vars = { "var1": ["dim1", "dim2"], "var2": ["dim1", "dim2"], "var3": ["dim3", "dim1"], } _dims = {"dim1": 8, "dim2": 9, "dim3": 10} obj = Dataset() obj["time"] = ("time", pd.date_range("2000-01-01", periods=20)) obj["dim2"] = ("dim2", 0.5 * np.arange(_dims["dim2"])) obj["dim3"] = ("dim3", list("abcdefghij")) for v, dims in sorted(_vars.items()): data = rs.normal(size=tuple(_dims[d] for d in dims)) obj[v] = (dims, data, {"foo": "variable"}) obj.coords["numbers"] = ( "dim3", np.array([0, 1, 2, 0, 0, 1, 1, 2, 2, 3], dtype="int64"), ) obj.encoding = {"foo": "bar"} assert all(obj.data.flags.writeable for obj in obj.variables.values()) return obj def create_append_test_data(seed=None): rs = np.random.RandomState(seed) lat = [2, 1, 0] lon = [0, 1, 2] nt1 = 3 nt2 = 2 time1 = pd.date_range("2000-01-01", periods=nt1) time2 = pd.date_range("2000-02-01", periods=nt2) string_var = np.array(["ae", "bc", "df"], dtype=object) string_var_to_append = np.array(["asdf", "asdfg"], dtype=object) unicode_var = ["áó", "áó", "áó"] datetime_var = np.array( ["2019-01-01", "2019-01-02", "2019-01-03"], dtype="datetime64[s]" ) datetime_var_to_append = np.array( ["2019-01-04", "2019-01-05"], dtype="datetime64[s]" ) bool_var = np.array([True, False, True], dtype=bool) bool_var_to_append = np.array([False, True], dtype=bool) ds = xr.Dataset( data_vars={ "da": xr.DataArray( rs.rand(3, 3, nt1), coords=[lat, lon, time1], dims=["lat", "lon", "time"], ), "string_var": xr.DataArray(string_var, coords=[time1], dims=["time"]), "unicode_var": xr.DataArray( unicode_var, coords=[time1], dims=["time"] ).astype(np.unicode_), "datetime_var": xr.DataArray(datetime_var, coords=[time1], dims=["time"]), "bool_var": xr.DataArray(bool_var, coords=[time1], dims=["time"]), } ) ds_to_append = xr.Dataset( data_vars={ "da": xr.DataArray( rs.rand(3, 3, nt2), coords=[lat, lon, time2], dims=["lat", "lon", "time"], ), "string_var": xr.DataArray( string_var_to_append, coords=[time2], dims=["time"] ), "unicode_var": xr.DataArray( unicode_var[:nt2], coords=[time2], dims=["time"] ).astype(np.unicode_), "datetime_var": xr.DataArray( datetime_var_to_append, coords=[time2], dims=["time"] ), "bool_var": xr.DataArray(bool_var_to_append, coords=[time2], dims=["time"]), } ) ds_with_new_var = xr.Dataset( data_vars={ "new_var": xr.DataArray( rs.rand(3, 3, nt1 + nt2), coords=[lat, lon, time1.append(time2)], dims=["lat", "lon", "time"], ) } ) assert all(objp.data.flags.writeable for objp in ds.variables.values()) assert all(objp.data.flags.writeable for objp in ds_to_append.variables.values()) return ds, ds_to_append, ds_with_new_var def create_test_multiindex(): mindex = pd.MultiIndex.from_product( [["a", "b"], [1, 2]], names=("level_1", "level_2") ) return Dataset({}, {"x": mindex}) def create_test_stacked_array(): x = DataArray(pd.Index(np.r_[:10], name="x")) y = DataArray(pd.Index(np.r_[:20], name="y")) a = x * y b = x * y * y return a, b class InaccessibleVariableDataStore(backends.InMemoryDataStore): def __init__(self): super().__init__() self._indexvars = set() def store(self, variables, *args, **kwargs): super().store(variables, *args, **kwargs) for k, v in variables.items(): if isinstance(v, IndexVariable): self._indexvars.add(k) def get_variables(self): def lazy_inaccessible(k, v): if k in self._indexvars: return v data = indexing.LazilyOuterIndexedArray(InaccessibleArray(v.values)) return Variable(v.dims, data, v.attrs) return {k: lazy_inaccessible(k, v) for k, v in self._variables.items()} class TestDataset: def test_repr(self): data = create_test_data(seed=123) data.attrs["foo"] = "bar" # need to insert str dtype at runtime to handle different endianness expected = dedent( """\ Dimensions: (dim1: 8, dim2: 9, dim3: 10, time: 20) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-20 * dim2 (dim2) float64 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 * dim3 (dim3) %s 'a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' 'i' 'j' numbers (dim3) int64 0 1 2 0 0 1 1 2 2 3 Dimensions without coordinates: dim1 Data variables: var1 (dim1, dim2) float64 -1.086 0.9973 0.283 ... 0.1995 0.4684 -0.8312 var2 (dim1, dim2) float64 1.162 -1.097 -2.123 ... 0.1302 1.267 0.3328 var3 (dim3, dim1) float64 0.5565 -0.2121 0.4563 ... -0.2452 -0.3616 Attributes: foo: bar""" % data["dim3"].dtype ) actual = "\n".join(x.rstrip() for x in repr(data).split("\n")) print(actual) assert expected == actual with set_options(display_width=100): max_len = max(map(len, repr(data).split("\n"))) assert 90 < max_len < 100 expected = dedent( """\ Dimensions: () Data variables: *empty*""" ) actual = "\n".join(x.rstrip() for x in repr(Dataset()).split("\n")) print(actual) assert expected == actual # verify that ... doesn't appear for scalar coordinates data = Dataset({"foo": ("x", np.ones(10))}).mean() expected = dedent( """\ Dimensions: () Data variables: foo float64 1.0""" ) actual = "\n".join(x.rstrip() for x in repr(data).split("\n")) print(actual) assert expected == actual # verify long attributes are truncated data = Dataset(attrs={"foo": "bar" * 1000}) assert len(repr(data)) < 1000 def test_repr_multiindex(self): data = create_test_multiindex() expected = dedent( """\ Dimensions: (x: 4) Coordinates: * x (x) MultiIndex - level_1 (x) object 'a' 'a' 'b' 'b' - level_2 (x) int64 1 2 1 2 Data variables: *empty*""" ) actual = "\n".join(x.rstrip() for x in repr(data).split("\n")) print(actual) assert expected == actual # verify that long level names are not truncated mindex = pd.MultiIndex.from_product( [["a", "b"], [1, 2]], names=("a_quite_long_level_name", "level_2") ) data = Dataset({}, {"x": mindex}) expected = dedent( """\ Dimensions: (x: 4) Coordinates: * x (x) MultiIndex - a_quite_long_level_name (x) object 'a' 'a' 'b' 'b' - level_2 (x) int64 1 2 1 2 Data variables: *empty*""" ) actual = "\n".join(x.rstrip() for x in repr(data).split("\n")) print(actual) assert expected == actual def test_repr_period_index(self): data = create_test_data(seed=456) data.coords["time"] = pd.period_range("2000-01-01", periods=20, freq="B") # check that creating the repr doesn't raise an error #GH645 repr(data) def test_unicode_data(self): # regression test for GH834 data = Dataset({"foø": ["ba®"]}, attrs={"å": "∑"}) repr(data) # should not raise byteorder = "<" if sys.byteorder == "little" else ">" expected = dedent( """\ Dimensions: (foø: 1) Coordinates: * foø (foø) %cU3 %r Data variables: *empty* Attributes: å: ∑""" % (byteorder, "ba®") ) actual = str(data) assert expected == actual @pytest.mark.skipif(not IS_NEP18_ACTIVE, reason="requires __array_function__") def test_repr_nep18(self): class Array: def __init__(self): self.shape = (2,) self.dtype = np.dtype(np.float64) def __array_function__(self, *args, **kwargs): pass def __repr__(self): return "Custom\nArray" dataset = Dataset({"foo": ("x", Array())}) expected = dedent( """\ Dimensions: (x: 2) Dimensions without coordinates: x Data variables: foo (x) float64 Custom Array""" ) assert expected == repr(dataset) def test_info(self): ds = create_test_data(seed=123) ds = ds.drop_vars("dim3") # string type prints differently in PY2 vs PY3 ds.attrs["unicode_attr"] = "ba®" ds.attrs["string_attr"] = "bar" buf = StringIO() ds.info(buf=buf) expected = dedent( """\ xarray.Dataset { dimensions: \tdim1 = 8 ; \tdim2 = 9 ; \tdim3 = 10 ; \ttime = 20 ; variables: \tdatetime64[ns] time(time) ; \tfloat64 dim2(dim2) ; \tfloat64 var1(dim1, dim2) ; \t\tvar1:foo = variable ; \tfloat64 var2(dim1, dim2) ; \t\tvar2:foo = variable ; \tfloat64 var3(dim3, dim1) ; \t\tvar3:foo = variable ; \tint64 numbers(dim3) ; // global attributes: \t:unicode_attr = ba® ; \t:string_attr = bar ; }""" ) actual = buf.getvalue() assert expected == actual buf.close() def test_constructor(self): x1 = ("x", 2 * np.arange(100)) x2 = ("x", np.arange(1000)) z = (["x", "y"], np.arange(1000).reshape(100, 10)) with raises_regex(ValueError, "conflicting sizes"): Dataset({"a": x1, "b": x2}) with raises_regex(ValueError, "disallows such variables"): Dataset({"a": x1, "x": z}) with raises_regex(TypeError, "tuple of form"): Dataset({"x": (1, 2, 3, 4, 5, 6, 7)}) with raises_regex(ValueError, "already exists as a scalar"): Dataset({"x": 0, "y": ("x", [1, 2, 3])}) # verify handling of DataArrays expected = Dataset({"x": x1, "z": z}) actual = Dataset({"z": expected["z"]}) assert_identical(expected, actual) def test_constructor_invalid_dims(self): # regression for GH1120 with pytest.raises(MergeError): Dataset( data_vars=dict(v=("y", [1, 2, 3, 4])), coords=dict(y=DataArray([0.1, 0.2, 0.3, 0.4], dims="x")), ) def test_constructor_1d(self): expected = Dataset({"x": (["x"], 5.0 + np.arange(5))}) actual = Dataset({"x": 5.0 + np.arange(5)}) assert_identical(expected, actual) actual = Dataset({"x": [5, 6, 7, 8, 9]}) assert_identical(expected, actual) def test_constructor_0d(self): expected = Dataset({"x": ([], 1)}) for arg in [1, np.array(1), expected["x"]]: actual = Dataset({"x": arg}) assert_identical(expected, actual) class Arbitrary: pass d = pd.Timestamp("2000-01-01T12") args = [ True, None, 3.4, np.nan, "hello", b"raw", np.datetime64("2000-01-01"), d, d.to_pydatetime(), Arbitrary(), ] for arg in args: print(arg) expected = Dataset({"x": ([], arg)}) actual = Dataset({"x": arg}) assert_identical(expected, actual) def test_constructor_deprecated(self): with raises_regex(ValueError, "DataArray dimensions"): DataArray([1, 2, 3], coords={"x": [0, 1, 2]}) def test_constructor_auto_align(self): a = DataArray([1, 2], [("x", [0, 1])]) b = DataArray([3, 4], [("x", [1, 2])]) # verify align uses outer join expected = Dataset( {"a": ("x", [1, 2, np.nan]), "b": ("x", [np.nan, 3, 4])}, {"x": [0, 1, 2]} ) actual = Dataset({"a": a, "b": b}) assert_identical(expected, actual) # regression test for GH346 assert isinstance(actual.variables["x"], IndexVariable) # variable with different dimensions c = ("y", [3, 4]) expected2 = expected.merge({"c": c}) actual = Dataset({"a": a, "b": b, "c": c}) assert_identical(expected2, actual) # variable that is only aligned against the aligned variables d = ("x", [3, 2, 1]) expected3 = expected.merge({"d": d}) actual = Dataset({"a": a, "b": b, "d": d}) assert_identical(expected3, actual) e = ("x", [0, 0]) with raises_regex(ValueError, "conflicting sizes"): Dataset({"a": a, "b": b, "e": e}) def test_constructor_pandas_sequence(self): ds = self.make_example_math_dataset() pandas_objs = { var_name: ds[var_name].to_pandas() for var_name in ["foo", "bar"] } ds_based_on_pandas = Dataset(pandas_objs, ds.coords, attrs=ds.attrs) del ds_based_on_pandas["x"] assert_equal(ds, ds_based_on_pandas) # reindex pandas obj, check align works rearranged_index = reversed(pandas_objs["foo"].index) pandas_objs["foo"] = pandas_objs["foo"].reindex(rearranged_index) ds_based_on_pandas = Dataset(pandas_objs, ds.coords, attrs=ds.attrs) del ds_based_on_pandas["x"] assert_equal(ds, ds_based_on_pandas) def test_constructor_pandas_single(self): das = [ DataArray(np.random.rand(4), dims=["a"]), # series DataArray(np.random.rand(4, 3), dims=["a", "b"]), # df ] for a in das: pandas_obj = a.to_pandas() ds_based_on_pandas = Dataset(pandas_obj) for dim in ds_based_on_pandas.data_vars: assert_array_equal(ds_based_on_pandas[dim], pandas_obj[dim]) def test_constructor_compat(self): data = {"x": DataArray(0, coords={"y": 1}), "y": ("z", [1, 1, 1])} expected = Dataset({"x": 0}, {"y": ("z", [1, 1, 1])}) actual = Dataset(data) assert_identical(expected, actual) data = {"y": ("z", [1, 1, 1]), "x": DataArray(0, coords={"y": 1})} actual = Dataset(data) assert_identical(expected, actual) original = Dataset( {"a": (("x", "y"), np.ones((2, 3)))}, {"c": (("x", "y"), np.zeros((2, 3))), "x": [0, 1]}, ) expected = Dataset( {"a": ("x", np.ones(2)), "b": ("y", np.ones(3))}, {"c": (("x", "y"), np.zeros((2, 3))), "x": [0, 1]}, ) actual = Dataset( {"a": original["a"][:, 0], "b": original["a"][0].drop_vars("x")} ) assert_identical(expected, actual) data = {"x": DataArray(0, coords={"y": 3}), "y": ("z", [1, 1, 1])} with pytest.raises(MergeError): Dataset(data) data = {"x": DataArray(0, coords={"y": 1}), "y": [1, 1]} actual = Dataset(data) expected = Dataset({"x": 0}, {"y": [1, 1]}) assert_identical(expected, actual) def test_constructor_with_coords(self): with raises_regex(ValueError, "found in both data_vars and"): Dataset({"a": ("x", [1])}, {"a": ("x", [1])}) ds = Dataset({}, {"a": ("x", [1])}) assert not ds.data_vars assert list(ds.coords.keys()) == ["a"] mindex = pd.MultiIndex.from_product( [["a", "b"], [1, 2]], names=("level_1", "level_2") ) with raises_regex(ValueError, "conflicting MultiIndex"): Dataset({}, {"x": mindex, "y": mindex}) Dataset({}, {"x": mindex, "level_1": range(4)}) def test_properties(self): ds = create_test_data() assert ds.dims == {"dim1": 8, "dim2": 9, "dim3": 10, "time": 20} assert list(ds.dims) == sorted(ds.dims) assert ds.sizes == ds.dims # These exact types aren't public API, but this makes sure we don't # change them inadvertently: assert isinstance(ds.dims, utils.Frozen) assert isinstance(ds.dims.mapping, utils.SortedKeysDict) assert type(ds.dims.mapping.mapping) is dict assert list(ds) == list(ds.data_vars) assert list(ds.keys()) == list(ds.data_vars) assert "aasldfjalskdfj" not in ds.variables assert "dim1" in repr(ds.variables) assert len(ds) == 3 assert bool(ds) assert list(ds.data_vars) == ["var1", "var2", "var3"] assert list(ds.data_vars.keys()) == ["var1", "var2", "var3"] assert "var1" in ds.data_vars assert "dim1" not in ds.data_vars assert "numbers" not in ds.data_vars assert len(ds.data_vars) == 3 assert set(ds.indexes) == {"dim2", "dim3", "time"} assert len(ds.indexes) == 3 assert "dim2" in repr(ds.indexes) assert list(ds.coords) == ["time", "dim2", "dim3", "numbers"] assert "dim2" in ds.coords assert "numbers" in ds.coords assert "var1" not in ds.coords assert "dim1" not in ds.coords assert len(ds.coords) == 4 assert Dataset({"x": np.int64(1), "y": np.float32([1, 2])}).nbytes == 16 def test_asarray(self): ds = Dataset({"x": 0}) with raises_regex(TypeError, "cannot directly convert"): np.asarray(ds) def test_get_index(self): ds = Dataset({"foo": (("x", "y"), np.zeros((2, 3)))}, coords={"x": ["a", "b"]}) assert ds.get_index("x").equals(pd.Index(["a", "b"])) assert ds.get_index("y").equals(pd.Index([0, 1, 2])) with pytest.raises(KeyError): ds.get_index("z") def test_attr_access(self): ds = Dataset( {"tmin": ("x", [42], {"units": "Celcius"})}, attrs={"title": "My test data"} ) assert_identical(ds.tmin, ds["tmin"]) assert_identical(ds.tmin.x, ds.x) assert ds.title == ds.attrs["title"] assert ds.tmin.units == ds["tmin"].attrs["units"] assert {"tmin", "title"} <= set(dir(ds)) assert "units" in set(dir(ds.tmin)) # should defer to variable of same name ds.attrs["tmin"] = -999 assert ds.attrs["tmin"] == -999 assert_identical(ds.tmin, ds["tmin"]) def test_variable(self): a = Dataset() d = np.random.random((10, 3)) a["foo"] = (("time", "x"), d) assert "foo" in a.variables assert "foo" in a a["bar"] = (("time", "x"), d) # order of creation is preserved assert list(a.variables) == ["foo", "bar"] assert_array_equal(a["foo"].values, d) # try to add variable with dim (10,3) with data that's (3,10) with pytest.raises(ValueError): a["qux"] = (("time", "x"), d.T) def test_modify_inplace(self): a = Dataset() vec = np.random.random((10,)) attributes = {"foo": "bar"} a["x"] = ("x", vec, attributes) assert "x" in a.coords assert isinstance(a.coords["x"].to_index(), pd.Index) assert_identical(a.coords["x"].variable, a.variables["x"]) b = Dataset() b["x"] = ("x", vec, attributes) assert_identical(a["x"], b["x"]) assert a.dims == b.dims # this should work a["x"] = ("x", vec[:5]) a["z"] = ("x", np.arange(5)) with pytest.raises(ValueError): # now it shouldn't, since there is a conflicting length a["x"] = ("x", vec[:4]) arr = np.random.random((10, 1)) scal = np.array(0) with pytest.raises(ValueError): a["y"] = ("y", arr) with pytest.raises(ValueError): a["y"] = ("y", scal) assert "y" not in a.dims def test_coords_properties(self): # use int64 for repr consistency on windows data = Dataset( { "x": ("x", np.array([-1, -2], "int64")), "y": ("y", np.array([0, 1, 2], "int64")), "foo": (["x", "y"], np.random.randn(2, 3)), }, {"a": ("x", np.array([4, 5], "int64")), "b": np.int64(-10)}, ) assert 4 == len(data.coords) assert ["x", "y", "a", "b"] == list(data.coords) assert_identical(data.coords["x"].variable, data["x"].variable) assert_identical(data.coords["y"].variable, data["y"].variable) assert "x" in data.coords assert "a" in data.coords assert 0 not in data.coords assert "foo" not in data.coords with pytest.raises(KeyError): data.coords["foo"] with pytest.raises(KeyError): data.coords[0] expected = dedent( """\ Coordinates: * x (x) int64 -1 -2 * y (y) int64 0 1 2 a (x) int64 4 5 b int64 -10""" ) actual = repr(data.coords) assert expected == actual assert {"x": 2, "y": 3} == data.coords.dims def test_coords_modify(self): data = Dataset( { "x": ("x", [-1, -2]), "y": ("y", [0, 1, 2]), "foo": (["x", "y"], np.random.randn(2, 3)), }, {"a": ("x", [4, 5]), "b": -10}, ) actual = data.copy(deep=True) actual.coords["x"] = ("x", ["a", "b"]) assert_array_equal(actual["x"], ["a", "b"]) actual = data.copy(deep=True) actual.coords["z"] = ("z", ["a", "b"]) assert_array_equal(actual["z"], ["a", "b"]) actual = data.copy(deep=True) with raises_regex(ValueError, "conflicting sizes"): actual.coords["x"] = ("x", [-1]) assert_identical(actual, data) # should not be modified actual = data.copy() del actual.coords["b"] expected = data.reset_coords("b", drop=True) assert_identical(expected, actual) with pytest.raises(KeyError): del data.coords["not_found"] with pytest.raises(KeyError): del data.coords["foo"] actual = data.copy(deep=True) actual.coords.update({"c": 11}) expected = data.merge({"c": 11}).set_coords("c") assert_identical(expected, actual) # regression test for GH3746 del actual.coords["x"] assert "x" not in actual.indexes def test_update_index(self): actual = Dataset(coords={"x": [1, 2, 3]}) actual["x"] = ["a", "b", "c"] assert actual.indexes["x"].equals(pd.Index(["a", "b", "c"])) def test_coords_setitem_with_new_dimension(self): actual = Dataset() actual.coords["foo"] = ("x", [1, 2, 3]) expected = Dataset(coords={"foo": ("x", [1, 2, 3])}) assert_identical(expected, actual) def test_coords_setitem_multiindex(self): data = create_test_multiindex() with raises_regex(ValueError, "conflicting MultiIndex"): data.coords["level_1"] = range(4) def test_coords_set(self): one_coord = Dataset({"x": ("x", [0]), "yy": ("x", [1]), "zzz": ("x", [2])}) two_coords = Dataset({"zzz": ("x", [2])}, {"x": ("x", [0]), "yy": ("x", [1])}) all_coords = Dataset( coords={"x": ("x", [0]), "yy": ("x", [1]), "zzz": ("x", [2])} ) actual = one_coord.set_coords("x") assert_identical(one_coord, actual) actual = one_coord.set_coords(["x"]) assert_identical(one_coord, actual) actual = one_coord.set_coords("yy") assert_identical(two_coords, actual) actual = one_coord.set_coords(["yy", "zzz"]) assert_identical(all_coords, actual) actual = one_coord.reset_coords() assert_identical(one_coord, actual) actual = two_coords.reset_coords() assert_identical(one_coord, actual) actual = all_coords.reset_coords() assert_identical(one_coord, actual) actual = all_coords.reset_coords(["yy", "zzz"]) assert_identical(one_coord, actual) actual = all_coords.reset_coords("zzz") assert_identical(two_coords, actual) with raises_regex(ValueError, "cannot remove index"): one_coord.reset_coords("x") actual = all_coords.reset_coords("zzz", drop=True) expected = all_coords.drop_vars("zzz") assert_identical(expected, actual) expected = two_coords.drop_vars("zzz") assert_identical(expected, actual) def test_coords_to_dataset(self): orig = Dataset({"foo": ("y", [-1, 0, 1])}, {"x": 10, "y": [2, 3, 4]}) expected = Dataset(coords={"x": 10, "y": [2, 3, 4]}) actual = orig.coords.to_dataset() assert_identical(expected, actual) def test_coords_merge(self): orig_coords = Dataset(coords={"a": ("x", [1, 2]), "x": [0, 1]}).coords other_coords = Dataset(coords={"b": ("x", ["a", "b"]), "x": [0, 1]}).coords expected = Dataset( coords={"a": ("x", [1, 2]), "b": ("x", ["a", "b"]), "x": [0, 1]} ) actual = orig_coords.merge(other_coords) assert_identical(expected, actual) actual = other_coords.merge(orig_coords) assert_identical(expected, actual) other_coords = Dataset(coords={"x": ("x", ["a"])}).coords with pytest.raises(MergeError): orig_coords.merge(other_coords) other_coords = Dataset(coords={"x": ("x", ["a", "b"])}).coords with pytest.raises(MergeError): orig_coords.merge(other_coords) other_coords = Dataset(coords={"x": ("x", ["a", "b", "c"])}).coords with pytest.raises(MergeError): orig_coords.merge(other_coords) other_coords = Dataset(coords={"a": ("x", [8, 9])}).coords expected = Dataset(coords={"x": range(2)}) actual = orig_coords.merge(other_coords) assert_identical(expected, actual) actual = other_coords.merge(orig_coords) assert_identical(expected, actual) other_coords = Dataset(coords={"x": np.nan}).coords actual = orig_coords.merge(other_coords) assert_identical(orig_coords.to_dataset(), actual) actual = other_coords.merge(orig_coords) assert_identical(orig_coords.to_dataset(), actual) def test_coords_merge_mismatched_shape(self): orig_coords = Dataset(coords={"a": ("x", [1, 1])}).coords other_coords = Dataset(coords={"a": 1}).coords expected = orig_coords.to_dataset() actual = orig_coords.merge(other_coords) assert_identical(expected, actual) other_coords = Dataset(coords={"a": ("y", [1])}).coords expected = Dataset(coords={"a": (["x", "y"], [[1], [1]])}) actual = orig_coords.merge(other_coords) assert_identical(expected, actual) actual = other_coords.merge(orig_coords) assert_identical(expected.transpose(), actual) orig_coords = Dataset(coords={"a": ("x", [np.nan])}).coords other_coords = Dataset(coords={"a": np.nan}).coords expected = orig_coords.to_dataset() actual = orig_coords.merge(other_coords) assert_identical(expected, actual) def test_data_vars_properties(self): ds = Dataset() ds["foo"] = (("x",), [1.0]) ds["bar"] = 2.0 assert set(ds.data_vars) == {"foo", "bar"} assert "foo" in ds.data_vars assert "x" not in ds.data_vars assert_identical(ds["foo"], ds.data_vars["foo"]) expected = dedent( """\ Data variables: foo (x) float64 1.0 bar float64 2.0""" ) actual = repr(ds.data_vars) assert expected == actual def test_equals_and_identical(self): data = create_test_data(seed=42) assert data.equals(data) assert data.identical(data) data2 = create_test_data(seed=42) data2.attrs["foobar"] = "baz" assert data.equals(data2) assert not data.identical(data2) del data2["time"] assert not data.equals(data2) data = create_test_data(seed=42).rename({"var1": None}) assert data.equals(data) assert data.identical(data) data2 = data.reset_coords() assert not data2.equals(data) assert not data2.identical(data) def test_equals_failures(self): data = create_test_data() assert not data.equals("foo") assert not data.identical(123) assert not data.broadcast_equals({1: 2}) def test_broadcast_equals(self): data1 = Dataset(coords={"x": 0}) data2 = Dataset(coords={"x": [0]}) assert data1.broadcast_equals(data2) assert not data1.equals(data2) assert not data1.identical(data2) def test_attrs(self): data = create_test_data(seed=42) data.attrs = {"foobar": "baz"} assert data.attrs["foobar"], "baz" assert isinstance(data.attrs, dict) @requires_dask def test_chunk(self): data = create_test_data() for v in data.variables.values(): assert isinstance(v.data, np.ndarray) assert data.chunks == {} reblocked = data.chunk() for k, v in reblocked.variables.items(): if k in reblocked.dims: assert isinstance(v.data, np.ndarray) else: assert isinstance(v.data, da.Array) expected_chunks = {"dim1": (8,), "dim2": (9,), "dim3": (10,)} assert reblocked.chunks == expected_chunks def get_dask_names(ds): return {k: v.data.name for k, v in ds.items()} orig_dask_names = get_dask_names(reblocked) reblocked = data.chunk({"time": 5, "dim1": 5, "dim2": 5, "dim3": 5}) # time is not a dim in any of the data_vars, so it # doesn't get chunked expected_chunks = {"dim1": (5, 3), "dim2": (5, 4), "dim3": (5, 5)} assert reblocked.chunks == expected_chunks # make sure dask names change when rechunking by different amounts # regression test for GH3350 new_dask_names = get_dask_names(reblocked) for k, v in new_dask_names.items(): assert v != orig_dask_names[k] reblocked = data.chunk(expected_chunks) assert reblocked.chunks == expected_chunks # reblock on already blocked data orig_dask_names = get_dask_names(reblocked) reblocked = reblocked.chunk(expected_chunks) new_dask_names = get_dask_names(reblocked) assert reblocked.chunks == expected_chunks assert_identical(reblocked, data) # recuhnking with same chunk sizes should not change names for k, v in new_dask_names.items(): assert v == orig_dask_names[k] with raises_regex(ValueError, "some chunks"): data.chunk({"foo": 10}) @requires_dask def test_dask_is_lazy(self): store = InaccessibleVariableDataStore() create_test_data().dump_to_store(store) ds = open_dataset(store).chunk() with pytest.raises(UnexpectedDataAccess): ds.load() with pytest.raises(UnexpectedDataAccess): ds["var1"].values # these should not raise UnexpectedDataAccess: ds.var1.data ds.isel(time=10) ds.isel(time=slice(10), dim1=[0]).isel(dim1=0, dim2=-1) ds.transpose() ds.mean() ds.fillna(0) ds.rename({"dim1": "foobar"}) ds.set_coords("var1") ds.drop_vars("var1") def test_isel(self): data = create_test_data() slicers = {"dim1": slice(None, None, 2), "dim2": slice(0, 2)} ret = data.isel(**slicers) # Verify that only the specified dimension was altered assert list(data.dims) == list(ret.dims) for d in data.dims: if d in slicers: assert ret.dims[d] == np.arange(data.dims[d])[slicers[d]].size else: assert data.dims[d] == ret.dims[d] # Verify that the data is what we expect for v in data.variables: assert data[v].dims == ret[v].dims assert data[v].attrs == ret[v].attrs slice_list = [slice(None)] * data[v].values.ndim for d, s in slicers.items(): if d in data[v].dims: inds = np.nonzero(np.array(data[v].dims) == d)[0] for ind in inds: slice_list[ind] = s expected = data[v].values[tuple(slice_list)] actual = ret[v].values np.testing.assert_array_equal(expected, actual) with pytest.raises(ValueError): data.isel(not_a_dim=slice(0, 2)) with raises_regex( ValueError, r"dimensions {'not_a_dim'} do not exist. Expected " r"one or more of " r"[\w\W]*'time'[\w\W]*'dim\d'[\w\W]*'dim\d'[\w\W]*'dim\d'[\w\W]*", ): data.isel(not_a_dim=slice(0, 2)) with pytest.warns( UserWarning, match=r"dimensions {'not_a_dim'} do not exist. " r"Expected one or more of " r"[\w\W]*'time'[\w\W]*'dim\d'[\w\W]*'dim\d'[\w\W]*'dim\d'[\w\W]*", ): data.isel(not_a_dim=slice(0, 2), missing_dims="warn") assert_identical(data, data.isel(not_a_dim=slice(0, 2), missing_dims="ignore")) ret = data.isel(dim1=0) assert {"time": 20, "dim2": 9, "dim3": 10} == ret.dims assert set(data.data_vars) == set(ret.data_vars) assert set(data.coords) == set(ret.coords) assert set(data.indexes) == set(ret.indexes) ret = data.isel(time=slice(2), dim1=0, dim2=slice(5)) assert {"time": 2, "dim2": 5, "dim3": 10} == ret.dims assert set(data.data_vars) == set(ret.data_vars) assert set(data.coords) == set(ret.coords) assert set(data.indexes) == set(ret.indexes) ret = data.isel(time=0, dim1=0, dim2=slice(5)) assert {"dim2": 5, "dim3": 10} == ret.dims assert set(data.data_vars) == set(ret.data_vars) assert set(data.coords) == set(ret.coords) assert set(data.indexes) == set(list(ret.indexes) + ["time"]) def test_isel_fancy(self): # isel with fancy indexing. data = create_test_data() pdim1 = [1, 2, 3] pdim2 = [4, 5, 1] pdim3 = [1, 2, 3] actual = data.isel( dim1=(("test_coord",), pdim1), dim2=(("test_coord",), pdim2), dim3=(("test_coord",), pdim3), ) assert "test_coord" in actual.dims assert actual.coords["test_coord"].shape == (len(pdim1),) # Should work with DataArray actual = data.isel( dim1=DataArray(pdim1, dims="test_coord"), dim2=(("test_coord",), pdim2), dim3=(("test_coord",), pdim3), ) assert "test_coord" in actual.dims assert actual.coords["test_coord"].shape == (len(pdim1),) expected = data.isel( dim1=(("test_coord",), pdim1), dim2=(("test_coord",), pdim2), dim3=(("test_coord",), pdim3), ) assert_identical(actual, expected) # DataArray with coordinate idx1 = DataArray(pdim1, dims=["a"], coords={"a": np.random.randn(3)}) idx2 = DataArray(pdim2, dims=["b"], coords={"b": np.random.randn(3)}) idx3 = DataArray(pdim3, dims=["c"], coords={"c": np.random.randn(3)}) # Should work with DataArray actual = data.isel(dim1=idx1, dim2=idx2, dim3=idx3) assert "a" in actual.dims assert "b" in actual.dims assert "c" in actual.dims assert "time" in actual.coords assert "dim2" in actual.coords assert "dim3" in actual.coords expected = data.isel( dim1=(("a",), pdim1), dim2=(("b",), pdim2), dim3=(("c",), pdim3) ) expected = expected.assign_coords(a=idx1["a"], b=idx2["b"], c=idx3["c"]) assert_identical(actual, expected) idx1 = DataArray(pdim1, dims=["a"], coords={"a": np.random.randn(3)}) idx2 = DataArray(pdim2, dims=["a"]) idx3 = DataArray(pdim3, dims=["a"]) # Should work with DataArray actual = data.isel(dim1=idx1, dim2=idx2, dim3=idx3) assert "a" in actual.dims assert "time" in actual.coords assert "dim2" in actual.coords assert "dim3" in actual.coords expected = data.isel( dim1=(("a",), pdim1), dim2=(("a",), pdim2), dim3=(("a",), pdim3) ) expected = expected.assign_coords(a=idx1["a"]) assert_identical(actual, expected) actual = data.isel(dim1=(("points",), pdim1), dim2=(("points",), pdim2)) assert "points" in actual.dims assert "dim3" in actual.dims assert "dim3" not in actual.data_vars np.testing.assert_array_equal(data["dim2"][pdim2], actual["dim2"]) # test that the order of the indexers doesn't matter assert_identical( data.isel(dim1=(("points",), pdim1), dim2=(("points",), pdim2)), data.isel(dim2=(("points",), pdim2), dim1=(("points",), pdim1)), ) # make sure we're raising errors in the right places with raises_regex(IndexError, "Dimensions of indexers mismatch"): data.isel(dim1=(("points",), [1, 2]), dim2=(("points",), [1, 2, 3])) with raises_regex(TypeError, "cannot use a Dataset"): data.isel(dim1=Dataset({"points": [1, 2]})) # test to be sure we keep around variables that were not indexed ds = Dataset({"x": [1, 2, 3, 4], "y": 0}) actual = ds.isel(x=(("points",), [0, 1, 2])) assert_identical(ds["y"], actual["y"]) # tests using index or DataArray as indexers stations = Dataset() stations["station"] = (("station",), ["A", "B", "C"]) stations["dim1s"] = (("station",), [1, 2, 3]) stations["dim2s"] = (("station",), [4, 5, 1]) actual = data.isel(dim1=stations["dim1s"], dim2=stations["dim2s"]) assert "station" in actual.coords assert "station" in actual.dims assert_identical(actual["station"].drop_vars(["dim2"]), stations["station"]) with raises_regex(ValueError, "conflicting values for "): data.isel( dim1=DataArray( [0, 1, 2], dims="station", coords={"station": [0, 1, 2]} ), dim2=DataArray( [0, 1, 2], dims="station", coords={"station": [0, 1, 3]} ), ) # multi-dimensional selection stations = Dataset() stations["a"] = (("a",), ["A", "B", "C"]) stations["b"] = (("b",), [0, 1]) stations["dim1s"] = (("a", "b"), [[1, 2], [2, 3], [3, 4]]) stations["dim2s"] = (("a",), [4, 5, 1]) actual = data.isel(dim1=stations["dim1s"], dim2=stations["dim2s"]) assert "a" in actual.coords assert "a" in actual.dims assert "b" in actual.coords assert "b" in actual.dims assert "dim2" in actual.coords assert "a" in actual["dim2"].dims assert_identical(actual["a"].drop_vars(["dim2"]), stations["a"]) assert_identical(actual["b"], stations["b"]) expected_var1 = data["var1"].variable[ stations["dim1s"].variable, stations["dim2s"].variable ] expected_var2 = data["var2"].variable[ stations["dim1s"].variable, stations["dim2s"].variable ] expected_var3 = data["var3"].variable[slice(None), stations["dim1s"].variable] assert_equal(actual["a"].drop_vars("dim2"), stations["a"]) assert_array_equal(actual["var1"], expected_var1) assert_array_equal(actual["var2"], expected_var2) assert_array_equal(actual["var3"], expected_var3) def test_isel_dataarray(self): """ Test for indexing by DataArray """ data = create_test_data() # indexing with DataArray with same-name coordinates. indexing_da = DataArray( np.arange(1, 4), dims=["dim1"], coords={"dim1": np.random.randn(3)} ) actual = data.isel(dim1=indexing_da) assert_identical(indexing_da["dim1"], actual["dim1"]) assert_identical(data["dim2"], actual["dim2"]) # Conflict in the dimension coordinate indexing_da = DataArray( np.arange(1, 4), dims=["dim2"], coords={"dim2": np.random.randn(3)} ) with raises_regex(IndexError, "dimension coordinate 'dim2'"): actual = data.isel(dim2=indexing_da) # Also the case for DataArray with raises_regex(IndexError, "dimension coordinate 'dim2'"): actual = data["var2"].isel(dim2=indexing_da) with raises_regex(IndexError, "dimension coordinate 'dim2'"): data["dim2"].isel(dim2=indexing_da) # same name coordinate which does not conflict indexing_da = DataArray( np.arange(1, 4), dims=["dim2"], coords={"dim2": data["dim2"].values[1:4]} ) actual = data.isel(dim2=indexing_da) assert_identical(actual["dim2"], indexing_da["dim2"]) # Silently drop conflicted (non-dimensional) coordinate of indexer indexing_da = DataArray( np.arange(1, 4), dims=["dim2"], coords={ "dim2": data["dim2"].values[1:4], "numbers": ("dim2", np.arange(2, 5)), }, ) actual = data.isel(dim2=indexing_da) assert_identical(actual["numbers"], data["numbers"]) # boolean data array with coordinate with the same name indexing_da = DataArray( np.arange(1, 10), dims=["dim2"], coords={"dim2": data["dim2"].values} ) indexing_da = indexing_da < 3 actual = data.isel(dim2=indexing_da) assert_identical(actual["dim2"], data["dim2"][:2]) # boolean data array with non-dimensioncoordinate indexing_da = DataArray( np.arange(1, 10), dims=["dim2"], coords={ "dim2": data["dim2"].values, "non_dim": (("dim2",), np.random.randn(9)), "non_dim2": 0, }, ) indexing_da = indexing_da < 3 actual = data.isel(dim2=indexing_da) assert_identical( actual["dim2"].drop_vars("non_dim").drop_vars("non_dim2"), data["dim2"][:2] ) assert_identical(actual["non_dim"], indexing_da["non_dim"][:2]) assert_identical(actual["non_dim2"], indexing_da["non_dim2"]) # non-dimension coordinate will be also attached indexing_da = DataArray( np.arange(1, 4), dims=["dim2"], coords={"non_dim": (("dim2",), np.random.randn(3))}, ) actual = data.isel(dim2=indexing_da) assert "non_dim" in actual assert "non_dim" in actual.coords # Index by a scalar DataArray indexing_da = DataArray(3, dims=[], coords={"station": 2}) actual = data.isel(dim2=indexing_da) assert "station" in actual actual = data.isel(dim2=indexing_da["station"]) assert "station" in actual # indexer generated from coordinates indexing_ds = Dataset({}, coords={"dim2": [0, 1, 2]}) with raises_regex(IndexError, "dimension coordinate 'dim2'"): actual = data.isel(dim2=indexing_ds["dim2"]) def test_sel(self): data = create_test_data() int_slicers = {"dim1": slice(None, None, 2), "dim2": slice(2), "dim3": slice(3)} loc_slicers = { "dim1": slice(None, None, 2), "dim2": slice(0, 0.5), "dim3": slice("a", "c"), } assert_equal(data.isel(**int_slicers), data.sel(**loc_slicers)) data["time"] = ("time", pd.date_range("2000-01-01", periods=20)) assert_equal(data.isel(time=0), data.sel(time="2000-01-01")) assert_equal( data.isel(time=slice(10)), data.sel(time=slice("2000-01-01", "2000-01-10")) ) assert_equal(data, data.sel(time=slice("1999", "2005"))) times = pd.date_range("2000-01-01", periods=3) assert_equal(data.isel(time=slice(3)), data.sel(time=times)) assert_equal( data.isel(time=slice(3)), data.sel(time=(data["time.dayofyear"] <= 3)) ) td = pd.to_timedelta(np.arange(3), unit="days") data = Dataset({"x": ("td", np.arange(3)), "td": td}) assert_equal(data, data.sel(td=td)) assert_equal(data, data.sel(td=slice("3 days"))) assert_equal(data.isel(td=0), data.sel(td=pd.Timedelta("0 days"))) assert_equal(data.isel(td=0), data.sel(td=pd.Timedelta("0h"))) assert_equal(data.isel(td=slice(1, 3)), data.sel(td=slice("1 days", "2 days"))) def test_sel_dataarray(self): data = create_test_data() ind = DataArray([0.0, 0.5, 1.0], dims=["dim2"]) actual = data.sel(dim2=ind) assert_equal(actual, data.isel(dim2=[0, 1, 2])) # with different dimension ind = DataArray([0.0, 0.5, 1.0], dims=["new_dim"]) actual = data.sel(dim2=ind) expected = data.isel(dim2=Variable("new_dim", [0, 1, 2])) assert "new_dim" in actual.dims assert_equal(actual, expected) # Multi-dimensional ind = DataArray([[0.0], [0.5], [1.0]], dims=["new_dim", "new_dim2"]) actual = data.sel(dim2=ind) expected = data.isel(dim2=Variable(("new_dim", "new_dim2"), [[0], [1], [2]])) assert "new_dim" in actual.dims assert "new_dim2" in actual.dims assert_equal(actual, expected) # with coordinate ind = DataArray( [0.0, 0.5, 1.0], dims=["new_dim"], coords={"new_dim": ["a", "b", "c"]} ) actual = data.sel(dim2=ind) expected = data.isel(dim2=[0, 1, 2]).rename({"dim2": "new_dim"}) assert "new_dim" in actual.dims assert "new_dim" in actual.coords assert_equal( actual.drop_vars("new_dim").drop_vars("dim2"), expected.drop_vars("new_dim") ) assert_equal(actual["new_dim"].drop_vars("dim2"), ind["new_dim"]) # with conflicted coordinate (silently ignored) ind = DataArray( [0.0, 0.5, 1.0], dims=["dim2"], coords={"dim2": ["a", "b", "c"]} ) actual = data.sel(dim2=ind) expected = data.isel(dim2=[0, 1, 2]) assert_equal(actual, expected) # with conflicted coordinate (silently ignored) ind = DataArray( [0.0, 0.5, 1.0], dims=["new_dim"], coords={"new_dim": ["a", "b", "c"], "dim2": 3}, ) actual = data.sel(dim2=ind) assert_equal( actual["new_dim"].drop_vars("dim2"), ind["new_dim"].drop_vars("dim2") ) expected = data.isel(dim2=[0, 1, 2]) expected["dim2"] = (("new_dim"), expected["dim2"].values) assert_equal(actual["dim2"].drop_vars("new_dim"), expected["dim2"]) assert actual["var1"].dims == ("dim1", "new_dim") # with non-dimensional coordinate ind = DataArray( [0.0, 0.5, 1.0], dims=["dim2"], coords={ "dim2": ["a", "b", "c"], "numbers": ("dim2", [0, 1, 2]), "new_dim": ("dim2", [1.1, 1.2, 1.3]), }, ) actual = data.sel(dim2=ind) expected = data.isel(dim2=[0, 1, 2]) assert_equal(actual.drop_vars("new_dim"), expected) assert np.allclose(actual["new_dim"].values, ind["new_dim"].values) def test_sel_dataarray_mindex(self): midx = pd.MultiIndex.from_product([list("abc"), [0, 1]], names=("one", "two")) mds = xr.Dataset( {"var": (("x", "y"), np.random.rand(6, 3))}, coords={"x": midx, "y": range(3)}, ) actual_isel = mds.isel(x=xr.DataArray(np.arange(3), dims="x")) actual_sel = mds.sel(x=DataArray(mds.indexes["x"][:3], dims="x")) assert actual_isel["x"].dims == ("x",) assert actual_sel["x"].dims == ("x",) assert_identical(actual_isel, actual_sel) actual_isel = mds.isel(x=xr.DataArray(np.arange(3), dims="z")) actual_sel = mds.sel(x=Variable("z", mds.indexes["x"][:3])) assert actual_isel["x"].dims == ("z",) assert actual_sel["x"].dims == ("z",) assert_identical(actual_isel, actual_sel) # with coordinate actual_isel = mds.isel( x=xr.DataArray(np.arange(3), dims="z", coords={"z": [0, 1, 2]}) ) actual_sel = mds.sel( x=xr.DataArray(mds.indexes["x"][:3], dims="z", coords={"z": [0, 1, 2]}) ) assert actual_isel["x"].dims == ("z",) assert actual_sel["x"].dims == ("z",) assert_identical(actual_isel, actual_sel) # Vectorized indexing with level-variables raises an error with raises_regex(ValueError, "Vectorized selection is "): mds.sel(one=["a", "b"]) with raises_regex( ValueError, "Vectorized selection is not available along MultiIndex variable: x", ): mds.sel( x=xr.DataArray( [np.array(midx[:2]), np.array(midx[-2:])], dims=["a", "b"] ) ) def test_sel_categorical(self): ind = pd.Series(["foo", "bar"], dtype="category") df = pd.DataFrame({"ind": ind, "values": [1, 2]}) ds = df.set_index("ind").to_xarray() actual = ds.sel(ind="bar") expected = ds.isel(ind=1) assert_identical(expected, actual) def test_sel_categorical_error(self): ind = pd.Series(["foo", "bar"], dtype="category") df = pd.DataFrame({"ind": ind, "values": [1, 2]}) ds = df.set_index("ind").to_xarray() with pytest.raises(ValueError): ds.sel(ind="bar", method="nearest") with pytest.raises(ValueError): ds.sel(ind="bar", tolerance="nearest") def test_categorical_index(self): cat = pd.CategoricalIndex( ["foo", "bar", "foo"], categories=["foo", "bar", "baz", "qux", "quux", "corge"], ) ds = xr.Dataset( {"var": ("cat", np.arange(3))}, coords={"cat": ("cat", cat), "c": ("cat", [0, 1, 1])}, ) # test slice actual = ds.sel(cat="foo") expected = ds.isel(cat=[0, 2]) assert_identical(expected, actual) # make sure the conversion to the array works actual = ds.sel(cat="foo")["cat"].values assert (actual == np.array(["foo", "foo"])).all() ds = ds.set_index(index=["cat", "c"]) actual = ds.unstack("index") assert actual["var"].shape == (2, 2) def test_categorical_reindex(self): cat = pd.CategoricalIndex( ["foo", "bar", "baz"], categories=["foo", "bar", "baz", "qux", "quux", "corge"], ) ds = xr.Dataset( {"var": ("cat", np.arange(3))}, coords={"cat": ("cat", cat), "c": ("cat", [0, 1, 2])}, ) actual = ds.reindex(cat=["foo"])["cat"].values assert (actual == np.array(["foo"])).all() def test_categorical_multiindex(self): i1 = pd.Series([0, 0]) cat = pd.CategoricalDtype(categories=["foo", "baz", "bar"]) i2 = pd.Series(["baz", "bar"], dtype=cat) df = pd.DataFrame({"i1": i1, "i2": i2, "values": [1, 2]}).set_index( ["i1", "i2"] ) actual = df.to_xarray() assert actual["values"].shape == (1, 2) def test_sel_drop(self): data = Dataset({"foo": ("x", [1, 2, 3])}, {"x": [0, 1, 2]}) expected = Dataset({"foo": 1}) selected = data.sel(x=0, drop=True) assert_identical(expected, selected) expected = Dataset({"foo": 1}, {"x": 0}) selected = data.sel(x=0, drop=False) assert_identical(expected, selected) data = Dataset({"foo": ("x", [1, 2, 3])}) expected = Dataset({"foo": 1}) selected = data.sel(x=0, drop=True) assert_identical(expected, selected) def test_isel_drop(self): data = Dataset({"foo": ("x", [1, 2, 3])}, {"x": [0, 1, 2]}) expected = Dataset({"foo": 1}) selected = data.isel(x=0, drop=True) assert_identical(expected, selected) expected = Dataset({"foo": 1}, {"x": 0}) selected = data.isel(x=0, drop=False) assert_identical(expected, selected) def test_head(self): data = create_test_data() expected = data.isel(time=slice(5), dim2=slice(6)) actual = data.head(time=5, dim2=6) assert_equal(expected, actual) expected = data.isel(time=slice(0)) actual = data.head(time=0) assert_equal(expected, actual) expected = data.isel({dim: slice(6) for dim in data.dims}) actual = data.head(6) assert_equal(expected, actual) expected = data.isel({dim: slice(5) for dim in data.dims}) actual = data.head() assert_equal(expected, actual) with raises_regex(TypeError, "either dict-like or a single int"): data.head([3]) with raises_regex(TypeError, "expected integer type"): data.head(dim2=3.1) with raises_regex(ValueError, "expected positive int"): data.head(time=-3) def test_tail(self): data = create_test_data() expected = data.isel(time=slice(-5, None), dim2=slice(-6, None)) actual = data.tail(time=5, dim2=6) assert_equal(expected, actual) expected = data.isel(dim1=slice(0)) actual = data.tail(dim1=0) assert_equal(expected, actual) expected = data.isel({dim: slice(-6, None) for dim in data.dims}) actual = data.tail(6) assert_equal(expected, actual) expected = data.isel({dim: slice(-5, None) for dim in data.dims}) actual = data.tail() assert_equal(expected, actual) with raises_regex(TypeError, "either dict-like or a single int"): data.tail([3]) with raises_regex(TypeError, "expected integer type"): data.tail(dim2=3.1) with raises_regex(ValueError, "expected positive int"): data.tail(time=-3) def test_thin(self): data = create_test_data() expected = data.isel(time=slice(None, None, 5), dim2=slice(None, None, 6)) actual = data.thin(time=5, dim2=6) assert_equal(expected, actual) expected = data.isel({dim: slice(None, None, 6) for dim in data.dims}) actual = data.thin(6) assert_equal(expected, actual) with raises_regex(TypeError, "either dict-like or a single int"): data.thin([3]) with raises_regex(TypeError, "expected integer type"): data.thin(dim2=3.1) with raises_regex(ValueError, "cannot be zero"): data.thin(time=0) with raises_regex(ValueError, "expected positive int"): data.thin(time=-3) @pytest.mark.filterwarnings("ignore::DeprecationWarning") def test_sel_fancy(self): data = create_test_data() # add in a range() index data["dim1"] = data.dim1 pdim1 = [1, 2, 3] pdim2 = [4, 5, 1] pdim3 = [1, 2, 3] expected = data.isel( dim1=Variable(("test_coord",), pdim1), dim2=Variable(("test_coord",), pdim2), dim3=Variable(("test_coord"), pdim3), ) actual = data.sel( dim1=Variable(("test_coord",), data.dim1[pdim1]), dim2=Variable(("test_coord",), data.dim2[pdim2]), dim3=Variable(("test_coord",), data.dim3[pdim3]), ) assert_identical(expected, actual) # DataArray Indexer idx_t = DataArray( data["time"][[3, 2, 1]].values, dims=["a"], coords={"a": ["a", "b", "c"]} ) idx_2 = DataArray( data["dim2"][[3, 2, 1]].values, dims=["a"], coords={"a": ["a", "b", "c"]} ) idx_3 = DataArray( data["dim3"][[3, 2, 1]].values, dims=["a"], coords={"a": ["a", "b", "c"]} ) actual = data.sel(time=idx_t, dim2=idx_2, dim3=idx_3) expected = data.isel( time=Variable(("a",), [3, 2, 1]), dim2=Variable(("a",), [3, 2, 1]), dim3=Variable(("a",), [3, 2, 1]), ) expected = expected.assign_coords(a=idx_t["a"]) assert_identical(expected, actual) idx_t = DataArray( data["time"][[3, 2, 1]].values, dims=["a"], coords={"a": ["a", "b", "c"]} ) idx_2 = DataArray( data["dim2"][[2, 1, 3]].values, dims=["b"], coords={"b": [0, 1, 2]} ) idx_3 = DataArray( data["dim3"][[1, 2, 1]].values, dims=["c"], coords={"c": [0.0, 1.1, 2.2]} ) actual = data.sel(time=idx_t, dim2=idx_2, dim3=idx_3) expected = data.isel( time=Variable(("a",), [3, 2, 1]), dim2=Variable(("b",), [2, 1, 3]), dim3=Variable(("c",), [1, 2, 1]), ) expected = expected.assign_coords(a=idx_t["a"], b=idx_2["b"], c=idx_3["c"]) assert_identical(expected, actual) # test from sel_points data = Dataset({"foo": (("x", "y"), np.arange(9).reshape(3, 3))}) data.coords.update({"x": [0, 1, 2], "y": [0, 1, 2]}) expected = Dataset( {"foo": ("points", [0, 4, 8])}, coords={ "x": Variable(("points",), [0, 1, 2]), "y": Variable(("points",), [0, 1, 2]), }, ) actual = data.sel( x=Variable(("points",), [0, 1, 2]), y=Variable(("points",), [0, 1, 2]) ) assert_identical(expected, actual) expected.coords.update({"x": ("points", [0, 1, 2]), "y": ("points", [0, 1, 2])}) actual = data.sel( x=Variable(("points",), [0.1, 1.1, 2.5]), y=Variable(("points",), [0, 1.2, 2.0]), method="pad", ) assert_identical(expected, actual) idx_x = DataArray([0, 1, 2], dims=["a"], coords={"a": ["a", "b", "c"]}) idx_y = DataArray([0, 2, 1], dims=["b"], coords={"b": [0, 3, 6]}) expected_ary = data["foo"][[0, 1, 2], [0, 2, 1]] actual = data.sel(x=idx_x, y=idx_y) assert_array_equal(expected_ary, actual["foo"]) assert_identical(actual["a"].drop_vars("x"), idx_x["a"]) assert_identical(actual["b"].drop_vars("y"), idx_y["b"]) with pytest.raises(KeyError): data.sel(x=[2.5], y=[2.0], method="pad", tolerance=1e-3) def test_sel_method(self): data = create_test_data() expected = data.sel(dim2=1) actual = data.sel(dim2=0.95, method="nearest") assert_identical(expected, actual) actual = data.sel(dim2=0.95, method="nearest", tolerance=1) assert_identical(expected, actual) with pytest.raises(KeyError): actual = data.sel(dim2=np.pi, method="nearest", tolerance=0) expected = data.sel(dim2=[1.5]) actual = data.sel(dim2=[1.45], method="backfill") assert_identical(expected, actual) with raises_regex(NotImplementedError, "slice objects"): data.sel(dim2=slice(1, 3), method="ffill") with raises_regex(TypeError, "``method``"): # this should not pass silently data.sel(method=data) # cannot pass method if there is no associated coordinate with raises_regex(ValueError, "cannot supply"): data.sel(dim1=0, method="nearest") def test_loc(self): data = create_test_data() expected = data.sel(dim3="a") actual = data.loc[dict(dim3="a")] assert_identical(expected, actual) with raises_regex(TypeError, "can only lookup dict"): data.loc["a"] with pytest.raises(TypeError): data.loc[dict(dim3="a")] = 0 def test_selection_multiindex(self): mindex = pd.MultiIndex.from_product( [["a", "b"], [1, 2], [-1, -2]], names=("one", "two", "three") ) mdata = Dataset(data_vars={"var": ("x", range(8))}, coords={"x": mindex}) def test_sel(lab_indexer, pos_indexer, replaced_idx=False, renamed_dim=None): ds = mdata.sel(x=lab_indexer) expected_ds = mdata.isel(x=pos_indexer) if not replaced_idx: assert_identical(ds, expected_ds) else: if renamed_dim: assert ds["var"].dims[0] == renamed_dim ds = ds.rename({renamed_dim: "x"}) assert_identical(ds["var"].variable, expected_ds["var"].variable) assert not ds["x"].equals(expected_ds["x"]) test_sel(("a", 1, -1), 0) test_sel(("b", 2, -2), -1) test_sel(("a", 1), [0, 1], replaced_idx=True, renamed_dim="three") test_sel(("a",), range(4), replaced_idx=True) test_sel("a", range(4), replaced_idx=True) test_sel([("a", 1, -1), ("b", 2, -2)], [0, 7]) test_sel(slice("a", "b"), range(8)) test_sel(slice(("a", 1), ("b", 1)), range(6)) test_sel({"one": "a", "two": 1, "three": -1}, 0) test_sel({"one": "a", "two": 1}, [0, 1], replaced_idx=True, renamed_dim="three") test_sel({"one": "a"}, range(4), replaced_idx=True) assert_identical(mdata.loc[{"x": {"one": "a"}}], mdata.sel(x={"one": "a"})) assert_identical(mdata.loc[{"x": "a"}], mdata.sel(x="a")) assert_identical(mdata.loc[{"x": ("a", 1)}], mdata.sel(x=("a", 1))) assert_identical(mdata.loc[{"x": ("a", 1, -1)}], mdata.sel(x=("a", 1, -1))) assert_identical(mdata.sel(x={"one": "a", "two": 1}), mdata.sel(one="a", two=1)) def test_broadcast_like(self): original1 = DataArray( np.random.randn(5), [("x", range(5))], name="a" ).to_dataset() original2 = DataArray(np.random.randn(6), [("y", range(6))], name="b") expected1, expected2 = broadcast(original1, original2) assert_identical( original1.broadcast_like(original2), expected1.transpose("y", "x") ) assert_identical(original2.broadcast_like(original1), expected2) def test_reindex_like(self): data = create_test_data() data["letters"] = ("dim3", 10 * ["a"]) expected = data.isel(dim1=slice(10), time=slice(13)) actual = data.reindex_like(expected) assert_identical(actual, expected) expected = data.copy(deep=True) expected["dim3"] = ("dim3", list("cdefghijkl")) expected["var3"][:-2] = expected["var3"][2:].values expected["var3"][-2:] = np.nan expected["letters"] = expected["letters"].astype(object) expected["letters"][-2:] = np.nan expected["numbers"] = expected["numbers"].astype(float) expected["numbers"][:-2] = expected["numbers"][2:].values expected["numbers"][-2:] = np.nan actual = data.reindex_like(expected) assert_identical(actual, expected) def test_reindex(self): data = create_test_data() assert_identical(data, data.reindex()) expected = data.assign_coords(dim1=data["dim1"]) actual = data.reindex(dim1=data["dim1"]) assert_identical(actual, expected) actual = data.reindex(dim1=data["dim1"].values) assert_identical(actual, expected) actual = data.reindex(dim1=data["dim1"].to_index()) assert_identical(actual, expected) with raises_regex(ValueError, "cannot reindex or align along dimension"): data.reindex(dim1=data["dim1"][:5]) expected = data.isel(dim2=slice(5)) actual = data.reindex(dim2=data["dim2"][:5]) assert_identical(actual, expected) # test dict-like argument actual = data.reindex({"dim2": data["dim2"]}) expected = data assert_identical(actual, expected) with raises_regex(ValueError, "cannot specify both"): data.reindex({"x": 0}, x=0) with raises_regex(ValueError, "dictionary"): data.reindex("foo") # invalid dimension with raises_regex(ValueError, "invalid reindex dim"): data.reindex(invalid=0) # out of order expected = data.sel(dim2=data["dim2"][:5:-1]) actual = data.reindex(dim2=data["dim2"][:5:-1]) assert_identical(actual, expected) # multiple fill values expected = data.reindex(dim2=[0.1, 2.1, 3.1, 4.1]).assign( var1=lambda ds: ds.var1.copy(data=[[-10, -10, -10, -10]] * len(ds.dim1)), var2=lambda ds: ds.var2.copy(data=[[-20, -20, -20, -20]] * len(ds.dim1)), ) actual = data.reindex( dim2=[0.1, 2.1, 3.1, 4.1], fill_value={"var1": -10, "var2": -20} ) assert_identical(actual, expected) # use the default value expected = data.reindex(dim2=[0.1, 2.1, 3.1, 4.1]).assign( var1=lambda ds: ds.var1.copy(data=[[-10, -10, -10, -10]] * len(ds.dim1)), var2=lambda ds: ds.var2.copy( data=[[np.nan, np.nan, np.nan, np.nan]] * len(ds.dim1) ), ) actual = data.reindex(dim2=[0.1, 2.1, 3.1, 4.1], fill_value={"var1": -10}) assert_identical(actual, expected) # regression test for #279 expected = Dataset({"x": ("time", np.random.randn(5))}, {"time": range(5)}) time2 = DataArray(np.arange(5), dims="time2") with pytest.raises(ValueError): actual = expected.reindex(time=time2) # another regression test ds = Dataset( {"foo": (["x", "y"], np.zeros((3, 4)))}, {"x": range(3), "y": range(4)} ) expected = Dataset( {"foo": (["x", "y"], np.zeros((3, 2)))}, {"x": [0, 1, 3], "y": [0, 1]} ) expected["foo"][-1] = np.nan actual = ds.reindex(x=[0, 1, 3], y=[0, 1]) assert_identical(expected, actual) def test_reindex_warning(self): data = create_test_data() with pytest.raises(ValueError): # DataArray with different dimension raises Future warning ind = xr.DataArray([0.0, 1.0], dims=["new_dim"], name="ind") data.reindex(dim2=ind) # Should not warn ind = xr.DataArray([0.0, 1.0], dims=["dim2"], name="ind") with pytest.warns(None) as ws: data.reindex(dim2=ind) assert len(ws) == 0 def test_reindex_variables_copied(self): data = create_test_data() reindexed_data = data.reindex(copy=False) for k in data.variables: assert reindexed_data.variables[k] is not data.variables[k] def test_reindex_method(self): ds = Dataset({"x": ("y", [10, 20]), "y": [0, 1]}) y = [-0.5, 0.5, 1.5] actual = ds.reindex(y=y, method="backfill") expected = Dataset({"x": ("y", [10, 20, np.nan]), "y": y}) assert_identical(expected, actual) actual = ds.reindex(y=y, method="backfill", tolerance=0.1) expected = Dataset({"x": ("y", 3 * [np.nan]), "y": y}) assert_identical(expected, actual) actual = ds.reindex(y=y, method="pad") expected = Dataset({"x": ("y", [np.nan, 10, 20]), "y": y}) assert_identical(expected, actual) alt = Dataset({"y": y}) actual = ds.reindex_like(alt, method="pad") assert_identical(expected, actual) @pytest.mark.parametrize("fill_value", [dtypes.NA, 2, 2.0, {"x": 2, "z": 1}]) def test_reindex_fill_value(self, fill_value): ds = Dataset({"x": ("y", [10, 20]), "z": ("y", [-20, -10]), "y": [0, 1]}) y = [0, 1, 2] actual = ds.reindex(y=y, fill_value=fill_value) if fill_value == dtypes.NA: # if we supply the default, we expect the missing value for a # float array fill_value_x = fill_value_z = np.nan elif isinstance(fill_value, dict): fill_value_x = fill_value["x"] fill_value_z = fill_value["z"] else: fill_value_x = fill_value_z = fill_value expected = Dataset( { "x": ("y", [10, 20, fill_value_x]), "z": ("y", [-20, -10, fill_value_z]), "y": y, } ) assert_identical(expected, actual) @pytest.mark.parametrize("fill_value", [dtypes.NA, 2, 2.0, {"x": 2, "z": 1}]) def test_reindex_like_fill_value(self, fill_value): ds = Dataset({"x": ("y", [10, 20]), "z": ("y", [-20, -10]), "y": [0, 1]}) y = [0, 1, 2] alt = Dataset({"y": y}) actual = ds.reindex_like(alt, fill_value=fill_value) if fill_value == dtypes.NA: # if we supply the default, we expect the missing value for a # float array fill_value_x = fill_value_z = np.nan elif isinstance(fill_value, dict): fill_value_x = fill_value["x"] fill_value_z = fill_value["z"] else: fill_value_x = fill_value_z = fill_value expected = Dataset( { "x": ("y", [10, 20, fill_value_x]), "z": ("y", [-20, -10, fill_value_z]), "y": y, } ) assert_identical(expected, actual) @pytest.mark.parametrize("fill_value", [dtypes.NA, 2, 2.0, {"foo": 2, "bar": 1}]) def test_align_fill_value(self, fill_value): x = Dataset({"foo": DataArray([1, 2], dims=["x"], coords={"x": [1, 2]})}) y = Dataset({"bar": DataArray([1, 2], dims=["x"], coords={"x": [1, 3]})}) x2, y2 = align(x, y, join="outer", fill_value=fill_value) if fill_value == dtypes.NA: # if we supply the default, we expect the missing value for a # float array fill_value_foo = fill_value_bar = np.nan elif isinstance(fill_value, dict): fill_value_foo = fill_value["foo"] fill_value_bar = fill_value["bar"] else: fill_value_foo = fill_value_bar = fill_value expected_x2 = Dataset( { "foo": DataArray( [1, 2, fill_value_foo], dims=["x"], coords={"x": [1, 2, 3]} ) } ) expected_y2 = Dataset( { "bar": DataArray( [1, fill_value_bar, 2], dims=["x"], coords={"x": [1, 2, 3]} ) } ) assert_identical(expected_x2, x2) assert_identical(expected_y2, y2) def test_align(self): left = create_test_data() right = left.copy(deep=True) right["dim3"] = ("dim3", list("cdefghijkl")) right["var3"][:-2] = right["var3"][2:].values right["var3"][-2:] = np.random.randn(*right["var3"][-2:].shape) right["numbers"][:-2] = right["numbers"][2:].values right["numbers"][-2:] = -10 intersection = list("cdefghij") union = list("abcdefghijkl") left2, right2 = align(left, right, join="inner") assert_array_equal(left2["dim3"], intersection) assert_identical(left2, right2) left2, right2 = align(left, right, join="outer") assert_array_equal(left2["dim3"], union) assert_equal(left2["dim3"].variable, right2["dim3"].variable) assert_identical(left2.sel(dim3=intersection), right2.sel(dim3=intersection)) assert np.isnan(left2["var3"][-2:]).all() assert np.isnan(right2["var3"][:2]).all() left2, right2 = align(left, right, join="left") assert_equal(left2["dim3"].variable, right2["dim3"].variable) assert_equal(left2["dim3"].variable, left["dim3"].variable) assert_identical(left2.sel(dim3=intersection), right2.sel(dim3=intersection)) assert np.isnan(right2["var3"][:2]).all() left2, right2 = align(left, right, join="right") assert_equal(left2["dim3"].variable, right2["dim3"].variable) assert_equal(left2["dim3"].variable, right["dim3"].variable) assert_identical(left2.sel(dim3=intersection), right2.sel(dim3=intersection)) assert np.isnan(left2["var3"][-2:]).all() with raises_regex(ValueError, "invalid value for join"): align(left, right, join="foobar") with pytest.raises(TypeError): align(left, right, foo="bar") def test_align_exact(self): left = xr.Dataset(coords={"x": [0, 1]}) right = xr.Dataset(coords={"x": [1, 2]}) left1, left2 = xr.align(left, left, join="exact") assert_identical(left1, left) assert_identical(left2, left) with raises_regex(ValueError, "indexes .* not equal"): xr.align(left, right, join="exact") def test_align_override(self): left = xr.Dataset(coords={"x": [0, 1, 2]}) right = xr.Dataset(coords={"x": [0.1, 1.1, 2.1], "y": [1, 2, 3]}) expected_right = xr.Dataset(coords={"x": [0, 1, 2], "y": [1, 2, 3]}) new_left, new_right = xr.align(left, right, join="override") assert_identical(left, new_left) assert_identical(new_right, expected_right) new_left, new_right = xr.align(left, right, exclude="x", join="override") assert_identical(left, new_left) assert_identical(right, new_right) new_left, new_right = xr.align( left.isel(x=0, drop=True), right, exclude="x", join="override" ) assert_identical(left.isel(x=0, drop=True), new_left) assert_identical(right, new_right) with raises_regex(ValueError, "Indexes along dimension 'x' don't have"): xr.align(left.isel(x=0).expand_dims("x"), right, join="override") def test_align_exclude(self): x = Dataset( { "foo": DataArray( [[1, 2], [3, 4]], dims=["x", "y"], coords={"x": [1, 2], "y": [3, 4]} ) } ) y = Dataset( { "bar": DataArray( [[1, 2], [3, 4]], dims=["x", "y"], coords={"x": [1, 3], "y": [5, 6]} ) } ) x2, y2 = align(x, y, exclude=["y"], join="outer") expected_x2 = Dataset( { "foo": DataArray( [[1, 2], [3, 4], [np.nan, np.nan]], dims=["x", "y"], coords={"x": [1, 2, 3], "y": [3, 4]}, ) } ) expected_y2 = Dataset( { "bar": DataArray( [[1, 2], [np.nan, np.nan], [3, 4]], dims=["x", "y"], coords={"x": [1, 2, 3], "y": [5, 6]}, ) } ) assert_identical(expected_x2, x2) assert_identical(expected_y2, y2) def test_align_nocopy(self): x = Dataset({"foo": DataArray([1, 2, 3], coords=[("x", [1, 2, 3])])}) y = Dataset({"foo": DataArray([1, 2], coords=[("x", [1, 2])])}) expected_x2 = x expected_y2 = Dataset( {"foo": DataArray([1, 2, np.nan], coords=[("x", [1, 2, 3])])} ) x2, y2 = align(x, y, copy=False, join="outer") assert_identical(expected_x2, x2) assert_identical(expected_y2, y2) assert source_ndarray(x["foo"].data) is source_ndarray(x2["foo"].data) x2, y2 = align(x, y, copy=True, join="outer") assert source_ndarray(x["foo"].data) is not source_ndarray(x2["foo"].data) assert_identical(expected_x2, x2) assert_identical(expected_y2, y2) def test_align_indexes(self): x = Dataset({"foo": DataArray([1, 2, 3], dims="x", coords=[("x", [1, 2, 3])])}) (x2,) = align(x, indexes={"x": [2, 3, 1]}) expected_x2 = Dataset( {"foo": DataArray([2, 3, 1], dims="x", coords={"x": [2, 3, 1]})} ) assert_identical(expected_x2, x2) def test_align_non_unique(self): x = Dataset({"foo": ("x", [3, 4, 5]), "x": [0, 0, 1]}) x1, x2 = align(x, x) assert x1.identical(x) and x2.identical(x) y = Dataset({"bar": ("x", [6, 7]), "x": [0, 1]}) with raises_regex(ValueError, "cannot reindex or align"): align(x, y) def test_broadcast(self): ds = Dataset( {"foo": 0, "bar": ("x", [1]), "baz": ("y", [2, 3])}, {"c": ("x", [4])} ) expected = Dataset( { "foo": (("x", "y"), [[0, 0]]), "bar": (("x", "y"), [[1, 1]]), "baz": (("x", "y"), [[2, 3]]), }, {"c": ("x", [4])}, ) (actual,) = broadcast(ds) assert_identical(expected, actual) ds_x = Dataset({"foo": ("x", [1])}) ds_y = Dataset({"bar": ("y", [2, 3])}) expected_x = Dataset({"foo": (("x", "y"), [[1, 1]])}) expected_y = Dataset({"bar": (("x", "y"), [[2, 3]])}) actual_x, actual_y = broadcast(ds_x, ds_y) assert_identical(expected_x, actual_x) assert_identical(expected_y, actual_y) array_y = ds_y["bar"] expected_y = expected_y["bar"] actual_x, actual_y = broadcast(ds_x, array_y) assert_identical(expected_x, actual_x) assert_identical(expected_y, actual_y) def test_broadcast_nocopy(self): # Test that data is not copied if not needed x = Dataset({"foo": (("x", "y"), [[1, 1]])}) y = Dataset({"bar": ("y", [2, 3])}) (actual_x,) = broadcast(x) assert_identical(x, actual_x) assert source_ndarray(actual_x["foo"].data) is source_ndarray(x["foo"].data) actual_x, actual_y = broadcast(x, y) assert_identical(x, actual_x) assert source_ndarray(actual_x["foo"].data) is source_ndarray(x["foo"].data) def test_broadcast_exclude(self): x = Dataset( { "foo": DataArray( [[1, 2], [3, 4]], dims=["x", "y"], coords={"x": [1, 2], "y": [3, 4]} ), "bar": DataArray(5), } ) y = Dataset( { "foo": DataArray( [[1, 2]], dims=["z", "y"], coords={"z": [1], "y": [5, 6]} ) } ) x2, y2 = broadcast(x, y, exclude=["y"]) expected_x2 = Dataset( { "foo": DataArray( [[[1, 2]], [[3, 4]]], dims=["x", "z", "y"], coords={"z": [1], "x": [1, 2], "y": [3, 4]}, ), "bar": DataArray( [[5], [5]], dims=["x", "z"], coords={"x": [1, 2], "z": [1]} ), } ) expected_y2 = Dataset( { "foo": DataArray( [[[1, 2]], [[1, 2]]], dims=["x", "z", "y"], coords={"z": [1], "x": [1, 2], "y": [5, 6]}, ) } ) assert_identical(expected_x2, x2) assert_identical(expected_y2, y2) def test_broadcast_misaligned(self): x = Dataset({"foo": DataArray([1, 2, 3], coords=[("x", [-1, -2, -3])])}) y = Dataset( { "bar": DataArray( [[1, 2], [3, 4]], dims=["y", "x"], coords={"y": [1, 2], "x": [10, -3]}, ) } ) x2, y2 = broadcast(x, y) expected_x2 = Dataset( { "foo": DataArray( [[3, 3], [2, 2], [1, 1], [np.nan, np.nan]], dims=["x", "y"], coords={"y": [1, 2], "x": [-3, -2, -1, 10]}, ) } ) expected_y2 = Dataset( { "bar": DataArray( [[2, 4], [np.nan, np.nan], [np.nan, np.nan], [1, 3]], dims=["x", "y"], coords={"y": [1, 2], "x": [-3, -2, -1, 10]}, ) } ) assert_identical(expected_x2, x2) assert_identical(expected_y2, y2) def test_variable_indexing(self): data = create_test_data() v = data["var1"] d1 = data["dim1"] d2 = data["dim2"] assert_equal(v, v[d1.values]) assert_equal(v, v[d1]) assert_equal(v[:3], v[d1 < 3]) assert_equal(v[:, 3:], v[:, d2 >= 1.5]) assert_equal(v[:3, 3:], v[d1 < 3, d2 >= 1.5]) assert_equal(v[:3, :2], v[range(3), range(2)]) assert_equal(v[:3, :2], v.loc[d1[:3], d2[:2]]) def test_drop_variables(self): data = create_test_data() assert_identical(data, data.drop_vars([])) expected = Dataset({k: data[k] for k in data.variables if k != "time"}) actual = data.drop_vars("time") assert_identical(expected, actual) actual = data.drop_vars(["time"]) assert_identical(expected, actual) with raises_regex(ValueError, "cannot be found"): data.drop_vars("not_found_here") actual = data.drop_vars("not_found_here", errors="ignore") assert_identical(data, actual) actual = data.drop_vars(["not_found_here"], errors="ignore") assert_identical(data, actual) actual = data.drop_vars(["time", "not_found_here"], errors="ignore") assert_identical(expected, actual) # deprecated approach with `drop` works (straight copy paste from above) with pytest.warns(PendingDeprecationWarning): actual = data.drop("not_found_here", errors="ignore") assert_identical(data, actual) with pytest.warns(PendingDeprecationWarning): actual = data.drop(["not_found_here"], errors="ignore") assert_identical(data, actual) with pytest.warns(PendingDeprecationWarning): actual = data.drop(["time", "not_found_here"], errors="ignore") assert_identical(expected, actual) with pytest.warns(PendingDeprecationWarning): actual = data.drop({"time", "not_found_here"}, errors="ignore") assert_identical(expected, actual) def test_drop_index_labels(self): data = Dataset({"A": (["x", "y"], np.random.randn(2, 3)), "x": ["a", "b"]}) with pytest.warns(DeprecationWarning): actual = data.drop(["a"], dim="x") expected = data.isel(x=[1]) assert_identical(expected, actual) with pytest.warns(DeprecationWarning): actual = data.drop(["a", "b"], dim="x") expected = data.isel(x=slice(0, 0)) assert_identical(expected, actual) with pytest.raises(KeyError): # not contained in axis with pytest.warns(DeprecationWarning): data.drop(["c"], dim="x") with pytest.warns(DeprecationWarning): actual = data.drop(["c"], dim="x", errors="ignore") assert_identical(data, actual) with pytest.raises(ValueError): with pytest.warns(DeprecationWarning): data.drop(["c"], dim="x", errors="wrong_value") with pytest.warns(DeprecationWarning): actual = data.drop(["a", "b", "c"], "x", errors="ignore") expected = data.isel(x=slice(0, 0)) assert_identical(expected, actual) # DataArrays as labels are a nasty corner case as they are not # Iterable[Hashable] - DataArray.__iter__ yields scalar DataArrays. actual = data.drop_sel(x=DataArray(["a", "b", "c"]), errors="ignore") expected = data.isel(x=slice(0, 0)) assert_identical(expected, actual) with pytest.warns(DeprecationWarning): data.drop(DataArray(["a", "b", "c"]), dim="x", errors="ignore") assert_identical(expected, actual) with raises_regex(ValueError, "does not have coordinate labels"): data.drop_sel(y=1) def test_drop_labels_by_keyword(self): data = Dataset( {"A": (["x", "y"], np.random.randn(2, 6)), "x": ["a", "b"], "y": range(6)} ) # Basic functionality. assert len(data.coords["x"]) == 2 with pytest.warns(DeprecationWarning): ds1 = data.drop(["a"], dim="x") ds2 = data.drop_sel(x="a") ds3 = data.drop_sel(x=["a"]) ds4 = data.drop_sel(x=["a", "b"]) ds5 = data.drop_sel(x=["a", "b"], y=range(0, 6, 2)) arr = DataArray(range(3), dims=["c"]) with pytest.warns(FutureWarning): data.drop(arr.coords) with pytest.warns(FutureWarning): data.drop(arr.indexes) assert_array_equal(ds1.coords["x"], ["b"]) assert_array_equal(ds2.coords["x"], ["b"]) assert_array_equal(ds3.coords["x"], ["b"]) assert ds4.coords["x"].size == 0 assert ds5.coords["x"].size == 0 assert_array_equal(ds5.coords["y"], [1, 3, 5]) # Error handling if user tries both approaches. with pytest.raises(ValueError): data.drop(labels=["a"], x="a") with pytest.raises(ValueError): data.drop(labels=["a"], dim="x", x="a") warnings.filterwarnings("ignore", r"\W*drop") with pytest.raises(ValueError): data.drop(dim="x", x="a") def test_drop_dims(self): data = xr.Dataset( { "A": (["x", "y"], np.random.randn(2, 3)), "B": ("x", np.random.randn(2)), "x": ["a", "b"], "z": np.pi, } ) actual = data.drop_dims("x") expected = data.drop_vars(["A", "B", "x"]) assert_identical(expected, actual) actual = data.drop_dims("y") expected = data.drop_vars("A") assert_identical(expected, actual) actual = data.drop_dims(["x", "y"]) expected = data.drop_vars(["A", "B", "x"]) assert_identical(expected, actual) with pytest.raises((ValueError, KeyError)): data.drop_dims("z") # not a dimension with pytest.raises((ValueError, KeyError)): data.drop_dims(None) actual = data.drop_dims("z", errors="ignore") assert_identical(data, actual) actual = data.drop_dims(None, errors="ignore") assert_identical(data, actual) with pytest.raises(ValueError): actual = data.drop_dims("z", errors="wrong_value") actual = data.drop_dims(["x", "y", "z"], errors="ignore") expected = data.drop_vars(["A", "B", "x"]) assert_identical(expected, actual) def test_copy(self): data = create_test_data() data.attrs["Test"] = [1, 2, 3] for copied in [data.copy(deep=False), copy(data)]: assert_identical(data, copied) assert data.encoding == copied.encoding # Note: IndexVariable objects with string dtype are always # copied because of xarray.core.util.safe_cast_to_index. # Limiting the test to data variables. for k in data.data_vars: v0 = data.variables[k] v1 = copied.variables[k] assert source_ndarray(v0.data) is source_ndarray(v1.data) copied["foo"] = ("z", np.arange(5)) assert "foo" not in data copied.attrs["foo"] = "bar" assert "foo" not in data.attrs assert data.attrs["Test"] is copied.attrs["Test"] for copied in [data.copy(deep=True), deepcopy(data)]: assert_identical(data, copied) for k, v0 in data.variables.items(): v1 = copied.variables[k] assert v0 is not v1 assert data.attrs["Test"] is not copied.attrs["Test"] def test_copy_with_data(self): orig = create_test_data() new_data = {k: np.random.randn(*v.shape) for k, v in orig.data_vars.items()} actual = orig.copy(data=new_data) expected = orig.copy() for k, v in new_data.items(): expected[k].data = v assert_identical(expected, actual) @pytest.mark.xfail(raises=AssertionError) @pytest.mark.parametrize( "deep, expected_orig", [ [ True, xr.DataArray( xr.IndexVariable("a", np.array([1, 2])), coords={"a": [1, 2]}, dims=["a"], ), ], [ False, xr.DataArray( xr.IndexVariable("a", np.array([999, 2])), coords={"a": [999, 2]}, dims=["a"], ), ], ], ) def test_copy_coords(self, deep, expected_orig): """The test fails for the shallow copy, and apparently only on Windows for some reason. In windows coords seem to be immutable unless it's one dataset deep copied from another.""" ds = xr.DataArray( np.ones([2, 2, 2]), coords={"a": [1, 2], "b": ["x", "y"], "c": [0, 1]}, dims=["a", "b", "c"], name="value", ).to_dataset() ds_cp = ds.copy(deep=deep) ds_cp.coords["a"].data[0] = 999 expected_cp = xr.DataArray( xr.IndexVariable("a", np.array([999, 2])), coords={"a": [999, 2]}, dims=["a"], ) assert_identical(ds_cp.coords["a"], expected_cp) assert_identical(ds.coords["a"], expected_orig) def test_copy_with_data_errors(self): orig = create_test_data() new_var1 = np.arange(orig["var1"].size).reshape(orig["var1"].shape) with raises_regex(ValueError, "Data must be dict-like"): orig.copy(data=new_var1) with raises_regex(ValueError, "only contain variables in original"): orig.copy(data={"not_in_original": new_var1}) with raises_regex(ValueError, "contain all variables in original"): orig.copy(data={"var1": new_var1}) def test_rename(self): data = create_test_data() newnames = {"var1": "renamed_var1", "dim2": "renamed_dim2"} renamed = data.rename(newnames) variables = dict(data.variables) for k, v in newnames.items(): variables[v] = variables.pop(k) for k, v in variables.items(): dims = list(v.dims) for name, newname in newnames.items(): if name in dims: dims[dims.index(name)] = newname assert_equal( Variable(dims, v.values, v.attrs), renamed[k].variable.to_base_variable(), ) assert v.encoding == renamed[k].encoding assert type(v) is type(renamed.variables[k]) # noqa: E721 assert "var1" not in renamed assert "dim2" not in renamed with raises_regex(ValueError, "cannot rename 'not_a_var'"): data.rename({"not_a_var": "nada"}) with raises_regex(ValueError, "'var1' conflicts"): data.rename({"var2": "var1"}) # verify that we can rename a variable without accessing the data var1 = data["var1"] data["var1"] = (var1.dims, InaccessibleArray(var1.values)) renamed = data.rename(newnames) with pytest.raises(UnexpectedDataAccess): renamed["renamed_var1"].values renamed_kwargs = data.rename(**newnames) assert_identical(renamed, renamed_kwargs) def test_rename_old_name(self): # regtest for GH1477 data = create_test_data() with raises_regex(ValueError, "'samecol' conflicts"): data.rename({"var1": "samecol", "var2": "samecol"}) # This shouldn't cause any problems. data.rename({"var1": "var2", "var2": "var1"}) def test_rename_same_name(self): data = create_test_data() newnames = {"var1": "var1", "dim2": "dim2"} renamed = data.rename(newnames) assert_identical(renamed, data) def test_rename_dims(self): original = Dataset({"x": ("x", [0, 1, 2]), "y": ("x", [10, 11, 12]), "z": 42}) expected = Dataset( {"x": ("x_new", [0, 1, 2]), "y": ("x_new", [10, 11, 12]), "z": 42} ) expected = expected.set_coords("x") dims_dict = {"x": "x_new"} actual = original.rename_dims(dims_dict) assert_identical(expected, actual) actual_2 = original.rename_dims(**dims_dict) assert_identical(expected, actual_2) # Test to raise ValueError dims_dict_bad = {"x_bad": "x_new"} with pytest.raises(ValueError): original.rename_dims(dims_dict_bad) with pytest.raises(ValueError): original.rename_dims({"x": "z"}) def test_rename_vars(self): original = Dataset({"x": ("x", [0, 1, 2]), "y": ("x", [10, 11, 12]), "z": 42}) expected = Dataset( {"x_new": ("x", [0, 1, 2]), "y": ("x", [10, 11, 12]), "z": 42} ) expected = expected.set_coords("x_new") name_dict = {"x": "x_new"} actual = original.rename_vars(name_dict) assert_identical(expected, actual) actual_2 = original.rename_vars(**name_dict) assert_identical(expected, actual_2) # Test to raise ValueError names_dict_bad = {"x_bad": "x_new"} with pytest.raises(ValueError): original.rename_vars(names_dict_bad) def test_rename_multiindex(self): mindex = pd.MultiIndex.from_tuples( [([1, 2]), ([3, 4])], names=["level0", "level1"] ) data = Dataset({}, {"x": mindex}) with raises_regex(ValueError, "conflicting MultiIndex"): data.rename({"x": "level0"}) @requires_cftime def test_rename_does_not_change_CFTimeIndex_type(self): # make sure CFTimeIndex is not converted to DatetimeIndex #3522 time = xr.cftime_range(start="2000", periods=6, freq="2MS", calendar="noleap") orig = Dataset(coords={"time": time}) renamed = orig.rename(time="time_new") assert "time_new" in renamed.indexes assert isinstance(renamed.indexes["time_new"], CFTimeIndex) assert renamed.indexes["time_new"].name == "time_new" # check original has not changed assert "time" in orig.indexes assert isinstance(orig.indexes["time"], CFTimeIndex) assert orig.indexes["time"].name == "time" # note: rename_dims(time="time_new") drops "ds.indexes" renamed = orig.rename_dims() assert isinstance(renamed.indexes["time"], CFTimeIndex) renamed = orig.rename_vars() assert isinstance(renamed.indexes["time"], CFTimeIndex) def test_rename_does_not_change_DatetimeIndex_type(self): # make sure DatetimeIndex is conderved on rename time = pd.date_range(start="2000", periods=6, freq="2MS") orig = Dataset(coords={"time": time}) renamed = orig.rename(time="time_new") assert "time_new" in renamed.indexes assert isinstance(renamed.indexes["time_new"], DatetimeIndex) assert renamed.indexes["time_new"].name == "time_new" # check original has not changed assert "time" in orig.indexes assert isinstance(orig.indexes["time"], DatetimeIndex) assert orig.indexes["time"].name == "time" # note: rename_dims(time="time_new") drops "ds.indexes" renamed = orig.rename_dims() assert isinstance(renamed.indexes["time"], DatetimeIndex) renamed = orig.rename_vars() assert isinstance(renamed.indexes["time"], DatetimeIndex) def test_swap_dims(self): original = Dataset({"x": [1, 2, 3], "y": ("x", list("abc")), "z": 42}) expected = Dataset({"z": 42}, {"x": ("y", [1, 2, 3]), "y": list("abc")}) actual = original.swap_dims({"x": "y"}) assert_identical(expected, actual) assert isinstance(actual.variables["y"], IndexVariable) assert isinstance(actual.variables["x"], Variable) pd.testing.assert_index_equal(actual.indexes["y"], expected.indexes["y"]) roundtripped = actual.swap_dims({"y": "x"}) assert_identical(original.set_coords("y"), roundtripped) with raises_regex(ValueError, "cannot swap"): original.swap_dims({"y": "x"}) with raises_regex(ValueError, "replacement dimension"): original.swap_dims({"x": "z"}) expected = Dataset( {"y": ("u", list("abc")), "z": 42}, coords={"x": ("u", [1, 2, 3])} ) actual = original.swap_dims({"x": "u"}) assert_identical(expected, actual) # handle multiindex case idx = pd.MultiIndex.from_arrays([list("aab"), list("yzz")], names=["y1", "y2"]) original = Dataset({"x": [1, 2, 3], "y": ("x", idx), "z": 42}) expected = Dataset({"z": 42}, {"x": ("y", [1, 2, 3]), "y": idx}) actual = original.swap_dims({"x": "y"}) assert_identical(expected, actual) assert isinstance(actual.variables["y"], IndexVariable) assert isinstance(actual.variables["x"], Variable) pd.testing.assert_index_equal(actual.indexes["y"], expected.indexes["y"]) def test_expand_dims_error(self): original = Dataset( { "x": ("a", np.random.randn(3)), "y": (["b", "a"], np.random.randn(4, 3)), "z": ("a", np.random.randn(3)), }, coords={ "a": np.linspace(0, 1, 3), "b": np.linspace(0, 1, 4), "c": np.linspace(0, 1, 5), }, attrs={"key": "entry"}, ) with raises_regex(ValueError, "already exists"): original.expand_dims(dim=["x"]) # Make sure it raises true error also for non-dimensional coordinates # which has dimension. original = original.set_coords("z") with raises_regex(ValueError, "already exists"): original.expand_dims(dim=["z"]) original = Dataset( { "x": ("a", np.random.randn(3)), "y": (["b", "a"], np.random.randn(4, 3)), "z": ("a", np.random.randn(3)), }, coords={ "a": np.linspace(0, 1, 3), "b": np.linspace(0, 1, 4), "c": np.linspace(0, 1, 5), }, attrs={"key": "entry"}, ) with raises_regex(TypeError, "value of new dimension"): original.expand_dims({"d": 3.2}) with raises_regex(ValueError, "both keyword and positional"): original.expand_dims({"d": 4}, e=4) def test_expand_dims_int(self): original = Dataset( {"x": ("a", np.random.randn(3)), "y": (["b", "a"], np.random.randn(4, 3))}, coords={ "a": np.linspace(0, 1, 3), "b": np.linspace(0, 1, 4), "c": np.linspace(0, 1, 5), }, attrs={"key": "entry"}, ) actual = original.expand_dims(["z"], [1]) expected = Dataset( { "x": original["x"].expand_dims("z", 1), "y": original["y"].expand_dims("z", 1), }, coords={ "a": np.linspace(0, 1, 3), "b": np.linspace(0, 1, 4), "c": np.linspace(0, 1, 5), }, attrs={"key": "entry"}, ) assert_identical(expected, actual) # make sure squeeze restores the original data set. roundtripped = actual.squeeze("z") assert_identical(original, roundtripped) # another test with a negative axis actual = original.expand_dims(["z"], [-1]) expected = Dataset( { "x": original["x"].expand_dims("z", -1), "y": original["y"].expand_dims("z", -1), }, coords={ "a": np.linspace(0, 1, 3), "b": np.linspace(0, 1, 4), "c": np.linspace(0, 1, 5), }, attrs={"key": "entry"}, ) assert_identical(expected, actual) # make sure squeeze restores the original data set. roundtripped = actual.squeeze("z") assert_identical(original, roundtripped) def test_expand_dims_coords(self): original = Dataset({"x": ("a", np.array([1, 2, 3]))}) expected = Dataset( {"x": (("b", "a"), np.array([[1, 2, 3], [1, 2, 3]]))}, coords={"b": [1, 2]} ) actual = original.expand_dims(dict(b=[1, 2])) assert_identical(expected, actual) assert "b" not in original._coord_names def test_expand_dims_existing_scalar_coord(self): original = Dataset({"x": 1}, {"a": 2}) expected = Dataset({"x": (("a",), [1])}, {"a": [2]}) actual = original.expand_dims("a") assert_identical(expected, actual) def test_isel_expand_dims_roundtrip(self): original = Dataset({"x": (("a",), [1])}, {"a": [2]}) actual = original.isel(a=0).expand_dims("a") assert_identical(actual, original) def test_expand_dims_mixed_int_and_coords(self): # Test expanding one dimension to have size > 1 that doesn't have # coordinates, and also expanding another dimension to have size > 1 # that DOES have coordinates. original = Dataset( {"x": ("a", np.random.randn(3)), "y": (["b", "a"], np.random.randn(4, 3))}, coords={ "a": np.linspace(0, 1, 3), "b": np.linspace(0, 1, 4), "c": np.linspace(0, 1, 5), }, ) actual = original.expand_dims({"d": 4, "e": ["l", "m", "n"]}) expected = Dataset( { "x": xr.DataArray( original["x"].values * np.ones([4, 3, 3]), coords=dict(d=range(4), e=["l", "m", "n"], a=np.linspace(0, 1, 3)), dims=["d", "e", "a"], ).drop_vars("d"), "y": xr.DataArray( original["y"].values * np.ones([4, 3, 4, 3]), coords=dict( d=range(4), e=["l", "m", "n"], b=np.linspace(0, 1, 4), a=np.linspace(0, 1, 3), ), dims=["d", "e", "b", "a"], ).drop_vars("d"), }, coords={"c": np.linspace(0, 1, 5)}, ) assert_identical(actual, expected) def test_expand_dims_kwargs_python36plus(self): original = Dataset( {"x": ("a", np.random.randn(3)), "y": (["b", "a"], np.random.randn(4, 3))}, coords={ "a": np.linspace(0, 1, 3), "b": np.linspace(0, 1, 4), "c": np.linspace(0, 1, 5), }, attrs={"key": "entry"}, ) other_way = original.expand_dims(e=["l", "m", "n"]) other_way_expected = Dataset( { "x": xr.DataArray( original["x"].values * np.ones([3, 3]), coords=dict(e=["l", "m", "n"], a=np.linspace(0, 1, 3)), dims=["e", "a"], ), "y": xr.DataArray( original["y"].values * np.ones([3, 4, 3]), coords=dict( e=["l", "m", "n"], b=np.linspace(0, 1, 4), a=np.linspace(0, 1, 3), ), dims=["e", "b", "a"], ), }, coords={"c": np.linspace(0, 1, 5)}, attrs={"key": "entry"}, ) assert_identical(other_way_expected, other_way) def test_set_index(self): expected = create_test_multiindex() mindex = expected["x"].to_index() indexes = [mindex.get_level_values(n) for n in mindex.names] coords = {idx.name: ("x", idx) for idx in indexes} ds = Dataset({}, coords=coords) obj = ds.set_index(x=mindex.names) assert_identical(obj, expected) with pytest.raises(TypeError): ds.set_index(x=mindex.names, inplace=True) assert_identical(ds, expected) # ensure set_index with no existing index and a single data var given # doesn't return multi-index ds = Dataset(data_vars={"x_var": ("x", [0, 1, 2])}) expected = Dataset(coords={"x": [0, 1, 2]}) assert_identical(ds.set_index(x="x_var"), expected) # Issue 3176: Ensure clear error message on key error. with pytest.raises(ValueError) as excinfo: ds.set_index(foo="bar") assert str(excinfo.value) == "bar is not the name of an existing variable." def test_reset_index(self): ds = create_test_multiindex() mindex = ds["x"].to_index() indexes = [mindex.get_level_values(n) for n in mindex.names] coords = {idx.name: ("x", idx) for idx in indexes} expected = Dataset({}, coords=coords) obj = ds.reset_index("x") assert_identical(obj, expected) with pytest.raises(TypeError): ds.reset_index("x", inplace=True) def test_reset_index_keep_attrs(self): coord_1 = DataArray([1, 2], dims=["coord_1"], attrs={"attrs": True}) ds = Dataset({}, {"coord_1": coord_1}) expected = Dataset({}, {"coord_1_": coord_1}) obj = ds.reset_index("coord_1") assert_identical(expected, obj) def test_reorder_levels(self): ds = create_test_multiindex() mindex = ds["x"].to_index() midx = mindex.reorder_levels(["level_2", "level_1"]) expected = Dataset({}, coords={"x": midx}) reindexed = ds.reorder_levels(x=["level_2", "level_1"]) assert_identical(reindexed, expected) with pytest.raises(TypeError): ds.reorder_levels(x=["level_2", "level_1"], inplace=True) ds = Dataset({}, coords={"x": [1, 2]}) with raises_regex(ValueError, "has no MultiIndex"): ds.reorder_levels(x=["level_1", "level_2"]) def test_stack(self): ds = Dataset( {"a": ("x", [0, 1]), "b": (("x", "y"), [[0, 1], [2, 3]]), "y": ["a", "b"]} ) exp_index = pd.MultiIndex.from_product([[0, 1], ["a", "b"]], names=["x", "y"]) expected = Dataset( {"a": ("z", [0, 0, 1, 1]), "b": ("z", [0, 1, 2, 3]), "z": exp_index} ) actual = ds.stack(z=["x", "y"]) assert_identical(expected, actual) actual = ds.stack(z=[...]) assert_identical(expected, actual) # non list dims with ellipsis actual = ds.stack(z=(...,)) assert_identical(expected, actual) # ellipsis with given dim actual = ds.stack(z=[..., "y"]) assert_identical(expected, actual) exp_index = pd.MultiIndex.from_product([["a", "b"], [0, 1]], names=["y", "x"]) expected = Dataset( {"a": ("z", [0, 1, 0, 1]), "b": ("z", [0, 2, 1, 3]), "z": exp_index} ) actual = ds.stack(z=["y", "x"]) assert_identical(expected, actual) def test_unstack(self): index = pd.MultiIndex.from_product([[0, 1], ["a", "b"]], names=["x", "y"]) ds = Dataset({"b": ("z", [0, 1, 2, 3]), "z": index}) expected = Dataset( {"b": (("x", "y"), [[0, 1], [2, 3]]), "x": [0, 1], "y": ["a", "b"]} ) for dim in ["z", ["z"], None]: actual = ds.unstack(dim) assert_identical(actual, expected) def test_unstack_errors(self): ds = Dataset({"x": [1, 2, 3]}) with raises_regex(ValueError, "does not contain the dimensions"): ds.unstack("foo") with raises_regex(ValueError, "do not have a MultiIndex"): ds.unstack("x") def test_unstack_fill_value(self): ds = xr.Dataset( {"var": (("x",), np.arange(6)), "other_var": (("x",), np.arange(3, 9))}, coords={"x": [0, 1, 2] * 2, "y": (("x",), ["a"] * 3 + ["b"] * 3)}, ) # make ds incomplete ds = ds.isel(x=[0, 2, 3, 4]).set_index(index=["x", "y"]) # test fill_value actual = ds.unstack("index", fill_value=-1) expected = ds.unstack("index").fillna(-1).astype(int) assert actual["var"].dtype == int assert_equal(actual, expected) actual = ds["var"].unstack("index", fill_value=-1) expected = ds["var"].unstack("index").fillna(-1).astype(int) assert_equal(actual, expected) actual = ds.unstack("index", fill_value={"var": -1, "other_var": 1}) expected = ds.unstack("index").fillna({"var": -1, "other_var": 1}).astype(int) assert_equal(actual, expected) @requires_sparse def test_unstack_sparse(self): ds = xr.Dataset( {"var": (("x",), np.arange(6))}, coords={"x": [0, 1, 2] * 2, "y": (("x",), ["a"] * 3 + ["b"] * 3)}, ) # make ds incomplete ds = ds.isel(x=[0, 2, 3, 4]).set_index(index=["x", "y"]) # test fill_value actual = ds.unstack("index", sparse=True) expected = ds.unstack("index") assert actual["var"].variable._to_dense().equals(expected["var"].variable) assert actual["var"].data.density < 1.0 actual = ds["var"].unstack("index", sparse=True) expected = ds["var"].unstack("index") assert actual.variable._to_dense().equals(expected.variable) assert actual.data.density < 1.0 def test_stack_unstack_fast(self): ds = Dataset( { "a": ("x", [0, 1]), "b": (("x", "y"), [[0, 1], [2, 3]]), "x": [0, 1], "y": ["a", "b"], } ) actual = ds.stack(z=["x", "y"]).unstack("z") assert actual.broadcast_equals(ds) actual = ds[["b"]].stack(z=["x", "y"]).unstack("z") assert actual.identical(ds[["b"]]) def test_stack_unstack_slow(self): ds = Dataset( { "a": ("x", [0, 1]), "b": (("x", "y"), [[0, 1], [2, 3]]), "x": [0, 1], "y": ["a", "b"], } ) stacked = ds.stack(z=["x", "y"]) actual = stacked.isel(z=slice(None, None, -1)).unstack("z") assert actual.broadcast_equals(ds) stacked = ds[["b"]].stack(z=["x", "y"]) actual = stacked.isel(z=slice(None, None, -1)).unstack("z") assert actual.identical(ds[["b"]]) def test_to_stacked_array_invalid_sample_dims(self): data = xr.Dataset( data_vars={"a": (("x", "y"), [[0, 1, 2], [3, 4, 5]]), "b": ("x", [6, 7])}, coords={"y": ["u", "v", "w"]}, ) with pytest.raises(ValueError): data.to_stacked_array("features", sample_dims=["y"]) def test_to_stacked_array_name(self): name = "adf9d" # make a two dimensional dataset a, b = create_test_stacked_array() D = xr.Dataset({"a": a, "b": b}) sample_dims = ["x"] y = D.to_stacked_array("features", sample_dims, name=name) assert y.name == name def test_to_stacked_array_dtype_dims(self): # make a two dimensional dataset a, b = create_test_stacked_array() D = xr.Dataset({"a": a, "b": b}) sample_dims = ["x"] y = D.to_stacked_array("features", sample_dims) assert y.indexes["features"].levels[1].dtype == D.y.dtype assert y.dims == ("x", "features") def test_to_stacked_array_to_unstacked_dataset(self): # single dimension: regression test for GH4049 arr = xr.DataArray(np.arange(3), coords=[("x", [0, 1, 2])]) data = xr.Dataset({"a": arr, "b": arr}) stacked = data.to_stacked_array("y", sample_dims=["x"]) unstacked = stacked.to_unstacked_dataset("y") assert_identical(unstacked, data) # make a two dimensional dataset a, b = create_test_stacked_array() D = xr.Dataset({"a": a, "b": b}) sample_dims = ["x"] y = D.to_stacked_array("features", sample_dims).transpose("x", "features") x = y.to_unstacked_dataset("features") assert_identical(D, x) # test on just one sample x0 = y[0].to_unstacked_dataset("features") d0 = D.isel(x=0) assert_identical(d0, x0) def test_to_stacked_array_to_unstacked_dataset_different_dimension(self): # test when variables have different dimensionality a, b = create_test_stacked_array() sample_dims = ["x"] D = xr.Dataset({"a": a, "b": b.isel(y=0)}) y = D.to_stacked_array("features", sample_dims) x = y.to_unstacked_dataset("features") assert_identical(D, x) def test_update(self): data = create_test_data(seed=0) expected = data.copy() var2 = Variable("dim1", np.arange(8)) actual = data.update({"var2": var2}) expected["var2"] = var2 assert_identical(expected, actual) actual = data.copy() actual_result = actual.update(data) assert actual_result is actual assert_identical(expected, actual) with pytest.raises(TypeError): actual = data.update(data, inplace=False) other = Dataset(attrs={"new": "attr"}) actual = data.copy() actual.update(other) assert_identical(expected, actual) def test_update_overwrite_coords(self): data = Dataset({"a": ("x", [1, 2])}, {"b": 3}) data.update(Dataset(coords={"b": 4})) expected = Dataset({"a": ("x", [1, 2])}, {"b": 4}) assert_identical(data, expected) data = Dataset({"a": ("x", [1, 2])}, {"b": 3}) data.update(Dataset({"c": 5}, coords={"b": 4})) expected = Dataset({"a": ("x", [1, 2]), "c": 5}, {"b": 4}) assert_identical(data, expected) data = Dataset({"a": ("x", [1, 2])}, {"b": 3}) data.update({"c": DataArray(5, coords={"b": 4})}) expected = Dataset({"a": ("x", [1, 2]), "c": 5}, {"b": 3}) assert_identical(data, expected) def test_update_auto_align(self): ds = Dataset({"x": ("t", [3, 4])}, {"t": [0, 1]}) expected = Dataset({"x": ("t", [3, 4]), "y": ("t", [np.nan, 5])}, {"t": [0, 1]}) actual = ds.copy() other = {"y": ("t", [5]), "t": [1]} with raises_regex(ValueError, "conflicting sizes"): actual.update(other) actual.update(Dataset(other)) assert_identical(expected, actual) actual = ds.copy() other = Dataset({"y": ("t", [5]), "t": [100]}) actual.update(other) expected = Dataset( {"x": ("t", [3, 4]), "y": ("t", [np.nan] * 2)}, {"t": [0, 1]} ) assert_identical(expected, actual) def test_getitem(self): data = create_test_data() assert isinstance(data["var1"], DataArray) assert_equal(data["var1"].variable, data.variables["var1"]) with pytest.raises(KeyError): data["notfound"] with pytest.raises(KeyError): data[["var1", "notfound"]] actual = data[["var1", "var2"]] expected = Dataset({"var1": data["var1"], "var2": data["var2"]}) assert_equal(expected, actual) actual = data["numbers"] expected = DataArray( data["numbers"].variable, {"dim3": data["dim3"], "numbers": data["numbers"]}, dims="dim3", name="numbers", ) assert_identical(expected, actual) actual = data[dict(dim1=0)] expected = data.isel(dim1=0) assert_identical(expected, actual) def test_getitem_hashable(self): data = create_test_data() data[(3, 4)] = data["var1"] + 1 expected = data["var1"] + 1 expected.name = (3, 4) assert_identical(expected, data[(3, 4)]) with raises_regex(KeyError, "('var1', 'var2')"): data[("var1", "var2")] def test_virtual_variables_default_coords(self): dataset = Dataset({"foo": ("x", range(10))}) expected = DataArray(range(10), dims="x", name="x") actual = dataset["x"] assert_identical(expected, actual) assert isinstance(actual.variable, IndexVariable) actual = dataset[["x", "foo"]] expected = dataset.assign_coords(x=range(10)) assert_identical(expected, actual) def test_virtual_variables_time(self): # access virtual variables data = create_test_data() expected = DataArray( 1 + np.arange(20), coords=[data["time"]], dims="time", name="dayofyear" ) assert_array_equal( data["time.month"].values, data.variables["time"].to_index().month ) assert_array_equal(data["time.season"].values, "DJF") # test virtual variable math assert_array_equal(data["time.dayofyear"] + 1, 2 + np.arange(20)) assert_array_equal(np.sin(data["time.dayofyear"]), np.sin(1 + np.arange(20))) # ensure they become coordinates expected = Dataset({}, {"dayofyear": data["time.dayofyear"]}) actual = data[["time.dayofyear"]] assert_equal(expected, actual) # non-coordinate variables ds = Dataset({"t": ("x", pd.date_range("2000-01-01", periods=3))}) assert (ds["t.year"] == 2000).all() def test_virtual_variable_same_name(self): # regression test for GH367 times = pd.date_range("2000-01-01", freq="H", periods=5) data = Dataset({"time": times}) actual = data["time.time"] expected = DataArray(times.time, [("time", times)], name="time") assert_identical(actual, expected) def test_virtual_variable_multiindex(self): # access multi-index levels as virtual variables data = create_test_multiindex() expected = DataArray( ["a", "a", "b", "b"], name="level_1", coords=[data["x"].to_index()], dims="x", ) assert_identical(expected, data["level_1"]) # combine multi-index level and datetime dr_index = pd.date_range("1/1/2011", periods=4, freq="H") mindex = pd.MultiIndex.from_arrays( [["a", "a", "b", "b"], dr_index], names=("level_str", "level_date") ) data = Dataset({}, {"x": mindex}) expected = DataArray( mindex.get_level_values("level_date").hour, name="hour", coords=[mindex], dims="x", ) assert_identical(expected, data["level_date.hour"]) # attribute style access assert_identical(data.level_str, data["level_str"]) def test_time_season(self): ds = Dataset({"t": pd.date_range("2000-01-01", periods=12, freq="M")}) seas = ["DJF"] * 2 + ["MAM"] * 3 + ["JJA"] * 3 + ["SON"] * 3 + ["DJF"] assert_array_equal(seas, ds["t.season"]) def test_slice_virtual_variable(self): data = create_test_data() assert_equal( data["time.dayofyear"][:10].variable, Variable(["time"], 1 + np.arange(10)) ) assert_equal(data["time.dayofyear"][0].variable, Variable([], 1)) def test_setitem(self): # assign a variable var = Variable(["dim1"], np.random.randn(8)) data1 = create_test_data() data1["A"] = var data2 = data1.copy() data2["A"] = var assert_identical(data1, data2) # assign a dataset array dv = 2 * data2["A"] data1["B"] = dv.variable data2["B"] = dv assert_identical(data1, data2) # can't assign an ND array without dimensions with raises_regex(ValueError, "without explicit dimension names"): data2["C"] = var.values.reshape(2, 4) # but can assign a 1D array data1["C"] = var.values data2["C"] = ("C", var.values) assert_identical(data1, data2) # can assign a scalar data1["scalar"] = 0 data2["scalar"] = ([], 0) assert_identical(data1, data2) # can't use the same dimension name as a scalar var with raises_regex(ValueError, "already exists as a scalar"): data1["newvar"] = ("scalar", [3, 4, 5]) # can't resize a used dimension with raises_regex(ValueError, "arguments without labels"): data1["dim1"] = data1["dim1"][:5] # override an existing value data1["A"] = 3 * data2["A"] assert_equal(data1["A"], 3 * data2["A"]) with pytest.raises(NotImplementedError): data1[{"x": 0}] = 0 def test_setitem_pandas(self): ds = self.make_example_math_dataset() ds["x"] = np.arange(3) ds_copy = ds.copy() ds_copy["bar"] = ds["bar"].to_pandas() assert_equal(ds, ds_copy) def test_setitem_auto_align(self): ds = Dataset() ds["x"] = ("y", range(3)) ds["y"] = 1 + np.arange(3) expected = Dataset({"x": ("y", range(3)), "y": 1 + np.arange(3)}) assert_identical(ds, expected) ds["y"] = DataArray(range(3), dims="y") expected = Dataset({"x": ("y", range(3))}, {"y": range(3)}) assert_identical(ds, expected) ds["x"] = DataArray([1, 2], coords=[("y", [0, 1])]) expected = Dataset({"x": ("y", [1, 2, np.nan])}, {"y": range(3)}) assert_identical(ds, expected) ds["x"] = 42 expected = Dataset({"x": 42, "y": range(3)}) assert_identical(ds, expected) ds["x"] = DataArray([4, 5, 6, 7], coords=[("y", [0, 1, 2, 3])]) expected = Dataset({"x": ("y", [4, 5, 6])}, {"y": range(3)}) assert_identical(ds, expected) def test_setitem_dimension_override(self): # regression test for GH-3377 ds = xr.Dataset({"x": [0, 1, 2]}) ds["x"] = ds["x"][:2] expected = Dataset({"x": [0, 1]}) assert_identical(ds, expected) ds = xr.Dataset({"x": [0, 1, 2]}) ds["x"] = np.array([0, 1]) assert_identical(ds, expected) ds = xr.Dataset({"x": [0, 1, 2]}) ds.coords["x"] = [0, 1] assert_identical(ds, expected) def test_setitem_with_coords(self): # Regression test for GH:2068 ds = create_test_data() other = DataArray( np.arange(10), dims="dim3", coords={"numbers": ("dim3", np.arange(10))} ) expected = ds.copy() expected["var3"] = other.drop_vars("numbers") actual = ds.copy() actual["var3"] = other assert_identical(expected, actual) assert "numbers" in other.coords # should not change other # with alignment other = ds["var3"].isel(dim3=slice(1, -1)) other["numbers"] = ("dim3", np.arange(8)) actual = ds.copy() actual["var3"] = other assert "numbers" in other.coords # should not change other expected = ds.copy() expected["var3"] = ds["var3"].isel(dim3=slice(1, -1)) assert_identical(expected, actual) # with non-duplicate coords other = ds["var3"].isel(dim3=slice(1, -1)) other["numbers"] = ("dim3", np.arange(8)) other["position"] = ("dim3", np.arange(8)) actual = ds.copy() actual["var3"] = other assert "position" in actual assert "position" in other.coords # assigning a coordinate-only dataarray actual = ds.copy() other = actual["numbers"] other[0] = 10 actual["numbers"] = other assert actual["numbers"][0] == 10 # GH: 2099 ds = Dataset( {"var": ("x", [1, 2, 3])}, coords={"x": [0, 1, 2], "z1": ("x", [1, 2, 3]), "z2": ("x", [1, 2, 3])}, ) ds["var"] = ds["var"] * 2 assert np.allclose(ds["var"], [2, 4, 6]) def test_setitem_align_new_indexes(self): ds = Dataset({"foo": ("x", [1, 2, 3])}, {"x": [0, 1, 2]}) ds["bar"] = DataArray([2, 3, 4], [("x", [1, 2, 3])]) expected = Dataset( {"foo": ("x", [1, 2, 3]), "bar": ("x", [np.nan, 2, 3])}, {"x": [0, 1, 2]} ) assert_identical(ds, expected) def test_assign(self): ds = Dataset() actual = ds.assign(x=[0, 1, 2], y=2) expected = Dataset({"x": [0, 1, 2], "y": 2}) assert_identical(actual, expected) assert list(actual.variables) == ["x", "y"] assert_identical(ds, Dataset()) actual = actual.assign(y=lambda ds: ds.x ** 2) expected = Dataset({"y": ("x", [0, 1, 4]), "x": [0, 1, 2]}) assert_identical(actual, expected) actual = actual.assign_coords(z=2) expected = Dataset({"y": ("x", [0, 1, 4])}, {"z": 2, "x": [0, 1, 2]}) assert_identical(actual, expected) ds = Dataset({"a": ("x", range(3))}, {"b": ("x", ["A"] * 2 + ["B"])}) actual = ds.groupby("b").assign(c=lambda ds: 2 * ds.a) expected = ds.merge({"c": ("x", [0, 2, 4])}) assert_identical(actual, expected) actual = ds.groupby("b").assign(c=lambda ds: ds.a.sum()) expected = ds.merge({"c": ("x", [1, 1, 2])}) assert_identical(actual, expected) actual = ds.groupby("b").assign_coords(c=lambda ds: ds.a.sum()) expected = expected.set_coords("c") assert_identical(actual, expected) def test_assign_coords(self): ds = Dataset() actual = ds.assign(x=[0, 1, 2], y=2) actual = actual.assign_coords(x=list("abc")) expected = Dataset({"x": list("abc"), "y": 2}) assert_identical(actual, expected) actual = ds.assign(x=[0, 1, 2], y=[2, 3]) actual = actual.assign_coords({"y": [2.0, 3.0]}) expected = ds.assign(x=[0, 1, 2], y=[2.0, 3.0]) assert_identical(actual, expected) def test_assign_attrs(self): expected = Dataset(attrs=dict(a=1, b=2)) new = Dataset() actual = new.assign_attrs(a=1, b=2) assert_identical(actual, expected) assert new.attrs == {} expected.attrs["c"] = 3 new_actual = actual.assign_attrs({"c": 3}) assert_identical(new_actual, expected) assert actual.attrs == dict(a=1, b=2) def test_assign_multiindex_level(self): data = create_test_multiindex() with raises_regex(ValueError, "conflicting MultiIndex"): data.assign(level_1=range(4)) data.assign_coords(level_1=range(4)) # raise an Error when any level name is used as dimension GH:2299 with pytest.raises(ValueError): data["y"] = ("level_1", [0, 1]) def test_merge_multiindex_level(self): data = create_test_multiindex() other = Dataset({"z": ("level_1", [0, 1])}) # conflict dimension with pytest.raises(ValueError): data.merge(other) other = Dataset({"level_1": ("x", [0, 1])}) # conflict variable name with pytest.raises(ValueError): data.merge(other) def test_setitem_original_non_unique_index(self): # regression test for GH943 original = Dataset({"data": ("x", np.arange(5))}, coords={"x": [0, 1, 2, 0, 1]}) expected = Dataset({"data": ("x", np.arange(5))}, {"x": range(5)}) actual = original.copy() actual["x"] = list(range(5)) assert_identical(actual, expected) actual = original.copy() actual["x"] = ("x", list(range(5))) assert_identical(actual, expected) actual = original.copy() actual.coords["x"] = list(range(5)) assert_identical(actual, expected) def test_setitem_both_non_unique_index(self): # regression test for GH956 names = ["joaquin", "manolo", "joaquin"] values = np.random.randint(0, 256, (3, 4, 4)) array = DataArray( values, dims=["name", "row", "column"], coords=[names, range(4), range(4)] ) expected = Dataset({"first": array, "second": array}) actual = array.rename("first").to_dataset() actual["second"] = array assert_identical(expected, actual) def test_setitem_multiindex_level(self): data = create_test_multiindex() with raises_regex(ValueError, "conflicting MultiIndex"): data["level_1"] = range(4) def test_delitem(self): data = create_test_data() all_items = set(data.variables) assert set(data.variables) == all_items del data["var1"] assert set(data.variables) == all_items - {"var1"} del data["numbers"] assert set(data.variables) == all_items - {"var1", "numbers"} assert "numbers" not in data.coords expected = Dataset() actual = Dataset({"y": ("x", [1, 2])}) del actual["y"] assert_identical(expected, actual) def test_squeeze(self): data = Dataset({"foo": (["x", "y", "z"], [[[1], [2]]])}) for args in [[], [["x"]], [["x", "z"]]]: def get_args(v): return [set(args[0]) & set(v.dims)] if args else [] expected = Dataset( {k: v.squeeze(*get_args(v)) for k, v in data.variables.items()} ) expected = expected.set_coords(data.coords) assert_identical(expected, data.squeeze(*args)) # invalid squeeze with raises_regex(ValueError, "cannot select a dimension"): data.squeeze("y") def test_squeeze_drop(self): data = Dataset({"foo": ("x", [1])}, {"x": [0]}) expected = Dataset({"foo": 1}) selected = data.squeeze(drop=True) assert_identical(expected, selected) expected = Dataset({"foo": 1}, {"x": 0}) selected = data.squeeze(drop=False) assert_identical(expected, selected) data = Dataset({"foo": (("x", "y"), [[1]])}, {"x": [0], "y": [0]}) expected = Dataset({"foo": 1}) selected = data.squeeze(drop=True) assert_identical(expected, selected) expected = Dataset({"foo": ("x", [1])}, {"x": [0]}) selected = data.squeeze(dim="y", drop=True) assert_identical(expected, selected) data = Dataset({"foo": (("x",), [])}, {"x": []}) selected = data.squeeze(drop=True) assert_identical(data, selected) def test_groupby(self): data = Dataset( {"z": (["x", "y"], np.random.randn(3, 5))}, {"x": ("x", list("abc")), "c": ("x", [0, 1, 0]), "y": range(5)}, ) groupby = data.groupby("x") assert len(groupby) == 3 expected_groups = {"a": 0, "b": 1, "c": 2} assert groupby.groups == expected_groups expected_items = [ ("a", data.isel(x=0)), ("b", data.isel(x=1)), ("c", data.isel(x=2)), ] for actual, expected in zip(groupby, expected_items): assert actual[0] == expected[0] assert_equal(actual[1], expected[1]) def identity(x): return x for k in ["x", "c", "y"]: actual = data.groupby(k, squeeze=False).map(identity) assert_equal(data, actual) def test_groupby_returns_new_type(self): data = Dataset({"z": (["x", "y"], np.random.randn(3, 5))}) actual = data.groupby("x").map(lambda ds: ds["z"]) expected = data["z"] assert_identical(expected, actual) actual = data["z"].groupby("x").map(lambda x: x.to_dataset()) expected = data assert_identical(expected, actual) def test_groupby_iter(self): data = create_test_data() for n, (t, sub) in enumerate(list(data.groupby("dim1"))[:3]): assert data["dim1"][n] == t assert_equal(data["var1"][n], sub["var1"]) assert_equal(data["var2"][n], sub["var2"]) assert_equal(data["var3"][:, n], sub["var3"]) def test_groupby_errors(self): data = create_test_data() with raises_regex(TypeError, "`group` must be"): data.groupby(np.arange(10)) with raises_regex(ValueError, "length does not match"): data.groupby(data["dim1"][:3]) with raises_regex(TypeError, "`group` must be"): data.groupby(data.coords["dim1"].to_index()) def test_groupby_reduce(self): data = Dataset( { "xy": (["x", "y"], np.random.randn(3, 4)), "xonly": ("x", np.random.randn(3)), "yonly": ("y", np.random.randn(4)), "letters": ("y", ["a", "a", "b", "b"]), } ) expected = data.mean("y") expected["yonly"] = expected["yonly"].variable.set_dims({"x": 3}) actual = data.groupby("x").mean(...) assert_allclose(expected, actual) actual = data.groupby("x").mean("y") assert_allclose(expected, actual) letters = data["letters"] expected = Dataset( { "xy": data["xy"].groupby(letters).mean(...), "xonly": (data["xonly"].mean().variable.set_dims({"letters": 2})), "yonly": data["yonly"].groupby(letters).mean(), } ) actual = data.groupby("letters").mean(...) assert_allclose(expected, actual) def test_groupby_math(self): def reorder_dims(x): return x.transpose("dim1", "dim2", "dim3", "time") ds = create_test_data() ds["dim1"] = ds["dim1"] for squeeze in [True, False]: grouped = ds.groupby("dim1", squeeze=squeeze) expected = reorder_dims(ds + ds.coords["dim1"]) actual = grouped + ds.coords["dim1"] assert_identical(expected, reorder_dims(actual)) actual = ds.coords["dim1"] + grouped assert_identical(expected, reorder_dims(actual)) ds2 = 2 * ds expected = reorder_dims(ds + ds2) actual = grouped + ds2 assert_identical(expected, reorder_dims(actual)) actual = ds2 + grouped assert_identical(expected, reorder_dims(actual)) grouped = ds.groupby("numbers") zeros = DataArray([0, 0, 0, 0], [("numbers", range(4))]) expected = (ds + Variable("dim3", np.zeros(10))).transpose( "dim3", "dim1", "dim2", "time" ) actual = grouped + zeros assert_equal(expected, actual) actual = zeros + grouped assert_equal(expected, actual) with raises_regex(ValueError, "incompat.* grouped binary"): grouped + ds with raises_regex(ValueError, "incompat.* grouped binary"): ds + grouped with raises_regex(TypeError, "only support binary ops"): grouped + 1 with raises_regex(TypeError, "only support binary ops"): grouped + grouped with raises_regex(TypeError, "in-place operations"): ds += grouped ds = Dataset( { "x": ("time", np.arange(100)), "time": pd.date_range("2000-01-01", periods=100), } ) with raises_regex(ValueError, "incompat.* grouped binary"): ds + ds.groupby("time.month") def test_groupby_math_virtual(self): ds = Dataset( {"x": ("t", [1, 2, 3])}, {"t": pd.date_range("20100101", periods=3)} ) grouped = ds.groupby("t.day") actual = grouped - grouped.mean(...) expected = Dataset({"x": ("t", [0, 0, 0])}, ds[["t", "t.day"]]) assert_identical(actual, expected) def test_groupby_nan(self): # nan should be excluded from groupby ds = Dataset({"foo": ("x", [1, 2, 3, 4])}, {"bar": ("x", [1, 1, 2, np.nan])}) actual = ds.groupby("bar").mean(...) expected = Dataset({"foo": ("bar", [1.5, 3]), "bar": [1, 2]}) assert_identical(actual, expected) def test_groupby_order(self): # groupby should preserve variables order ds = Dataset() for vn in ["a", "b", "c"]: ds[vn] = DataArray(np.arange(10), dims=["t"]) data_vars_ref = list(ds.data_vars.keys()) ds = ds.groupby("t").mean(...) data_vars = list(ds.data_vars.keys()) assert data_vars == data_vars_ref # coords are now at the end of the list, so the test below fails # all_vars = list(ds.variables.keys()) # all_vars_ref = list(ds.variables.keys()) # self.assertEqual(all_vars, all_vars_ref) def test_resample_and_first(self): times = pd.date_range("2000-01-01", freq="6H", periods=10) ds = Dataset( { "foo": (["time", "x", "y"], np.random.randn(10, 5, 3)), "bar": ("time", np.random.randn(10), {"meta": "data"}), "time": times, } ) actual = ds.resample(time="1D").first(keep_attrs=True) expected = ds.isel(time=[0, 4, 8]) assert_identical(expected, actual) # upsampling expected_time = pd.date_range("2000-01-01", freq="3H", periods=19) expected = ds.reindex(time=expected_time) actual = ds.resample(time="3H") for how in ["mean", "sum", "first", "last"]: method = getattr(actual, how) result = method() assert_equal(expected, result) for method in [np.mean]: result = actual.reduce(method) assert_equal(expected, result) def test_resample_min_count(self): times = pd.date_range("2000-01-01", freq="6H", periods=10) ds = Dataset( { "foo": (["time", "x", "y"], np.random.randn(10, 5, 3)), "bar": ("time", np.random.randn(10), {"meta": "data"}), "time": times, } ) # inject nan ds["foo"] = xr.where(ds["foo"] > 2.0, np.nan, ds["foo"]) actual = ds.resample(time="1D").sum(min_count=1) expected = xr.concat( [ ds.isel(time=slice(i * 4, (i + 1) * 4)).sum("time", min_count=1) for i in range(3) ], dim=actual["time"], ) assert_equal(expected, actual) def test_resample_by_mean_with_keep_attrs(self): times = pd.date_range("2000-01-01", freq="6H", periods=10) ds = Dataset( { "foo": (["time", "x", "y"], np.random.randn(10, 5, 3)), "bar": ("time", np.random.randn(10), {"meta": "data"}), "time": times, } ) ds.attrs["dsmeta"] = "dsdata" resampled_ds = ds.resample(time="1D").mean(keep_attrs=True) actual = resampled_ds["bar"].attrs expected = ds["bar"].attrs assert expected == actual actual = resampled_ds.attrs expected = ds.attrs assert expected == actual def test_resample_loffset(self): times = pd.date_range("2000-01-01", freq="6H", periods=10) ds = Dataset( { "foo": (["time", "x", "y"], np.random.randn(10, 5, 3)), "bar": ("time", np.random.randn(10), {"meta": "data"}), "time": times, } ) ds.attrs["dsmeta"] = "dsdata" actual = ds.resample(time="24H", loffset="-12H").mean("time").time expected = xr.DataArray( ds.bar.to_series().resample("24H", loffset="-12H").mean() ).time assert_identical(expected, actual) def test_resample_by_mean_discarding_attrs(self): times = pd.date_range("2000-01-01", freq="6H", periods=10) ds = Dataset( { "foo": (["time", "x", "y"], np.random.randn(10, 5, 3)), "bar": ("time", np.random.randn(10), {"meta": "data"}), "time": times, } ) ds.attrs["dsmeta"] = "dsdata" resampled_ds = ds.resample(time="1D").mean(keep_attrs=False) assert resampled_ds["bar"].attrs == {} assert resampled_ds.attrs == {} def test_resample_by_last_discarding_attrs(self): times = pd.date_range("2000-01-01", freq="6H", periods=10) ds = Dataset( { "foo": (["time", "x", "y"], np.random.randn(10, 5, 3)), "bar": ("time", np.random.randn(10), {"meta": "data"}), "time": times, } ) ds.attrs["dsmeta"] = "dsdata" resampled_ds = ds.resample(time="1D").last(keep_attrs=False) assert resampled_ds["bar"].attrs == {} assert resampled_ds.attrs == {} @requires_scipy def test_resample_drop_nondim_coords(self): xs = np.arange(6) ys = np.arange(3) times = pd.date_range("2000-01-01", freq="6H", periods=5) data = np.tile(np.arange(5), (6, 3, 1)) xx, yy = np.meshgrid(xs * 5, ys * 2.5) tt = np.arange(len(times), dtype=int) array = DataArray(data, {"time": times, "x": xs, "y": ys}, ("x", "y", "time")) xcoord = DataArray(xx.T, {"x": xs, "y": ys}, ("x", "y")) ycoord = DataArray(yy.T, {"x": xs, "y": ys}, ("x", "y")) tcoord = DataArray(tt, {"time": times}, ("time",)) ds = Dataset({"data": array, "xc": xcoord, "yc": ycoord, "tc": tcoord}) ds = ds.set_coords(["xc", "yc", "tc"]) # Re-sample actual = ds.resample(time="12H").mean("time") assert "tc" not in actual.coords # Up-sample - filling actual = ds.resample(time="1H").ffill() assert "tc" not in actual.coords # Up-sample - interpolation actual = ds.resample(time="1H").interpolate("linear") assert "tc" not in actual.coords def test_resample_old_api(self): times = pd.date_range("2000-01-01", freq="6H", periods=10) ds = Dataset( { "foo": (["time", "x", "y"], np.random.randn(10, 5, 3)), "bar": ("time", np.random.randn(10), {"meta": "data"}), "time": times, } ) with raises_regex(TypeError, r"resample\(\) no longer supports"): ds.resample("1D", "time") with raises_regex(TypeError, r"resample\(\) no longer supports"): ds.resample("1D", dim="time", how="mean") with raises_regex(TypeError, r"resample\(\) no longer supports"): ds.resample("1D", dim="time") def test_resample_ds_da_are_the_same(self): time = pd.date_range("2000-01-01", freq="6H", periods=365 * 4) ds = xr.Dataset( { "foo": (("time", "x"), np.random.randn(365 * 4, 5)), "time": time, "x": np.arange(5), } ) assert_identical( ds.resample(time="M").mean()["foo"], ds.foo.resample(time="M").mean() ) def test_ds_resample_apply_func_args(self): def func(arg1, arg2, arg3=0.0): return arg1.mean("time") + arg2 + arg3 times = pd.date_range("2000", freq="D", periods=3) ds = xr.Dataset({"foo": ("time", [1.0, 1.0, 1.0]), "time": times}) expected = xr.Dataset({"foo": ("time", [3.0, 3.0, 3.0]), "time": times}) actual = ds.resample(time="D").map(func, args=(1.0,), arg3=1.0) assert_identical(expected, actual) def test_to_array(self): ds = Dataset( {"a": 1, "b": ("x", [1, 2, 3])}, coords={"c": 42}, attrs={"Conventions": "None"}, ) data = [[1, 1, 1], [1, 2, 3]] coords = {"c": 42, "variable": ["a", "b"]} dims = ("variable", "x") expected = DataArray(data, coords, dims, attrs=ds.attrs) actual = ds.to_array() assert_identical(expected, actual) actual = ds.to_array("abc", name="foo") expected = expected.rename({"variable": "abc"}).rename("foo") assert_identical(expected, actual) def test_to_and_from_dataframe(self): x = np.random.randn(10) y = np.random.randn(10) t = list("abcdefghij") ds = Dataset({"a": ("t", x), "b": ("t", y), "t": ("t", t)}) expected = pd.DataFrame( np.array([x, y]).T, columns=["a", "b"], index=pd.Index(t, name="t") ) actual = ds.to_dataframe() # use the .equals method to check all DataFrame metadata assert expected.equals(actual), (expected, actual) # verify coords are included actual = ds.set_coords("b").to_dataframe() assert expected.equals(actual), (expected, actual) # check roundtrip assert_identical(ds, Dataset.from_dataframe(actual)) # test a case with a MultiIndex w = np.random.randn(2, 3) ds = Dataset({"w": (("x", "y"), w)}) ds["y"] = ("y", list("abc")) exp_index = pd.MultiIndex.from_arrays( [[0, 0, 0, 1, 1, 1], ["a", "b", "c", "a", "b", "c"]], names=["x", "y"] ) expected = pd.DataFrame(w.reshape(-1), columns=["w"], index=exp_index) actual = ds.to_dataframe() assert expected.equals(actual) # check roundtrip assert_identical(ds.assign_coords(x=[0, 1]), Dataset.from_dataframe(actual)) # Check multiindex reordering new_order = ["x", "y"] actual = ds.to_dataframe(dim_order=new_order) assert expected.equals(actual) new_order = ["y", "x"] exp_index = pd.MultiIndex.from_arrays( [["a", "a", "b", "b", "c", "c"], [0, 1, 0, 1, 0, 1]], names=["y", "x"] ) expected = pd.DataFrame( w.transpose().reshape(-1), columns=["w"], index=exp_index ) actual = ds.to_dataframe(dim_order=new_order) assert expected.equals(actual) invalid_order = ["x"] with pytest.raises( ValueError, match="does not match the set of dimensions of this" ): ds.to_dataframe(dim_order=invalid_order) invalid_order = ["x", "z"] with pytest.raises( ValueError, match="does not match the set of dimensions of this" ): ds.to_dataframe(dim_order=invalid_order) # check pathological cases df = pd.DataFrame([1]) actual = Dataset.from_dataframe(df) expected = Dataset({0: ("index", [1])}, {"index": [0]}) assert_identical(expected, actual) df = pd.DataFrame() actual = Dataset.from_dataframe(df) expected = Dataset(coords={"index": []}) assert_identical(expected, actual) # GH697 df = pd.DataFrame({"A": []}) actual = Dataset.from_dataframe(df) expected = Dataset({"A": DataArray([], dims=("index",))}, {"index": []}) assert_identical(expected, actual) # regression test for GH278 # use int64 to ensure consistent results for the pandas .equals method # on windows (which requires the same dtype) ds = Dataset({"x": pd.Index(["bar"]), "a": ("y", np.array([1], "int64"))}).isel( x=0 ) # use .loc to ensure consistent results on Python 3 actual = ds.to_dataframe().loc[:, ["a", "x"]] expected = pd.DataFrame( [[1, "bar"]], index=pd.Index([0], name="y"), columns=["a", "x"] ) assert expected.equals(actual), (expected, actual) ds = Dataset({"x": np.array([0], "int64"), "y": np.array([1], "int64")}) actual = ds.to_dataframe() idx = pd.MultiIndex.from_arrays([[0], [1]], names=["x", "y"]) expected = pd.DataFrame([[]], index=idx) assert expected.equals(actual), (expected, actual) def test_from_dataframe_categorical(self): cat = pd.CategoricalDtype( categories=["foo", "bar", "baz", "qux", "quux", "corge"] ) i1 = pd.Series(["foo", "bar", "foo"], dtype=cat) i2 = pd.Series(["bar", "bar", "baz"], dtype=cat) df = pd.DataFrame({"i1": i1, "i2": i2, "values": [1, 2, 3]}) ds = df.set_index("i1").to_xarray() assert len(ds["i1"]) == 3 ds = df.set_index(["i1", "i2"]).to_xarray() assert len(ds["i1"]) == 2 assert len(ds["i2"]) == 2 @requires_sparse def test_from_dataframe_sparse(self): import sparse df_base = pd.DataFrame( {"x": range(10), "y": list("abcdefghij"), "z": np.arange(0, 100, 10)} ) ds_sparse = Dataset.from_dataframe(df_base.set_index("x"), sparse=True) ds_dense = Dataset.from_dataframe(df_base.set_index("x"), sparse=False) assert isinstance(ds_sparse["y"].data, sparse.COO) assert isinstance(ds_sparse["z"].data, sparse.COO) ds_sparse["y"].data = ds_sparse["y"].data.todense() ds_sparse["z"].data = ds_sparse["z"].data.todense() assert_identical(ds_dense, ds_sparse) ds_sparse = Dataset.from_dataframe(df_base.set_index(["x", "y"]), sparse=True) ds_dense = Dataset.from_dataframe(df_base.set_index(["x", "y"]), sparse=False) assert isinstance(ds_sparse["z"].data, sparse.COO) ds_sparse["z"].data = ds_sparse["z"].data.todense() assert_identical(ds_dense, ds_sparse) def test_to_and_from_empty_dataframe(self): # GH697 expected = pd.DataFrame({"foo": []}) ds = Dataset.from_dataframe(expected) assert len(ds["foo"]) == 0 actual = ds.to_dataframe() assert len(actual) == 0 assert expected.equals(actual) def test_from_dataframe_multiindex(self): index = pd.MultiIndex.from_product([["a", "b"], [1, 2, 3]], names=["x", "y"]) df = pd.DataFrame({"z": np.arange(6)}, index=index) expected = Dataset( {"z": (("x", "y"), [[0, 1, 2], [3, 4, 5]])}, coords={"x": ["a", "b"], "y": [1, 2, 3]}, ) actual = Dataset.from_dataframe(df) assert_identical(actual, expected) df2 = df.iloc[[3, 2, 1, 0, 4, 5], :] actual = Dataset.from_dataframe(df2) assert_identical(actual, expected) df3 = df.iloc[:4, :] expected3 = Dataset( {"z": (("x", "y"), [[0, 1, 2], [3, np.nan, np.nan]])}, coords={"x": ["a", "b"], "y": [1, 2, 3]}, ) actual = Dataset.from_dataframe(df3) assert_identical(actual, expected3) df_nonunique = df.iloc[[0, 0], :] with raises_regex(ValueError, "non-unique MultiIndex"): Dataset.from_dataframe(df_nonunique) def test_from_dataframe_unsorted_levels(self): # regression test for GH-4186 index = pd.MultiIndex( levels=[["b", "a"], ["foo"]], codes=[[0, 1], [0, 0]], names=["lev1", "lev2"] ) df = pd.DataFrame({"c1": [0, 2], "c2": [1, 3]}, index=index) expected = Dataset( { "c1": (("lev1", "lev2"), [[0], [2]]), "c2": (("lev1", "lev2"), [[1], [3]]), }, coords={"lev1": ["b", "a"], "lev2": ["foo"]}, ) actual = Dataset.from_dataframe(df) assert_identical(actual, expected) def test_from_dataframe_non_unique_columns(self): # regression test for GH449 df = pd.DataFrame(np.zeros((2, 2))) df.columns = ["foo", "foo"] with raises_regex(ValueError, "non-unique columns"): Dataset.from_dataframe(df) def test_convert_dataframe_with_many_types_and_multiindex(self): # regression test for GH737 df = pd.DataFrame( { "a": list("abc"), "b": list(range(1, 4)), "c": np.arange(3, 6).astype("u1"), "d": np.arange(4.0, 7.0, dtype="float64"), "e": [True, False, True], "f": pd.Categorical(list("abc")), "g": pd.date_range("20130101", periods=3), "h": pd.date_range("20130101", periods=3, tz="US/Eastern"), } ) df.index = pd.MultiIndex.from_product([["a"], range(3)], names=["one", "two"]) roundtripped = Dataset.from_dataframe(df).to_dataframe() # we can't do perfectly, but we should be at least as faithful as # np.asarray expected = df.apply(np.asarray) assert roundtripped.equals(expected) def test_to_and_from_dict(self): # # Dimensions: (t: 10) # Coordinates: # * t (t) U1" expected_no_data["coords"]["t"].update({"dtype": endiantype, "shape": (10,)}) expected_no_data["data_vars"]["a"].update({"dtype": "float64", "shape": (10,)}) expected_no_data["data_vars"]["b"].update({"dtype": "float64", "shape": (10,)}) actual_no_data = ds.to_dict(data=False) assert expected_no_data == actual_no_data # verify coords are included roundtrip expected_ds = ds.set_coords("b") actual = Dataset.from_dict(expected_ds.to_dict()) assert_identical(expected_ds, actual) # test some incomplete dicts: # this one has no attrs field, the dims are strings, and x, y are # np.arrays d = { "coords": {"t": {"dims": "t", "data": t}}, "dims": "t", "data_vars": {"a": {"dims": "t", "data": x}, "b": {"dims": "t", "data": y}}, } assert_identical(ds, Dataset.from_dict(d)) # this is kind of a flattened version with no coords, or data_vars d = { "a": {"dims": "t", "data": x}, "t": {"data": t, "dims": "t"}, "b": {"dims": "t", "data": y}, } assert_identical(ds, Dataset.from_dict(d)) # this one is missing some necessary information d = { "a": {"data": x}, "t": {"data": t, "dims": "t"}, "b": {"dims": "t", "data": y}, } with raises_regex(ValueError, "cannot convert dict without the key 'dims'"): Dataset.from_dict(d) def test_to_and_from_dict_with_time_dim(self): x = np.random.randn(10, 3) y = np.random.randn(10, 3) t = pd.date_range("20130101", periods=10) lat = [77.7, 83.2, 76] ds = Dataset( { "a": (["t", "lat"], x), "b": (["t", "lat"], y), "t": ("t", t), "lat": ("lat", lat), } ) roundtripped = Dataset.from_dict(ds.to_dict()) assert_identical(ds, roundtripped) def test_to_and_from_dict_with_nan_nat(self): x = np.random.randn(10, 3) y = np.random.randn(10, 3) y[2] = np.nan t = pd.Series(pd.date_range("20130101", periods=10)) t[2] = np.nan lat = [77.7, 83.2, 76] ds = Dataset( { "a": (["t", "lat"], x), "b": (["t", "lat"], y), "t": ("t", t), "lat": ("lat", lat), } ) roundtripped = Dataset.from_dict(ds.to_dict()) assert_identical(ds, roundtripped) def test_to_dict_with_numpy_attrs(self): # this doesn't need to roundtrip x = np.random.randn(10) y = np.random.randn(10) t = list("abcdefghij") attrs = { "created": np.float64(1998), "coords": np.array([37, -110.1, 100]), "maintainer": "bar", } ds = Dataset({"a": ("t", x, attrs), "b": ("t", y, attrs), "t": ("t", t)}) expected_attrs = { "created": attrs["created"].item(), "coords": attrs["coords"].tolist(), "maintainer": "bar", } actual = ds.to_dict() # check that they are identical assert expected_attrs == actual["data_vars"]["a"]["attrs"] def test_pickle(self): data = create_test_data() roundtripped = pickle.loads(pickle.dumps(data)) assert_identical(data, roundtripped) # regression test for #167: assert data.dims == roundtripped.dims def test_lazy_load(self): store = InaccessibleVariableDataStore() create_test_data().dump_to_store(store) for decode_cf in [True, False]: ds = open_dataset(store, decode_cf=decode_cf) with pytest.raises(UnexpectedDataAccess): ds.load() with pytest.raises(UnexpectedDataAccess): ds["var1"].values # these should not raise UnexpectedDataAccess: ds.isel(time=10) ds.isel(time=slice(10), dim1=[0]).isel(dim1=0, dim2=-1) def test_dropna(self): x = np.random.randn(4, 4) x[::2, 0] = np.nan y = np.random.randn(4) y[-1] = np.nan ds = Dataset({"foo": (("a", "b"), x), "bar": (("b", y))}) expected = ds.isel(a=slice(1, None, 2)) actual = ds.dropna("a") assert_identical(actual, expected) expected = ds.isel(b=slice(1, 3)) actual = ds.dropna("b") assert_identical(actual, expected) actual = ds.dropna("b", subset=["foo", "bar"]) assert_identical(actual, expected) expected = ds.isel(b=slice(1, None)) actual = ds.dropna("b", subset=["foo"]) assert_identical(actual, expected) expected = ds.isel(b=slice(3)) actual = ds.dropna("b", subset=["bar"]) assert_identical(actual, expected) actual = ds.dropna("a", subset=[]) assert_identical(actual, ds) actual = ds.dropna("a", subset=["bar"]) assert_identical(actual, ds) actual = ds.dropna("a", how="all") assert_identical(actual, ds) actual = ds.dropna("b", how="all", subset=["bar"]) expected = ds.isel(b=[0, 1, 2]) assert_identical(actual, expected) actual = ds.dropna("b", thresh=1, subset=["bar"]) assert_identical(actual, expected) actual = ds.dropna("b", thresh=2) assert_identical(actual, ds) actual = ds.dropna("b", thresh=4) expected = ds.isel(b=[1, 2, 3]) assert_identical(actual, expected) actual = ds.dropna("a", thresh=3) expected = ds.isel(a=[1, 3]) assert_identical(actual, ds) with raises_regex(ValueError, "a single dataset dimension"): ds.dropna("foo") with raises_regex(ValueError, "invalid how"): ds.dropna("a", how="somehow") with raises_regex(TypeError, "must specify how or thresh"): ds.dropna("a", how=None) def test_fillna(self): ds = Dataset({"a": ("x", [np.nan, 1, np.nan, 3])}, {"x": [0, 1, 2, 3]}) # fill with -1 actual = ds.fillna(-1) expected = Dataset({"a": ("x", [-1, 1, -1, 3])}, {"x": [0, 1, 2, 3]}) assert_identical(expected, actual) actual = ds.fillna({"a": -1}) assert_identical(expected, actual) other = Dataset({"a": -1}) actual = ds.fillna(other) assert_identical(expected, actual) actual = ds.fillna({"a": other.a}) assert_identical(expected, actual) # fill with range(4) b = DataArray(range(4), coords=[("x", range(4))]) actual = ds.fillna(b) expected = b.rename("a").to_dataset() assert_identical(expected, actual) actual = ds.fillna(expected) assert_identical(expected, actual) actual = ds.fillna(range(4)) assert_identical(expected, actual) actual = ds.fillna(b[:3]) assert_identical(expected, actual) # okay to only include some data variables ds["b"] = np.nan actual = ds.fillna({"a": -1}) expected = Dataset( {"a": ("x", [-1, 1, -1, 3]), "b": np.nan}, {"x": [0, 1, 2, 3]} ) assert_identical(expected, actual) # but new data variables is not okay with raises_regex(ValueError, "must be contained"): ds.fillna({"x": 0}) # empty argument should be OK result = ds.fillna({}) assert_identical(ds, result) result = ds.fillna(Dataset(coords={"c": 42})) expected = ds.assign_coords(c=42) assert_identical(expected, result) # groupby expected = Dataset({"a": ("x", range(4))}, {"x": [0, 1, 2, 3]}) for target in [ds, expected]: target.coords["b"] = ("x", [0, 0, 1, 1]) actual = ds.groupby("b").fillna(DataArray([0, 2], dims="b")) assert_identical(expected, actual) actual = ds.groupby("b").fillna(Dataset({"a": ("b", [0, 2])})) assert_identical(expected, actual) # attrs with groupby ds.attrs["attr"] = "ds" ds.a.attrs["attr"] = "da" actual = ds.groupby("b").fillna(Dataset({"a": ("b", [0, 2])})) assert actual.attrs == ds.attrs assert actual.a.name == "a" assert actual.a.attrs == ds.a.attrs da = DataArray(range(5), name="a", attrs={"attr": "da"}) actual = da.fillna(1) assert actual.name == "a" assert actual.attrs == da.attrs ds = Dataset({"a": da}, attrs={"attr": "ds"}) actual = ds.fillna({"a": 1}) assert actual.attrs == ds.attrs assert actual.a.name == "a" assert actual.a.attrs == ds.a.attrs @pytest.mark.parametrize( "func", [lambda x: x.clip(0, 1), lambda x: np.float64(1.0) * x, np.abs, abs] ) def test_propagate_attrs(self, func): da = DataArray(range(5), name="a", attrs={"attr": "da"}) ds = Dataset({"a": da}, attrs={"attr": "ds"}) # test defaults assert func(ds).attrs == ds.attrs with set_options(keep_attrs=False): assert func(ds).attrs != ds.attrs assert func(ds).a.attrs != ds.a.attrs with set_options(keep_attrs=False): assert func(ds).attrs != ds.attrs assert func(ds).a.attrs != ds.a.attrs with set_options(keep_attrs=True): assert func(ds).attrs == ds.attrs assert func(ds).a.attrs == ds.a.attrs def test_where(self): ds = Dataset({"a": ("x", range(5))}) expected = Dataset({"a": ("x", [np.nan, np.nan, 2, 3, 4])}) actual = ds.where(ds > 1) assert_identical(expected, actual) actual = ds.where(ds.a > 1) assert_identical(expected, actual) actual = ds.where(ds.a.values > 1) assert_identical(expected, actual) actual = ds.where(True) assert_identical(ds, actual) expected = ds.copy(deep=True) expected["a"].values = [np.nan] * 5 actual = ds.where(False) assert_identical(expected, actual) # 2d ds = Dataset({"a": (("x", "y"), [[0, 1], [2, 3]])}) expected = Dataset({"a": (("x", "y"), [[np.nan, 1], [2, 3]])}) actual = ds.where(ds > 0) assert_identical(expected, actual) # groupby ds = Dataset({"a": ("x", range(5))}, {"c": ("x", [0, 0, 1, 1, 1])}) cond = Dataset({"a": ("c", [True, False])}) expected = ds.copy(deep=True) expected["a"].values = [0, 1] + [np.nan] * 3 actual = ds.groupby("c").where(cond) assert_identical(expected, actual) # attrs with groupby ds.attrs["attr"] = "ds" ds.a.attrs["attr"] = "da" actual = ds.groupby("c").where(cond) assert actual.attrs == ds.attrs assert actual.a.name == "a" assert actual.a.attrs == ds.a.attrs # attrs da = DataArray(range(5), name="a", attrs={"attr": "da"}) actual = da.where(da.values > 1) assert actual.name == "a" assert actual.attrs == da.attrs ds = Dataset({"a": da}, attrs={"attr": "ds"}) actual = ds.where(ds > 0) assert actual.attrs == ds.attrs assert actual.a.name == "a" assert actual.a.attrs == ds.a.attrs # lambda ds = Dataset({"a": ("x", range(5))}) expected = Dataset({"a": ("x", [np.nan, np.nan, 2, 3, 4])}) actual = ds.where(lambda x: x > 1) assert_identical(expected, actual) def test_where_other(self): ds = Dataset({"a": ("x", range(5))}, {"x": range(5)}) expected = Dataset({"a": ("x", [-1, -1, 2, 3, 4])}, {"x": range(5)}) actual = ds.where(ds > 1, -1) assert_equal(expected, actual) assert actual.a.dtype == int actual = ds.where(lambda x: x > 1, -1) assert_equal(expected, actual) with raises_regex(ValueError, "cannot set"): ds.where(ds > 1, other=0, drop=True) with raises_regex(ValueError, "indexes .* are not equal"): ds.where(ds > 1, ds.isel(x=slice(3))) with raises_regex(ValueError, "exact match required"): ds.where(ds > 1, ds.assign(b=2)) def test_where_drop(self): # if drop=True # 1d # data array case array = DataArray(range(5), coords=[range(5)], dims=["x"]) expected = DataArray(range(5)[2:], coords=[range(5)[2:]], dims=["x"]) actual = array.where(array > 1, drop=True) assert_identical(expected, actual) # dataset case ds = Dataset({"a": array}) expected = Dataset({"a": expected}) actual = ds.where(ds > 1, drop=True) assert_identical(expected, actual) actual = ds.where(ds.a > 1, drop=True) assert_identical(expected, actual) with raises_regex(TypeError, "must be a"): ds.where(np.arange(5) > 1, drop=True) # 1d with odd coordinates array = DataArray( np.array([2, 7, 1, 8, 3]), coords=[np.array([3, 1, 4, 5, 9])], dims=["x"] ) expected = DataArray( np.array([7, 8, 3]), coords=[np.array([1, 5, 9])], dims=["x"] ) actual = array.where(array > 2, drop=True) assert_identical(expected, actual) # 1d multiple variables ds = Dataset({"a": (("x"), [0, 1, 2, 3]), "b": (("x"), [4, 5, 6, 7])}) expected = Dataset( {"a": (("x"), [np.nan, 1, 2, 3]), "b": (("x"), [4, 5, 6, np.nan])} ) actual = ds.where((ds > 0) & (ds < 7), drop=True) assert_identical(expected, actual) # 2d ds = Dataset({"a": (("x", "y"), [[0, 1], [2, 3]])}) expected = Dataset({"a": (("x", "y"), [[np.nan, 1], [2, 3]])}) actual = ds.where(ds > 0, drop=True) assert_identical(expected, actual) # 2d with odd coordinates ds = Dataset( {"a": (("x", "y"), [[0, 1], [2, 3]])}, coords={ "x": [4, 3], "y": [1, 2], "z": (["x", "y"], [[np.e, np.pi], [np.pi * np.e, np.pi * 3]]), }, ) expected = Dataset( {"a": (("x", "y"), [[3]])}, coords={"x": [3], "y": [2], "z": (["x", "y"], [[np.pi * 3]])}, ) actual = ds.where(ds > 2, drop=True) assert_identical(expected, actual) # 2d multiple variables ds = Dataset( {"a": (("x", "y"), [[0, 1], [2, 3]]), "b": (("x", "y"), [[4, 5], [6, 7]])} ) expected = Dataset( { "a": (("x", "y"), [[np.nan, 1], [2, 3]]), "b": (("x", "y"), [[4, 5], [6, 7]]), } ) actual = ds.where(ds > 0, drop=True) assert_identical(expected, actual) def test_where_drop_empty(self): # regression test for GH1341 array = DataArray(np.random.rand(100, 10), dims=["nCells", "nVertLevels"]) mask = DataArray(np.zeros((100,), dtype="bool"), dims="nCells") actual = array.where(mask, drop=True) expected = DataArray(np.zeros((0, 10)), dims=["nCells", "nVertLevels"]) assert_identical(expected, actual) def test_where_drop_no_indexes(self): ds = Dataset({"foo": ("x", [0.0, 1.0])}) expected = Dataset({"foo": ("x", [1.0])}) actual = ds.where(ds == 1, drop=True) assert_identical(expected, actual) def test_reduce(self): data = create_test_data() assert len(data.mean().coords) == 0 actual = data.max() expected = Dataset({k: v.max() for k, v in data.data_vars.items()}) assert_equal(expected, actual) assert_equal(data.min(dim=["dim1"]), data.min(dim="dim1")) for reduct, expected in [ ("dim2", ["dim1", "dim3", "time"]), (["dim2", "time"], ["dim1", "dim3"]), (("dim2", "time"), ["dim1", "dim3"]), ((), ["dim1", "dim2", "dim3", "time"]), ]: actual = list(data.min(dim=reduct).dims) assert actual == expected assert_equal(data.mean(dim=[]), data) def test_reduce_coords(self): # regression test for GH1470 data = xr.Dataset({"a": ("x", [1, 2, 3])}, coords={"b": 4}) expected = xr.Dataset({"a": 2}, coords={"b": 4}) actual = data.mean("x") assert_identical(actual, expected) # should be consistent actual = data["a"].mean("x").to_dataset() assert_identical(actual, expected) def test_mean_uint_dtype(self): data = xr.Dataset( { "a": (("x", "y"), np.arange(6).reshape(3, 2).astype("uint")), "b": (("x",), np.array([0.1, 0.2, np.nan])), } ) actual = data.mean("x", skipna=True) expected = xr.Dataset( {"a": data["a"].mean("x"), "b": data["b"].mean("x", skipna=True)} ) assert_identical(actual, expected) def test_reduce_bad_dim(self): data = create_test_data() with raises_regex(ValueError, "Dataset does not contain"): data.mean(dim="bad_dim") def test_reduce_cumsum(self): data = xr.Dataset( {"a": 1, "b": ("x", [1, 2]), "c": (("x", "y"), [[np.nan, 3], [0, 4]])} ) assert_identical(data.fillna(0), data.cumsum("y")) expected = xr.Dataset( {"a": 1, "b": ("x", [1, 3]), "c": (("x", "y"), [[0, 3], [0, 7]])} ) assert_identical(expected, data.cumsum()) def test_reduce_cumsum_test_dims(self): data = create_test_data() for cumfunc in ["cumsum", "cumprod"]: with raises_regex(ValueError, "Dataset does not contain"): getattr(data, cumfunc)(dim="bad_dim") # ensure dimensions are correct for reduct, expected in [ ("dim1", ["dim1", "dim2", "dim3", "time"]), ("dim2", ["dim1", "dim2", "dim3", "time"]), ("dim3", ["dim1", "dim2", "dim3", "time"]), ("time", ["dim1", "dim2", "dim3"]), ]: actual = getattr(data, cumfunc)(dim=reduct).dims assert list(actual) == expected def test_reduce_non_numeric(self): data1 = create_test_data(seed=44) data2 = create_test_data(seed=44) add_vars = {"var4": ["dim1", "dim2"]} for v, dims in sorted(add_vars.items()): size = tuple(data1.dims[d] for d in dims) data = np.random.randint(0, 100, size=size).astype(np.str_) data1[v] = (dims, data, {"foo": "variable"}) assert "var4" not in data1.mean() assert_equal(data1.mean(), data2.mean()) assert_equal(data1.mean(dim="dim1"), data2.mean(dim="dim1")) @pytest.mark.filterwarnings( "ignore:Once the behaviour of DataArray:DeprecationWarning" ) def test_reduce_strings(self): expected = Dataset({"x": "a"}) ds = Dataset({"x": ("y", ["a", "b"])}) ds.coords["y"] = [-10, 10] actual = ds.min() assert_identical(expected, actual) expected = Dataset({"x": "b"}) actual = ds.max() assert_identical(expected, actual) expected = Dataset({"x": 0}) actual = ds.argmin() assert_identical(expected, actual) expected = Dataset({"x": 1}) actual = ds.argmax() assert_identical(expected, actual) expected = Dataset({"x": -10}) actual = ds.idxmin() assert_identical(expected, actual) expected = Dataset({"x": 10}) actual = ds.idxmax() assert_identical(expected, actual) expected = Dataset({"x": b"a"}) ds = Dataset({"x": ("y", np.array(["a", "b"], "S1"))}) actual = ds.min() assert_identical(expected, actual) expected = Dataset({"x": "a"}) ds = Dataset({"x": ("y", np.array(["a", "b"], "U1"))}) actual = ds.min() assert_identical(expected, actual) def test_reduce_dtypes(self): # regression test for GH342 expected = Dataset({"x": 1}) actual = Dataset({"x": True}).sum() assert_identical(expected, actual) # regression test for GH505 expected = Dataset({"x": 3}) actual = Dataset({"x": ("y", np.array([1, 2], "uint16"))}).sum() assert_identical(expected, actual) expected = Dataset({"x": 1 + 1j}) actual = Dataset({"x": ("y", [1, 1j])}).sum() assert_identical(expected, actual) def test_reduce_keep_attrs(self): data = create_test_data() _attrs = {"attr1": "value1", "attr2": 2929} attrs = dict(_attrs) data.attrs = attrs # Test dropped attrs ds = data.mean() assert ds.attrs == {} for v in ds.data_vars.values(): assert v.attrs == {} # Test kept attrs ds = data.mean(keep_attrs=True) assert ds.attrs == attrs for k, v in ds.data_vars.items(): assert v.attrs == data[k].attrs @pytest.mark.filterwarnings( "ignore:Once the behaviour of DataArray:DeprecationWarning" ) def test_reduce_argmin(self): # regression test for #205 ds = Dataset({"a": ("x", [0, 1])}) expected = Dataset({"a": ([], 0)}) actual = ds.argmin() assert_identical(expected, actual) actual = ds.argmin("x") assert_identical(expected, actual) def test_reduce_scalars(self): ds = Dataset({"x": ("a", [2, 2]), "y": 2, "z": ("b", [2])}) expected = Dataset({"x": 0, "y": 0, "z": 0}) actual = ds.var() assert_identical(expected, actual) expected = Dataset({"x": 0, "y": 0, "z": ("b", [0])}) actual = ds.var("a") assert_identical(expected, actual) def test_reduce_only_one_axis(self): def mean_only_one_axis(x, axis): if not isinstance(axis, integer_types): raise TypeError("non-integer axis") return x.mean(axis) ds = Dataset({"a": (["x", "y"], [[0, 1, 2, 3, 4]])}) expected = Dataset({"a": ("x", [2])}) actual = ds.reduce(mean_only_one_axis, "y") assert_identical(expected, actual) with raises_regex(TypeError, "missing 1 required positional argument: 'axis'"): ds.reduce(mean_only_one_axis) with raises_regex(TypeError, "non-integer axis"): ds.reduce(mean_only_one_axis, axis=["x", "y"]) def test_reduce_no_axis(self): def total_sum(x): return np.sum(x.flatten()) ds = Dataset({"a": (["x", "y"], [[0, 1, 2, 3, 4]])}) expected = Dataset({"a": ((), 10)}) actual = ds.reduce(total_sum) assert_identical(expected, actual) with raises_regex(TypeError, "unexpected keyword argument 'axis'"): ds.reduce(total_sum, axis=0) with raises_regex(TypeError, "unexpected keyword argument 'axis'"): ds.reduce(total_sum, dim="x") def test_reduce_keepdims(self): ds = Dataset( {"a": (["x", "y"], [[0, 1, 2, 3, 4]])}, coords={ "y": [0, 1, 2, 3, 4], "x": [0], "lat": (["x", "y"], [[0, 1, 2, 3, 4]]), "c": -999.0, }, ) # Shape should match behaviour of numpy reductions with keepdims=True # Coordinates involved in the reduction should be removed actual = ds.mean(keepdims=True) expected = Dataset( {"a": (["x", "y"], np.mean(ds.a, keepdims=True))}, coords={"c": ds.c} ) assert_identical(expected, actual) actual = ds.mean("x", keepdims=True) expected = Dataset( {"a": (["x", "y"], np.mean(ds.a, axis=0, keepdims=True))}, coords={"y": ds.y, "c": ds.c}, ) assert_identical(expected, actual) @pytest.mark.parametrize("skipna", [True, False]) @pytest.mark.parametrize("q", [0.25, [0.50], [0.25, 0.75]]) def test_quantile(self, q, skipna): ds = create_test_data(seed=123) for dim in [None, "dim1", ["dim1"]]: ds_quantile = ds.quantile(q, dim=dim, skipna=skipna) if is_scalar(q): assert "quantile" not in ds_quantile.dims else: assert "quantile" in ds_quantile.dims for var, dar in ds.data_vars.items(): assert var in ds_quantile assert_identical( ds_quantile[var], dar.quantile(q, dim=dim, skipna=skipna) ) dim = ["dim1", "dim2"] ds_quantile = ds.quantile(q, dim=dim, skipna=skipna) assert "dim3" in ds_quantile.dims assert all(d not in ds_quantile.dims for d in dim) @pytest.mark.parametrize("skipna", [True, False]) def test_quantile_skipna(self, skipna): q = 0.1 dim = "time" ds = Dataset({"a": ([dim], np.arange(0, 11))}) ds = ds.where(ds >= 1) result = ds.quantile(q=q, dim=dim, skipna=skipna) value = 1.9 if skipna else np.nan expected = Dataset({"a": value}, coords={"quantile": q}) assert_identical(result, expected) @requires_bottleneck def test_rank(self): ds = create_test_data(seed=1234) # only ds.var3 depends on dim3 z = ds.rank("dim3") assert ["var3"] == list(z.data_vars) # same as dataarray version x = z.var3 y = ds.var3.rank("dim3") assert_equal(x, y) # coordinates stick assert list(z.coords) == list(ds.coords) assert list(x.coords) == list(y.coords) # invalid dim with raises_regex(ValueError, "does not contain"): x.rank("invalid_dim") def test_count(self): ds = Dataset({"x": ("a", [np.nan, 1]), "y": 0, "z": np.nan}) expected = Dataset({"x": 1, "y": 1, "z": 0}) actual = ds.count() assert_identical(expected, actual) def test_map(self): data = create_test_data() data.attrs["foo"] = "bar" assert_identical(data.map(np.mean), data.mean()) expected = data.mean(keep_attrs=True) actual = data.map(lambda x: x.mean(keep_attrs=True), keep_attrs=True) assert_identical(expected, actual) assert_identical(data.map(lambda x: x, keep_attrs=True), data.drop_vars("time")) def scale(x, multiple=1): return multiple * x actual = data.map(scale, multiple=2) assert_equal(actual["var1"], 2 * data["var1"]) assert_identical(actual["numbers"], data["numbers"]) actual = data.map(np.asarray) expected = data.drop_vars("time") # time is not used on a data var assert_equal(expected, actual) def test_apply_pending_deprecated_map(self): data = create_test_data() data.attrs["foo"] = "bar" with pytest.warns(PendingDeprecationWarning): assert_identical(data.apply(np.mean), data.mean()) def make_example_math_dataset(self): variables = { "bar": ("x", np.arange(100, 400, 100)), "foo": (("x", "y"), 1.0 * np.arange(12).reshape(3, 4)), } coords = {"abc": ("x", ["a", "b", "c"]), "y": 10 * np.arange(4)} ds = Dataset(variables, coords) ds["foo"][0, 0] = np.nan return ds def test_dataset_number_math(self): ds = self.make_example_math_dataset() assert_identical(ds, +ds) assert_identical(ds, ds + 0) assert_identical(ds, 0 + ds) assert_identical(ds, ds + np.array(0)) assert_identical(ds, np.array(0) + ds) actual = ds.copy(deep=True) actual += 0 assert_identical(ds, actual) def test_unary_ops(self): ds = self.make_example_math_dataset() assert_identical(ds.map(abs), abs(ds)) assert_identical(ds.map(lambda x: x + 4), ds + 4) for func in [ lambda x: x.isnull(), lambda x: x.round(), lambda x: x.astype(int), ]: assert_identical(ds.map(func), func(ds)) assert_identical(ds.isnull(), ~ds.notnull()) # don't actually patch these methods in with pytest.raises(AttributeError): ds.item with pytest.raises(AttributeError): ds.searchsorted def test_dataset_array_math(self): ds = self.make_example_math_dataset() expected = ds.map(lambda x: x - ds["foo"]) assert_identical(expected, ds - ds["foo"]) assert_identical(expected, -ds["foo"] + ds) assert_identical(expected, ds - ds["foo"].variable) assert_identical(expected, -ds["foo"].variable + ds) actual = ds.copy(deep=True) actual -= ds["foo"] assert_identical(expected, actual) expected = ds.map(lambda x: x + ds["bar"]) assert_identical(expected, ds + ds["bar"]) actual = ds.copy(deep=True) actual += ds["bar"] assert_identical(expected, actual) expected = Dataset({"bar": ds["bar"] + np.arange(3)}) assert_identical(expected, ds[["bar"]] + np.arange(3)) assert_identical(expected, np.arange(3) + ds[["bar"]]) def test_dataset_dataset_math(self): ds = self.make_example_math_dataset() assert_identical(ds, ds + 0 * ds) assert_identical(ds, ds + {"foo": 0, "bar": 0}) expected = ds.map(lambda x: 2 * x) assert_identical(expected, 2 * ds) assert_identical(expected, ds + ds) assert_identical(expected, ds + ds.data_vars) assert_identical(expected, ds + dict(ds.data_vars)) actual = ds.copy(deep=True) expected_id = id(actual) actual += ds assert_identical(expected, actual) assert expected_id == id(actual) assert_identical(ds == ds, ds.notnull()) subsampled = ds.isel(y=slice(2)) expected = 2 * subsampled assert_identical(expected, subsampled + ds) assert_identical(expected, ds + subsampled) def test_dataset_math_auto_align(self): ds = self.make_example_math_dataset() subset = ds.isel(y=[1, 3]) expected = 2 * subset actual = ds + subset assert_identical(expected, actual) actual = ds.isel(y=slice(1)) + ds.isel(y=slice(1, None)) expected = 2 * ds.drop_sel(y=ds.y) assert_equal(actual, expected) actual = ds + ds[["bar"]] expected = (2 * ds[["bar"]]).merge(ds.coords) assert_identical(expected, actual) assert_identical(ds + Dataset(), ds.coords.to_dataset()) assert_identical(Dataset() + Dataset(), Dataset()) ds2 = Dataset(coords={"bar": 42}) assert_identical(ds + ds2, ds.coords.merge(ds2)) # maybe unary arithmetic with empty datasets should raise instead? assert_identical(Dataset() + 1, Dataset()) actual = ds.copy(deep=True) other = ds.isel(y=slice(2)) actual += other expected = ds + other.reindex_like(ds) assert_identical(expected, actual) def test_dataset_math_errors(self): ds = self.make_example_math_dataset() with pytest.raises(TypeError): ds["foo"] += ds with pytest.raises(TypeError): ds["foo"].variable += ds with raises_regex(ValueError, "must have the same"): ds += ds[["bar"]] # verify we can rollback in-place operations if something goes wrong # nb. inplace datetime64 math actually will work with an integer array # but not floats thanks to numpy's inconsistent handling other = DataArray(np.datetime64("2000-01-01"), coords={"c": 2}) actual = ds.copy(deep=True) with pytest.raises(TypeError): actual += other assert_identical(actual, ds) def test_dataset_transpose(self): ds = Dataset( { "a": (("x", "y"), np.random.randn(3, 4)), "b": (("y", "x"), np.random.randn(4, 3)), }, coords={ "x": range(3), "y": range(4), "xy": (("x", "y"), np.random.randn(3, 4)), }, ) actual = ds.transpose() expected = Dataset( {"a": (("y", "x"), ds.a.values.T), "b": (("x", "y"), ds.b.values.T)}, coords={ "x": ds.x.values, "y": ds.y.values, "xy": (("y", "x"), ds.xy.values.T), }, ) assert_identical(expected, actual) actual = ds.transpose(...) expected = ds assert_identical(expected, actual) actual = ds.transpose("x", "y") expected = ds.map(lambda x: x.transpose("x", "y", transpose_coords=True)) assert_identical(expected, actual) ds = create_test_data() actual = ds.transpose() for k in ds.variables: assert actual[k].dims[::-1] == ds[k].dims new_order = ("dim2", "dim3", "dim1", "time") actual = ds.transpose(*new_order) for k in ds.variables: expected_dims = tuple(d for d in new_order if d in ds[k].dims) assert actual[k].dims == expected_dims # same as above but with ellipsis new_order = ("dim2", "dim3", "dim1", "time") actual = ds.transpose("dim2", "dim3", ...) for k in ds.variables: expected_dims = tuple(d for d in new_order if d in ds[k].dims) assert actual[k].dims == expected_dims with raises_regex(ValueError, "permuted"): ds.transpose("dim1", "dim2", "dim3") with raises_regex(ValueError, "permuted"): ds.transpose("dim1", "dim2", "dim3", "time", "extra_dim") assert "T" not in dir(ds) def test_dataset_ellipsis_transpose_different_ordered_vars(self): # https://github.com/pydata/xarray/issues/1081#issuecomment-544350457 ds = Dataset( dict( a=(("w", "x", "y", "z"), np.ones((2, 3, 4, 5))), b=(("x", "w", "y", "z"), np.zeros((3, 2, 4, 5))), ) ) result = ds.transpose(..., "z", "y") assert list(result["a"].dims) == list("wxzy") assert list(result["b"].dims) == list("xwzy") def test_dataset_retains_period_index_on_transpose(self): ds = create_test_data() ds["time"] = pd.period_range("2000-01-01", periods=20) transposed = ds.transpose() assert isinstance(transposed.time.to_index(), pd.PeriodIndex) def test_dataset_diff_n1_simple(self): ds = Dataset({"foo": ("x", [5, 5, 6, 6])}) actual = ds.diff("x") expected = Dataset({"foo": ("x", [0, 1, 0])}) assert_equal(expected, actual) def test_dataset_diff_n1_label(self): ds = Dataset({"foo": ("x", [5, 5, 6, 6])}, {"x": [0, 1, 2, 3]}) actual = ds.diff("x", label="lower") expected = Dataset({"foo": ("x", [0, 1, 0])}, {"x": [0, 1, 2]}) assert_equal(expected, actual) actual = ds.diff("x", label="upper") expected = Dataset({"foo": ("x", [0, 1, 0])}, {"x": [1, 2, 3]}) assert_equal(expected, actual) def test_dataset_diff_n1(self): ds = create_test_data(seed=1) actual = ds.diff("dim2") expected = {} expected["var1"] = DataArray( np.diff(ds["var1"].values, axis=1), {"dim2": ds["dim2"].values[1:]}, ["dim1", "dim2"], ) expected["var2"] = DataArray( np.diff(ds["var2"].values, axis=1), {"dim2": ds["dim2"].values[1:]}, ["dim1", "dim2"], ) expected["var3"] = ds["var3"] expected = Dataset(expected, coords={"time": ds["time"].values}) expected.coords["numbers"] = ("dim3", ds["numbers"].values) assert_equal(expected, actual) def test_dataset_diff_n2(self): ds = create_test_data(seed=1) actual = ds.diff("dim2", n=2) expected = {} expected["var1"] = DataArray( np.diff(ds["var1"].values, axis=1, n=2), {"dim2": ds["dim2"].values[2:]}, ["dim1", "dim2"], ) expected["var2"] = DataArray( np.diff(ds["var2"].values, axis=1, n=2), {"dim2": ds["dim2"].values[2:]}, ["dim1", "dim2"], ) expected["var3"] = ds["var3"] expected = Dataset(expected, coords={"time": ds["time"].values}) expected.coords["numbers"] = ("dim3", ds["numbers"].values) assert_equal(expected, actual) def test_dataset_diff_exception_n_neg(self): ds = create_test_data(seed=1) with raises_regex(ValueError, "must be non-negative"): ds.diff("dim2", n=-1) def test_dataset_diff_exception_label_str(self): ds = create_test_data(seed=1) with raises_regex(ValueError, "'label' argument has to"): ds.diff("dim2", label="raise_me") @pytest.mark.parametrize("fill_value", [dtypes.NA, 2, 2.0, {"foo": -10}]) def test_shift(self, fill_value): coords = {"bar": ("x", list("abc")), "x": [-4, 3, 2]} attrs = {"meta": "data"} ds = Dataset({"foo": ("x", [1, 2, 3])}, coords, attrs) actual = ds.shift(x=1, fill_value=fill_value) if fill_value == dtypes.NA: # if we supply the default, we expect the missing value for a # float array fill_value = np.nan elif isinstance(fill_value, dict): fill_value = fill_value.get("foo", np.nan) expected = Dataset({"foo": ("x", [fill_value, 1, 2])}, coords, attrs) assert_identical(expected, actual) with raises_regex(ValueError, "dimensions"): ds.shift(foo=123) def test_roll_coords(self): coords = {"bar": ("x", list("abc")), "x": [-4, 3, 2]} attrs = {"meta": "data"} ds = Dataset({"foo": ("x", [1, 2, 3])}, coords, attrs) actual = ds.roll(x=1, roll_coords=True) ex_coords = {"bar": ("x", list("cab")), "x": [2, -4, 3]} expected = Dataset({"foo": ("x", [3, 1, 2])}, ex_coords, attrs) assert_identical(expected, actual) with raises_regex(ValueError, "dimensions"): ds.roll(foo=123, roll_coords=True) def test_roll_no_coords(self): coords = {"bar": ("x", list("abc")), "x": [-4, 3, 2]} attrs = {"meta": "data"} ds = Dataset({"foo": ("x", [1, 2, 3])}, coords, attrs) actual = ds.roll(x=1, roll_coords=False) expected = Dataset({"foo": ("x", [3, 1, 2])}, coords, attrs) assert_identical(expected, actual) with raises_regex(ValueError, "dimensions"): ds.roll(abc=321, roll_coords=False) def test_roll_coords_none(self): coords = {"bar": ("x", list("abc")), "x": [-4, 3, 2]} attrs = {"meta": "data"} ds = Dataset({"foo": ("x", [1, 2, 3])}, coords, attrs) with pytest.warns(FutureWarning): actual = ds.roll(x=1, roll_coords=None) ex_coords = {"bar": ("x", list("cab")), "x": [2, -4, 3]} expected = Dataset({"foo": ("x", [3, 1, 2])}, ex_coords, attrs) assert_identical(expected, actual) def test_roll_multidim(self): # regression test for 2445 arr = xr.DataArray( [[1, 2, 3], [4, 5, 6]], coords={"x": range(3), "y": range(2)}, dims=("y", "x"), ) actual = arr.roll(x=1, roll_coords=True) expected = xr.DataArray( [[3, 1, 2], [6, 4, 5]], coords=[("y", [0, 1]), ("x", [2, 0, 1])] ) assert_identical(expected, actual) def test_real_and_imag(self): attrs = {"foo": "bar"} ds = Dataset({"x": ((), 1 + 2j, attrs)}, attrs=attrs) expected_re = Dataset({"x": ((), 1, attrs)}, attrs=attrs) assert_identical(ds.real, expected_re) expected_im = Dataset({"x": ((), 2, attrs)}, attrs=attrs) assert_identical(ds.imag, expected_im) def test_setattr_raises(self): ds = Dataset({}, coords={"scalar": 1}, attrs={"foo": "bar"}) with raises_regex(AttributeError, "cannot set attr"): ds.scalar = 2 with raises_regex(AttributeError, "cannot set attr"): ds.foo = 2 with raises_regex(AttributeError, "cannot set attr"): ds.other = 2 def test_filter_by_attrs(self): precip = dict(standard_name="convective_precipitation_flux") temp0 = dict(standard_name="air_potential_temperature", height="0 m") temp10 = dict(standard_name="air_potential_temperature", height="10 m") ds = Dataset( { "temperature_0": (["t"], [0], temp0), "temperature_10": (["t"], [0], temp10), "precipitation": (["t"], [0], precip), }, coords={"time": (["t"], [0], dict(axis="T", long_name="time_in_seconds"))}, ) # Test return empty Dataset. ds.filter_by_attrs(standard_name="invalid_standard_name") new_ds = ds.filter_by_attrs(standard_name="invalid_standard_name") assert not bool(new_ds.data_vars) # Test return one DataArray. new_ds = ds.filter_by_attrs(standard_name="convective_precipitation_flux") assert new_ds["precipitation"].standard_name == "convective_precipitation_flux" assert_equal(new_ds["precipitation"], ds["precipitation"]) # Test filter coordinates new_ds = ds.filter_by_attrs(long_name="time_in_seconds") assert new_ds["time"].long_name == "time_in_seconds" assert not bool(new_ds.data_vars) # Test return more than one DataArray. new_ds = ds.filter_by_attrs(standard_name="air_potential_temperature") assert len(new_ds.data_vars) == 2 for var in new_ds.data_vars: assert new_ds[var].standard_name == "air_potential_temperature" # Test callable. new_ds = ds.filter_by_attrs(height=lambda v: v is not None) assert len(new_ds.data_vars) == 2 for var in new_ds.data_vars: assert new_ds[var].standard_name == "air_potential_temperature" new_ds = ds.filter_by_attrs(height="10 m") assert len(new_ds.data_vars) == 1 for var in new_ds.data_vars: assert new_ds[var].height == "10 m" # Test return empty Dataset due to conflicting filters new_ds = ds.filter_by_attrs( standard_name="convective_precipitation_flux", height="0 m" ) assert not bool(new_ds.data_vars) # Test return one DataArray with two filter conditions new_ds = ds.filter_by_attrs( standard_name="air_potential_temperature", height="0 m" ) for var in new_ds.data_vars: assert new_ds[var].standard_name == "air_potential_temperature" assert new_ds[var].height == "0 m" assert new_ds[var].height != "10 m" # Test return empty Dataset due to conflicting callables new_ds = ds.filter_by_attrs( standard_name=lambda v: False, height=lambda v: True ) assert not bool(new_ds.data_vars) def test_binary_op_propagate_indexes(self): ds = Dataset( {"d1": DataArray([1, 2, 3], dims=["x"], coords={"x": [10, 20, 30]})} ) expected = ds.indexes["x"] actual = (ds * 2).indexes["x"] assert expected is actual def test_binary_op_join_setting(self): # arithmetic_join applies to data array coordinates missing_2 = xr.Dataset({"x": [0, 1]}) missing_0 = xr.Dataset({"x": [1, 2]}) with xr.set_options(arithmetic_join="outer"): actual = missing_2 + missing_0 expected = xr.Dataset({"x": [0, 1, 2]}) assert_equal(actual, expected) # arithmetic join also applies to data_vars ds1 = xr.Dataset({"foo": 1, "bar": 2}) ds2 = xr.Dataset({"bar": 2, "baz": 3}) expected = xr.Dataset({"bar": 4}) # default is inner joining actual = ds1 + ds2 assert_equal(actual, expected) with xr.set_options(arithmetic_join="outer"): expected = xr.Dataset({"foo": np.nan, "bar": 4, "baz": np.nan}) actual = ds1 + ds2 assert_equal(actual, expected) with xr.set_options(arithmetic_join="left"): expected = xr.Dataset({"foo": np.nan, "bar": 4}) actual = ds1 + ds2 assert_equal(actual, expected) with xr.set_options(arithmetic_join="right"): expected = xr.Dataset({"bar": 4, "baz": np.nan}) actual = ds1 + ds2 assert_equal(actual, expected) def test_full_like(self): # For more thorough tests, see test_variable.py # Note: testing data_vars with mismatched dtypes ds = Dataset( { "d1": DataArray([1, 2, 3], dims=["x"], coords={"x": [10, 20, 30]}), "d2": DataArray([1.1, 2.2, 3.3], dims=["y"]), }, attrs={"foo": "bar"}, ) actual = full_like(ds, 2) expected = ds.copy(deep=True) expected["d1"].values = [2, 2, 2] expected["d2"].values = [2.0, 2.0, 2.0] assert expected["d1"].dtype == int assert expected["d2"].dtype == float assert_identical(expected, actual) # override dtype actual = full_like(ds, fill_value=True, dtype=bool) expected = ds.copy(deep=True) expected["d1"].values = [True, True, True] expected["d2"].values = [True, True, True] assert expected["d1"].dtype == bool assert expected["d2"].dtype == bool assert_identical(expected, actual) # with multiple fill values actual = full_like(ds, {"d1": 1, "d2": 2.3}) expected = ds.assign(d1=("x", [1, 1, 1]), d2=("y", [2.3, 2.3, 2.3])) assert expected["d1"].dtype == int assert expected["d2"].dtype == float assert_identical(expected, actual) # override multiple dtypes actual = full_like(ds, fill_value={"d1": 1, "d2": 2.3}, dtype={"d1": bool}) expected = ds.assign(d1=("x", [True, True, True]), d2=("y", [2.3, 2.3, 2.3])) assert expected["d1"].dtype == bool assert expected["d2"].dtype == float assert_identical(expected, actual) def test_combine_first(self): dsx0 = DataArray([0, 0], [("x", ["a", "b"])]).to_dataset(name="dsx0") dsx1 = DataArray([1, 1], [("x", ["b", "c"])]).to_dataset(name="dsx1") actual = dsx0.combine_first(dsx1) expected = Dataset( {"dsx0": ("x", [0, 0, np.nan]), "dsx1": ("x", [np.nan, 1, 1])}, coords={"x": ["a", "b", "c"]}, ) assert_equal(actual, expected) assert_equal(actual, xr.merge([dsx0, dsx1])) # works just like xr.merge([self, other]) dsy2 = DataArray([2, 2, 2], [("x", ["b", "c", "d"])]).to_dataset(name="dsy2") actual = dsx0.combine_first(dsy2) expected = xr.merge([dsy2, dsx0]) assert_equal(actual, expected) def test_sortby(self): ds = Dataset( { "A": DataArray( [[1, 2], [3, 4], [5, 6]], [("x", ["c", "b", "a"]), ("y", [1, 0])] ), "B": DataArray([[5, 6], [7, 8], [9, 10]], dims=["x", "y"]), } ) sorted1d = Dataset( { "A": DataArray( [[5, 6], [3, 4], [1, 2]], [("x", ["a", "b", "c"]), ("y", [1, 0])] ), "B": DataArray([[9, 10], [7, 8], [5, 6]], dims=["x", "y"]), } ) sorted2d = Dataset( { "A": DataArray( [[6, 5], [4, 3], [2, 1]], [("x", ["a", "b", "c"]), ("y", [0, 1])] ), "B": DataArray([[10, 9], [8, 7], [6, 5]], dims=["x", "y"]), } ) expected = sorted1d dax = DataArray([100, 99, 98], [("x", ["c", "b", "a"])]) actual = ds.sortby(dax) assert_equal(actual, expected) # test descending order sort actual = ds.sortby(dax, ascending=False) assert_equal(actual, ds) # test alignment (fills in nan for 'c') dax_short = DataArray([98, 97], [("x", ["b", "a"])]) actual = ds.sortby(dax_short) assert_equal(actual, expected) # test 1-D lexsort # dax0 is sorted first to give indices of [1, 2, 0] # and then dax1 would be used to move index 2 ahead of 1 dax0 = DataArray([100, 95, 95], [("x", ["c", "b", "a"])]) dax1 = DataArray([0, 1, 0], [("x", ["c", "b", "a"])]) actual = ds.sortby([dax0, dax1]) # lexsort underneath gives [2, 1, 0] assert_equal(actual, expected) expected = sorted2d # test multi-dim sort by 1D dataarray values day = DataArray([90, 80], [("y", [1, 0])]) actual = ds.sortby([day, dax]) assert_equal(actual, expected) # test exception-raising with pytest.raises(KeyError) as excinfo: actual = ds.sortby("z") with pytest.raises(ValueError) as excinfo: actual = ds.sortby(ds["A"]) assert "DataArray is not 1-D" in str(excinfo.value) expected = sorted1d actual = ds.sortby("x") assert_equal(actual, expected) # test pandas.MultiIndex indices = (("b", 1), ("b", 0), ("a", 1), ("a", 0)) midx = pd.MultiIndex.from_tuples(indices, names=["one", "two"]) ds_midx = Dataset( { "A": DataArray( [[1, 2], [3, 4], [5, 6], [7, 8]], [("x", midx), ("y", [1, 0])] ), "B": DataArray([[5, 6], [7, 8], [9, 10], [11, 12]], dims=["x", "y"]), } ) actual = ds_midx.sortby("x") midx_reversed = pd.MultiIndex.from_tuples( tuple(reversed(indices)), names=["one", "two"] ) expected = Dataset( { "A": DataArray( [[7, 8], [5, 6], [3, 4], [1, 2]], [("x", midx_reversed), ("y", [1, 0])], ), "B": DataArray([[11, 12], [9, 10], [7, 8], [5, 6]], dims=["x", "y"]), } ) assert_equal(actual, expected) # multi-dim sort by coordinate objects expected = sorted2d actual = ds.sortby(["x", "y"]) assert_equal(actual, expected) # test descending order sort actual = ds.sortby(["x", "y"], ascending=False) assert_equal(actual, ds) def test_attribute_access(self): ds = create_test_data(seed=1) for key in ["var1", "var2", "var3", "time", "dim1", "dim2", "dim3", "numbers"]: assert_equal(ds[key], getattr(ds, key)) assert key in dir(ds) for key in ["dim3", "dim1", "numbers"]: assert_equal(ds["var3"][key], getattr(ds.var3, key)) assert key in dir(ds["var3"]) # attrs assert ds["var3"].attrs["foo"] == ds.var3.foo assert "foo" in dir(ds["var3"]) def test_ipython_key_completion(self): ds = create_test_data(seed=1) actual = ds._ipython_key_completions_() expected = ["var1", "var2", "var3", "time", "dim1", "dim2", "dim3", "numbers"] for item in actual: ds[item] # should not raise assert sorted(actual) == sorted(expected) # for dataarray actual = ds["var3"]._ipython_key_completions_() expected = ["dim3", "dim1", "numbers"] for item in actual: ds["var3"][item] # should not raise assert sorted(actual) == sorted(expected) # MultiIndex ds_midx = ds.stack(dim12=["dim1", "dim2"]) actual = ds_midx._ipython_key_completions_() expected = [ "var1", "var2", "var3", "time", "dim1", "dim2", "dim3", "numbers", "dim12", ] for item in actual: ds_midx[item] # should not raise assert sorted(actual) == sorted(expected) # coords actual = ds.coords._ipython_key_completions_() expected = ["time", "dim1", "dim2", "dim3", "numbers"] for item in actual: ds.coords[item] # should not raise assert sorted(actual) == sorted(expected) actual = ds["var3"].coords._ipython_key_completions_() expected = ["dim1", "dim3", "numbers"] for item in actual: ds["var3"].coords[item] # should not raise assert sorted(actual) == sorted(expected) # data_vars actual = ds.data_vars._ipython_key_completions_() expected = ["var1", "var2", "var3", "dim1"] for item in actual: ds.data_vars[item] # should not raise assert sorted(actual) == sorted(expected) def test_polyfit_output(self): ds = create_test_data(seed=1) out = ds.polyfit("dim2", 2, full=False) assert "var1_polyfit_coefficients" in out out = ds.polyfit("dim1", 2, full=True) assert "var1_polyfit_coefficients" in out assert "dim1_matrix_rank" in out out = ds.polyfit("time", 2) assert len(out.data_vars) == 0 def test_polyfit_warnings(self): ds = create_test_data(seed=1) with warnings.catch_warnings(record=True) as ws: ds.var1.polyfit("dim2", 10, full=False) assert len(ws) == 1 assert ws[0].category == np.RankWarning ds.var1.polyfit("dim2", 10, full=True) assert len(ws) == 1 def test_pad(self): ds = create_test_data(seed=1) padded = ds.pad(dim2=(1, 1), constant_values=42) assert padded["dim2"].shape == (11,) assert padded["var1"].shape == (8, 11) assert padded["var2"].shape == (8, 11) assert padded["var3"].shape == (10, 8) assert dict(padded.dims) == {"dim1": 8, "dim2": 11, "dim3": 10, "time": 20} np.testing.assert_equal(padded["var1"].isel(dim2=[0, -1]).data, 42) np.testing.assert_equal(padded["dim2"][[0, -1]].data, np.nan) def test_astype_attrs(self): data = create_test_data(seed=123) data.attrs["foo"] = "bar" assert data.attrs == data.astype(float).attrs assert data.var1.attrs == data.astype(float).var1.attrs assert not data.astype(float, keep_attrs=False).attrs assert not data.astype(float, keep_attrs=False).var1.attrs # Py.test tests @pytest.fixture(params=[None]) def data_set(request): return create_test_data(request.param) @pytest.mark.parametrize("test_elements", ([1, 2], np.array([1, 2]), DataArray([1, 2]))) def test_isin(test_elements): expected = Dataset( data_vars={ "var1": (("dim1",), [0, 1]), "var2": (("dim1",), [1, 1]), "var3": (("dim1",), [0, 1]), } ).astype("bool") result = Dataset( data_vars={ "var1": (("dim1",), [0, 1]), "var2": (("dim1",), [1, 2]), "var3": (("dim1",), [0, 1]), } ).isin(test_elements) assert_equal(result, expected) @pytest.mark.skipif(not has_dask, reason="requires dask") @pytest.mark.parametrize("test_elements", ([1, 2], np.array([1, 2]), DataArray([1, 2]))) def test_isin_dask(test_elements): expected = Dataset( data_vars={ "var1": (("dim1",), [0, 1]), "var2": (("dim1",), [1, 1]), "var3": (("dim1",), [0, 1]), } ).astype("bool") result = ( Dataset( data_vars={ "var1": (("dim1",), [0, 1]), "var2": (("dim1",), [1, 2]), "var3": (("dim1",), [0, 1]), } ) .chunk(1) .isin(test_elements) .compute() ) assert_equal(result, expected) def test_isin_dataset(): ds = Dataset({"x": [1, 2]}) with pytest.raises(TypeError): ds.isin(ds) @pytest.mark.parametrize( "unaligned_coords", ( {"x": [2, 1, 0]}, {"x": (["x"], np.asarray([2, 1, 0]))}, {"x": (["x"], np.asarray([1, 2, 0]))}, {"x": pd.Index([2, 1, 0])}, {"x": Variable(dims="x", data=[0, 2, 1])}, {"x": IndexVariable(dims="x", data=[0, 1, 2])}, {"y": 42}, {"y": ("x", [2, 1, 0])}, {"y": ("x", np.asarray([2, 1, 0]))}, {"y": (["x"], np.asarray([2, 1, 0]))}, ), ) @pytest.mark.parametrize("coords", ({"x": ("x", [0, 1, 2])}, {"x": [0, 1, 2]})) def test_dataset_constructor_aligns_to_explicit_coords(unaligned_coords, coords): a = xr.DataArray([1, 2, 3], dims=["x"], coords=unaligned_coords) expected = xr.Dataset(coords=coords) expected["a"] = a result = xr.Dataset({"a": a}, coords=coords) assert_equal(expected, result) def test_error_message_on_set_supplied(): with pytest.raises(TypeError, match="has invalid type "): xr.Dataset(dict(date=[1, 2, 3], sec={4})) @pytest.mark.parametrize("unaligned_coords", ({"y": ("b", np.asarray([2, 1, 0]))},)) def test_constructor_raises_with_invalid_coords(unaligned_coords): with pytest.raises(ValueError, match="not a subset of the DataArray dimensions"): xr.DataArray([1, 2, 3], dims=["x"], coords=unaligned_coords) def test_dir_expected_attrs(data_set): some_expected_attrs = {"pipe", "mean", "isnull", "var1", "dim2", "numbers"} result = dir(data_set) assert set(result) >= some_expected_attrs def test_dir_non_string(data_set): # add a numbered key to ensure this doesn't break dir data_set[5] = "foo" result = dir(data_set) assert 5 not in result # GH2172 sample_data = np.random.uniform(size=[2, 2000, 10000]) x = xr.Dataset({"sample_data": (sample_data.shape, sample_data)}) x2 = x["sample_data"] dir(x2) def test_dir_unicode(data_set): data_set["unicode"] = "uni" result = dir(data_set) assert "unicode" in result @pytest.fixture(params=[1]) def ds(request): if request.param == 1: return Dataset( { "z1": (["y", "x"], np.random.randn(2, 8)), "z2": (["time", "y"], np.random.randn(10, 2)), }, { "x": ("x", np.linspace(0, 1.0, 8)), "time": ("time", np.linspace(0, 1.0, 10)), "c": ("y", ["a", "b"]), "y": range(2), }, ) if request.param == 2: return Dataset( { "z1": (["time", "y"], np.random.randn(10, 2)), "z2": (["time"], np.random.randn(10)), "z3": (["x", "time"], np.random.randn(8, 10)), }, { "x": ("x", np.linspace(0, 1.0, 8)), "time": ("time", np.linspace(0, 1.0, 10)), "c": ("y", ["a", "b"]), "y": range(2), }, ) def test_coarsen_absent_dims_error(ds): with raises_regex(ValueError, "not found in Dataset."): ds.coarsen(foo=2) @pytest.mark.parametrize("dask", [True, False]) @pytest.mark.parametrize(("boundary", "side"), [("trim", "left"), ("pad", "right")]) def test_coarsen(ds, dask, boundary, side): if dask and has_dask: ds = ds.chunk({"x": 4}) actual = ds.coarsen(time=2, x=3, boundary=boundary, side=side).max() assert_equal( actual["z1"], ds["z1"].coarsen(x=3, boundary=boundary, side=side).max() ) # coordinate should be mean by default assert_equal( actual["time"], ds["time"].coarsen(time=2, boundary=boundary, side=side).mean() ) @pytest.mark.parametrize("dask", [True, False]) def test_coarsen_coords(ds, dask): if dask and has_dask: ds = ds.chunk({"x": 4}) # check if coord_func works actual = ds.coarsen(time=2, x=3, boundary="trim", coord_func={"time": "max"}).max() assert_equal(actual["z1"], ds["z1"].coarsen(x=3, boundary="trim").max()) assert_equal(actual["time"], ds["time"].coarsen(time=2, boundary="trim").max()) # raise if exact with pytest.raises(ValueError): ds.coarsen(x=3).mean() # should be no error ds.isel(x=slice(0, 3 * (len(ds["x"]) // 3))).coarsen(x=3).mean() # working test with pd.time da = xr.DataArray( np.linspace(0, 365, num=364), dims="time", coords={"time": pd.date_range("15/12/1999", periods=364)}, ) actual = da.coarsen(time=2).mean() @requires_cftime def test_coarsen_coords_cftime(): times = xr.cftime_range("2000", periods=6) da = xr.DataArray(range(6), [("time", times)]) actual = da.coarsen(time=3).mean() expected_times = xr.cftime_range("2000-01-02", freq="3D", periods=2) np.testing.assert_array_equal(actual.time, expected_times) def test_coarsen_keep_attrs(): _attrs = {"units": "test", "long_name": "testing"} var1 = np.linspace(10, 15, 100) var2 = np.linspace(5, 10, 100) coords = np.linspace(1, 10, 100) ds = Dataset( data_vars={"var1": ("coord", var1), "var2": ("coord", var2)}, coords={"coord": coords}, attrs=_attrs, ) ds2 = ds.copy(deep=True) # Test dropped attrs dat = ds.coarsen(coord=5).mean() assert dat.attrs == {} # Test kept attrs using dataset keyword dat = ds.coarsen(coord=5, keep_attrs=True).mean() assert dat.attrs == _attrs # Test kept attrs using global option with set_options(keep_attrs=True): dat = ds.coarsen(coord=5).mean() assert dat.attrs == _attrs # Test kept attrs in original object xr.testing.assert_identical(ds, ds2) @pytest.mark.parametrize( "funcname, argument", [ ("reduce", (np.mean,)), ("mean", ()), ("construct", ("window_dim",)), ("count", ()), ], ) def test_rolling_keep_attrs(funcname, argument): global_attrs = {"units": "test", "long_name": "testing"} da_attrs = {"da_attr": "test"} da_not_rolled_attrs = {"da_not_rolled_attr": "test"} data = np.linspace(10, 15, 100) coords = np.linspace(1, 10, 100) ds = Dataset( data_vars={"da": ("coord", data), "da_not_rolled": ("no_coord", data)}, coords={"coord": coords}, attrs=global_attrs, ) ds.da.attrs = da_attrs ds.da_not_rolled.attrs = da_not_rolled_attrs # attrs are now kept per default func = getattr(ds.rolling(dim={"coord": 5}), funcname) result = func(*argument) assert result.attrs == global_attrs assert result.da.attrs == da_attrs assert result.da_not_rolled.attrs == da_not_rolled_attrs assert result.da.name == "da" assert result.da_not_rolled.name == "da_not_rolled" # discard attrs func = getattr(ds.rolling(dim={"coord": 5}), funcname) result = func(*argument, keep_attrs=False) assert result.attrs == {} assert result.da.attrs == {} assert result.da_not_rolled.attrs == {} assert result.da.name == "da" assert result.da_not_rolled.name == "da_not_rolled" # test discard attrs using global option func = getattr(ds.rolling(dim={"coord": 5}), funcname) with set_options(keep_attrs=False): result = func(*argument) assert result.attrs == {} assert result.da.attrs == {} assert result.da_not_rolled.attrs == {} assert result.da.name == "da" assert result.da_not_rolled.name == "da_not_rolled" # keyword takes precedence over global option func = getattr(ds.rolling(dim={"coord": 5}), funcname) with set_options(keep_attrs=False): result = func(*argument, keep_attrs=True) assert result.attrs == global_attrs assert result.da.attrs == da_attrs assert result.da_not_rolled.attrs == da_not_rolled_attrs assert result.da.name == "da" assert result.da_not_rolled.name == "da_not_rolled" func = getattr(ds.rolling(dim={"coord": 5}), funcname) with set_options(keep_attrs=True): result = func(*argument, keep_attrs=False) assert result.attrs == {} assert result.da.attrs == {} assert result.da_not_rolled.attrs == {} assert result.da.name == "da" assert result.da_not_rolled.name == "da_not_rolled" def test_rolling_keep_attrs_deprecated(): global_attrs = {"units": "test", "long_name": "testing"} attrs_da = {"da_attr": "test"} data = np.linspace(10, 15, 100) coords = np.linspace(1, 10, 100) ds = Dataset( data_vars={"da": ("coord", data)}, coords={"coord": coords}, attrs=global_attrs, ) ds.da.attrs = attrs_da # deprecated option with pytest.warns( FutureWarning, match="Passing ``keep_attrs`` to ``rolling`` is deprecated" ): result = ds.rolling(dim={"coord": 5}, keep_attrs=False).construct("window_dim") assert result.attrs == {} assert result.da.attrs == {} # the keep_attrs in the reduction function takes precedence with pytest.warns( FutureWarning, match="Passing ``keep_attrs`` to ``rolling`` is deprecated" ): result = ds.rolling(dim={"coord": 5}, keep_attrs=True).construct( "window_dim", keep_attrs=False ) assert result.attrs == {} assert result.da.attrs == {} def test_rolling_properties(ds): # catching invalid args with pytest.raises(ValueError, match="window must be > 0"): ds.rolling(time=-2) with pytest.raises(ValueError, match="min_periods must be greater than zero"): ds.rolling(time=2, min_periods=0) with pytest.raises(KeyError, match="time2"): ds.rolling(time2=2) @pytest.mark.parametrize("name", ("sum", "mean", "std", "var", "min", "max", "median")) @pytest.mark.parametrize("center", (True, False, None)) @pytest.mark.parametrize("min_periods", (1, None)) @pytest.mark.parametrize("key", ("z1", "z2")) def test_rolling_wrapped_bottleneck(ds, name, center, min_periods, key): bn = pytest.importorskip("bottleneck", minversion="1.1") # Test all bottleneck functions rolling_obj = ds.rolling(time=7, min_periods=min_periods) func_name = f"move_{name}" actual = getattr(rolling_obj, name)() if key == "z1": # z1 does not depend on 'Time' axis. Stored as it is. expected = ds[key] elif key == "z2": expected = getattr(bn, func_name)( ds[key].values, window=7, axis=0, min_count=min_periods ) else: raise ValueError assert_array_equal(actual[key].values, expected) # Test center rolling_obj = ds.rolling(time=7, center=center) actual = getattr(rolling_obj, name)()["time"] assert_equal(actual, ds["time"]) @requires_numbagg def test_rolling_exp(ds): result = ds.rolling_exp(time=10, window_type="span").mean() assert isinstance(result, Dataset) @requires_numbagg def test_rolling_exp_keep_attrs(ds): attrs_global = {"attrs": "global"} attrs_z1 = {"attr": "z1"} ds.attrs = attrs_global ds.z1.attrs = attrs_z1 # attrs are kept per default result = ds.rolling_exp(time=10).mean() assert result.attrs == attrs_global assert result.z1.attrs == attrs_z1 # discard attrs result = ds.rolling_exp(time=10).mean(keep_attrs=False) assert result.attrs == {} assert result.z1.attrs == {} # test discard attrs using global option with set_options(keep_attrs=False): result = ds.rolling_exp(time=10).mean() assert result.attrs == {} assert result.z1.attrs == {} # keyword takes precedence over global option with set_options(keep_attrs=False): result = ds.rolling_exp(time=10).mean(keep_attrs=True) assert result.attrs == attrs_global assert result.z1.attrs == attrs_z1 with set_options(keep_attrs=True): result = ds.rolling_exp(time=10).mean(keep_attrs=False) assert result.attrs == {} assert result.z1.attrs == {} @pytest.mark.parametrize("center", (True, False)) @pytest.mark.parametrize("min_periods", (None, 1, 2, 3)) @pytest.mark.parametrize("window", (1, 2, 3, 4)) def test_rolling_pandas_compat(center, window, min_periods): df = pd.DataFrame( { "x": np.random.randn(20), "y": np.random.randn(20), "time": np.linspace(0, 1, 20), } ) ds = Dataset.from_dataframe(df) if min_periods is not None and window < min_periods: min_periods = window df_rolling = df.rolling(window, center=center, min_periods=min_periods).mean() ds_rolling = ds.rolling(index=window, center=center, min_periods=min_periods).mean() np.testing.assert_allclose(df_rolling["x"].values, ds_rolling["x"].values) np.testing.assert_allclose(df_rolling.index, ds_rolling["index"]) @pytest.mark.parametrize("center", (True, False)) @pytest.mark.parametrize("window", (1, 2, 3, 4)) def test_rolling_construct(center, window): df = pd.DataFrame( { "x": np.random.randn(20), "y": np.random.randn(20), "time": np.linspace(0, 1, 20), } ) ds = Dataset.from_dataframe(df) df_rolling = df.rolling(window, center=center, min_periods=1).mean() ds_rolling = ds.rolling(index=window, center=center) ds_rolling_mean = ds_rolling.construct("window").mean("window") np.testing.assert_allclose(df_rolling["x"].values, ds_rolling_mean["x"].values) np.testing.assert_allclose(df_rolling.index, ds_rolling_mean["index"]) # with stride ds_rolling_mean = ds_rolling.construct("window", stride=2).mean("window") np.testing.assert_allclose(df_rolling["x"][::2].values, ds_rolling_mean["x"].values) np.testing.assert_allclose(df_rolling.index[::2], ds_rolling_mean["index"]) # with fill_value ds_rolling_mean = ds_rolling.construct("window", stride=2, fill_value=0.0).mean( "window" ) assert (ds_rolling_mean.isnull().sum() == 0).to_array(dim="vars").all() assert (ds_rolling_mean["x"] == 0.0).sum() >= 0 @pytest.mark.slow @pytest.mark.parametrize("ds", (1, 2), indirect=True) @pytest.mark.parametrize("center", (True, False)) @pytest.mark.parametrize("min_periods", (None, 1, 2, 3)) @pytest.mark.parametrize("window", (1, 2, 3, 4)) @pytest.mark.parametrize("name", ("sum", "mean", "std", "var", "min", "max", "median")) def test_rolling_reduce(ds, center, min_periods, window, name): if min_periods is not None and window < min_periods: min_periods = window if name == "std" and window == 1: pytest.skip("std with window == 1 is unstable in bottleneck") rolling_obj = ds.rolling(time=window, center=center, min_periods=min_periods) # add nan prefix to numpy methods to get similar behavior as bottleneck actual = rolling_obj.reduce(getattr(np, "nan%s" % name)) expected = getattr(rolling_obj, name)() assert_allclose(actual, expected) assert ds.dims == actual.dims # make sure the order of data_var are not changed. assert list(ds.data_vars.keys()) == list(actual.data_vars.keys()) # Make sure the dimension order is restored for key, src_var in ds.data_vars.items(): assert src_var.dims == actual[key].dims @pytest.mark.parametrize("ds", (2,), indirect=True) @pytest.mark.parametrize("center", (True, False)) @pytest.mark.parametrize("min_periods", (None, 1)) @pytest.mark.parametrize("name", ("sum", "max")) @pytest.mark.parametrize("dask", (True, False)) def test_ndrolling_reduce(ds, center, min_periods, name, dask): if dask and has_dask: ds = ds.chunk({"x": 4}) rolling_obj = ds.rolling(time=4, x=3, center=center, min_periods=min_periods) actual = getattr(rolling_obj, name)() expected = getattr( getattr( ds.rolling(time=4, center=center, min_periods=min_periods), name )().rolling(x=3, center=center, min_periods=min_periods), name, )() assert_allclose(actual, expected) assert actual.dims == expected.dims # Do it in the opposite order expected = getattr( getattr( ds.rolling(x=3, center=center, min_periods=min_periods), name )().rolling(time=4, center=center, min_periods=min_periods), name, )() assert_allclose(actual, expected) assert actual.dims == expected.dims @pytest.mark.parametrize("center", (True, False, (True, False))) @pytest.mark.parametrize("fill_value", (np.nan, 0.0)) @pytest.mark.parametrize("dask", (True, False)) def test_ndrolling_construct(center, fill_value, dask): da = DataArray( np.arange(5 * 6 * 7).reshape(5, 6, 7).astype(float), dims=["x", "y", "z"], coords={"x": ["a", "b", "c", "d", "e"], "y": np.arange(6)}, ) ds = xr.Dataset({"da": da}) if dask and has_dask: ds = ds.chunk({"x": 4}) actual = ds.rolling(x=3, z=2, center=center).construct( x="x1", z="z1", fill_value=fill_value ) if not isinstance(center, tuple): center = (center, center) expected = ( ds.rolling(x=3, center=center[0]) .construct(x="x1", fill_value=fill_value) .rolling(z=2, center=center[1]) .construct(z="z1", fill_value=fill_value) ) assert_allclose(actual, expected) def test_raise_no_warning_for_nan_in_binary_ops(): with pytest.warns(None) as record: Dataset(data_vars={"x": ("y", [1, 2, np.NaN])}) > 0 assert len(record) == 0 @pytest.mark.filterwarnings("error") @pytest.mark.parametrize("ds", (2,), indirect=True) def test_raise_no_warning_assert_close(ds): assert_allclose(ds, ds) @pytest.mark.xfail(reason="See https://github.com/pydata/xarray/pull/4369 or docstring") @pytest.mark.filterwarnings("error") @pytest.mark.parametrize("ds", (2,), indirect=True) @pytest.mark.parametrize("name", ("mean", "max")) def test_raise_no_warning_dask_rolling_assert_close(ds, name): """ This is a puzzle — I can't easily find the source of the warning. It requires `assert_allclose` to be run, for the `ds` param to be 2, and is different for `mean` and `max`. `sum` raises no warning. """ ds = ds.chunk({"x": 4}) rolling_obj = ds.rolling(time=4, x=3) actual = getattr(rolling_obj, name)() expected = getattr(getattr(ds.rolling(time=4), name)().rolling(x=3), name)() assert_allclose(actual, expected) @pytest.mark.parametrize("dask", [True, False]) @pytest.mark.parametrize("edge_order", [1, 2]) def test_differentiate(dask, edge_order): rs = np.random.RandomState(42) coord = [0.2, 0.35, 0.4, 0.6, 0.7, 0.75, 0.76, 0.8] da = xr.DataArray( rs.randn(8, 6), dims=["x", "y"], coords={"x": coord, "z": 3, "x2d": (("x", "y"), rs.randn(8, 6))}, ) if dask and has_dask: da = da.chunk({"x": 4}) ds = xr.Dataset({"var": da}) # along x actual = da.differentiate("x", edge_order) expected_x = xr.DataArray( np.gradient(da, da["x"], axis=0, edge_order=edge_order), dims=da.dims, coords=da.coords, ) assert_equal(expected_x, actual) assert_equal( ds["var"].differentiate("x", edge_order=edge_order), ds.differentiate("x", edge_order=edge_order)["var"], ) # coordinate should not change assert_equal(da["x"], actual["x"]) # along y actual = da.differentiate("y", edge_order) expected_y = xr.DataArray( np.gradient(da, da["y"], axis=1, edge_order=edge_order), dims=da.dims, coords=da.coords, ) assert_equal(expected_y, actual) assert_equal(actual, ds.differentiate("y", edge_order=edge_order)["var"]) assert_equal( ds["var"].differentiate("y", edge_order=edge_order), ds.differentiate("y", edge_order=edge_order)["var"], ) with pytest.raises(ValueError): da.differentiate("x2d") @pytest.mark.parametrize("dask", [True, False]) def test_differentiate_datetime(dask): rs = np.random.RandomState(42) coord = np.array( [ "2004-07-13", "2006-01-13", "2010-08-13", "2010-09-13", "2010-10-11", "2010-12-13", "2011-02-13", "2012-08-13", ], dtype="datetime64", ) da = xr.DataArray( rs.randn(8, 6), dims=["x", "y"], coords={"x": coord, "z": 3, "x2d": (("x", "y"), rs.randn(8, 6))}, ) if dask and has_dask: da = da.chunk({"x": 4}) # along x actual = da.differentiate("x", edge_order=1, datetime_unit="D") expected_x = xr.DataArray( np.gradient( da, da["x"].variable._to_numeric(datetime_unit="D"), axis=0, edge_order=1 ), dims=da.dims, coords=da.coords, ) assert_equal(expected_x, actual) actual2 = da.differentiate("x", edge_order=1, datetime_unit="h") assert np.allclose(actual, actual2 * 24) # for datetime variable actual = da["x"].differentiate("x", edge_order=1, datetime_unit="D") assert np.allclose(actual, 1.0) # with different date unit da = xr.DataArray(coord.astype("datetime64[ms]"), dims=["x"], coords={"x": coord}) actual = da.differentiate("x", edge_order=1) assert np.allclose(actual, 1.0) @pytest.mark.skipif(not has_cftime, reason="Test requires cftime.") @pytest.mark.parametrize("dask", [True, False]) def test_differentiate_cftime(dask): rs = np.random.RandomState(42) coord = xr.cftime_range("2000", periods=8, freq="2M") da = xr.DataArray( rs.randn(8, 6), coords={"time": coord, "z": 3, "t2d": (("time", "y"), rs.randn(8, 6))}, dims=["time", "y"], ) if dask and has_dask: da = da.chunk({"time": 4}) actual = da.differentiate("time", edge_order=1, datetime_unit="D") expected_data = np.gradient( da, da["time"].variable._to_numeric(datetime_unit="D"), axis=0, edge_order=1 ) expected = xr.DataArray(expected_data, coords=da.coords, dims=da.dims) assert_equal(expected, actual) actual2 = da.differentiate("time", edge_order=1, datetime_unit="h") assert_allclose(actual, actual2 * 24) # Test the differentiation of datetimes themselves actual = da["time"].differentiate("time", edge_order=1, datetime_unit="D") assert_allclose(actual, xr.ones_like(da["time"]).astype(float)) @pytest.mark.parametrize("dask", [True, False]) def test_integrate(dask): rs = np.random.RandomState(42) coord = [0.2, 0.35, 0.4, 0.6, 0.7, 0.75, 0.76, 0.8] da = xr.DataArray( rs.randn(8, 6), dims=["x", "y"], coords={ "x": coord, "x2": (("x",), rs.randn(8)), "z": 3, "x2d": (("x", "y"), rs.randn(8, 6)), }, ) if dask and has_dask: da = da.chunk({"x": 4}) ds = xr.Dataset({"var": da}) # along x actual = da.integrate("x") # coordinate that contains x should be dropped. expected_x = xr.DataArray( np.trapz(da.compute(), da["x"], axis=0), dims=["y"], coords={k: v for k, v in da.coords.items() if "x" not in v.dims}, ) assert_allclose(expected_x, actual.compute()) assert_equal(ds["var"].integrate("x"), ds.integrate("x")["var"]) # make sure result is also a dask array (if the source is dask array) assert isinstance(actual.data, type(da.data)) # along y actual = da.integrate("y") expected_y = xr.DataArray( np.trapz(da, da["y"], axis=1), dims=["x"], coords={k: v for k, v in da.coords.items() if "y" not in v.dims}, ) assert_allclose(expected_y, actual.compute()) assert_equal(actual, ds.integrate("y")["var"]) assert_equal(ds["var"].integrate("y"), ds.integrate("y")["var"]) # along x and y actual = da.integrate(("y", "x")) assert actual.ndim == 0 with pytest.raises(ValueError): da.integrate("x2d") @pytest.mark.parametrize("dask", [True, False]) @pytest.mark.parametrize("which_datetime", ["np", "cftime"]) def test_trapz_datetime(dask, which_datetime): rs = np.random.RandomState(42) if which_datetime == "np": coord = np.array( [ "2004-07-13", "2006-01-13", "2010-08-13", "2010-09-13", "2010-10-11", "2010-12-13", "2011-02-13", "2012-08-13", ], dtype="datetime64", ) else: if not has_cftime: pytest.skip("Test requires cftime.") coord = xr.cftime_range("2000", periods=8, freq="2D") da = xr.DataArray( rs.randn(8, 6), coords={"time": coord, "z": 3, "t2d": (("time", "y"), rs.randn(8, 6))}, dims=["time", "y"], ) if dask and has_dask: da = da.chunk({"time": 4}) actual = da.integrate("time", datetime_unit="D") expected_data = np.trapz( da.data, duck_array_ops.datetime_to_numeric(da["time"].data, datetime_unit="D"), axis=0, ) expected = xr.DataArray( expected_data, dims=["y"], coords={k: v for k, v in da.coords.items() if "time" not in v.dims}, ) assert_allclose(expected, actual.compute()) # make sure result is also a dask array (if the source is dask array) assert isinstance(actual.data, type(da.data)) actual2 = da.integrate("time", datetime_unit="h") assert_allclose(actual, actual2 / 24.0) def test_no_dict(): d = Dataset() with pytest.raises(AttributeError): d.__dict__ def test_subclass_slots(): """Test that Dataset subclasses must explicitly define ``__slots__``. .. note:: As of 0.13.0, this is actually mitigated into a FutureWarning for any class defined outside of the xarray package. """ with pytest.raises(AttributeError) as e: class MyDS(Dataset): pass assert str(e.value) == "MyDS must explicitly define __slots__" def test_weakref(): """Classes with __slots__ are incompatible with the weakref module unless they explicitly state __weakref__ among their slots """ from weakref import ref ds = Dataset() r = ref(ds) assert r() is ds def test_deepcopy_obj_array(): x0 = Dataset(dict(foo=DataArray(np.array([object()])))) x1 = deepcopy(x0) assert x0["foo"].values[0] is not x1["foo"].values[0]