from __future__ import annotations import asyncio import contextlib import gzip import itertools import math import os.path import pickle import platform import re import shutil import sys import tempfile import uuid import warnings from collections import ChainMap from collections.abc import Generator, Iterator, Mapping from contextlib import ExitStack from importlib import import_module from io import BytesIO from pathlib import Path from typing import TYPE_CHECKING, Any, Final, Literal, cast from unittest.mock import patch import numpy as np import pandas as pd import pytest from packaging.version import Version from pandas.errors import OutOfBoundsDatetime import xarray as xr import xarray.testing as xrt from xarray import ( DataArray, Dataset, backends, load_dataarray, load_dataset, open_dataarray, open_dataset, open_mfdataset, save_mfdataset, ) from xarray.backends.common import robust_getitem from xarray.backends.h5netcdf_ import H5netcdfBackendEntrypoint from xarray.backends.netcdf3 import _nc3_dtype_coercions from xarray.backends.netCDF4_ import ( NetCDF4BackendEntrypoint, _extract_nc4_variable_encoding, ) from xarray.backends.pydap_ import PydapDataStore from xarray.backends.scipy_ import ScipyBackendEntrypoint from xarray.backends.zarr import ZarrStore from xarray.coders import CFDatetimeCoder, CFTimedeltaCoder from xarray.coding.cftime_offsets import date_range from xarray.coding.strings import check_vlen_dtype, create_vlen_dtype from xarray.coding.variables import SerializationWarning from xarray.conventions import encode_dataset_coordinates from xarray.core import indexing from xarray.core.indexes import PandasIndex from xarray.core.options import set_options from xarray.core.types import PDDatetimeUnitOptions from xarray.core.utils import module_available from xarray.namedarray.pycompat import array_type from xarray.structure.alignment import AlignmentError from xarray.tests import ( assert_allclose, assert_array_equal, assert_equal, assert_identical, assert_no_warnings, has_dask, has_h5netcdf_1_4_0_or_above, has_netCDF4, has_numpy_2, has_scipy, has_zarr, has_zarr_v3, has_zarr_v3_async_oindex, has_zarr_v3_dtypes, mock, network, parametrize_zarr_format, requires_cftime, requires_dask, requires_fsspec, requires_h5netcdf, requires_h5netcdf_1_4_0_or_above, requires_h5netcdf_ros3, requires_iris, requires_netcdf, requires_netCDF4, requires_netCDF4_1_6_2_or_above, requires_netCDF4_1_7_0_or_above, requires_pydap, requires_scipy, requires_scipy_or_netCDF4, requires_zarr, requires_zarr_v3, ) from xarray.tests.test_coding_times import ( _ALL_CALENDARS, _NON_STANDARD_CALENDARS, _STANDARD_CALENDARS, ) from xarray.tests.test_dataset import ( create_append_string_length_mismatch_test_data, create_append_test_data, create_test_data, ) with contextlib.suppress(ImportError): import netCDF4 as nc4 try: import dask import dask.array as da except ImportError: pass if has_zarr: import zarr import zarr.codecs if has_zarr_v3: from zarr.storage import MemoryStore as KVStore from zarr.storage import WrapperStore ZARR_FORMATS = [2, 3] else: ZARR_FORMATS = [2] try: from zarr import ( # type: ignore[attr-defined,no-redef,unused-ignore] KVStoreV3 as KVStore, ) except ImportError: KVStore = None # type: ignore[assignment,misc,unused-ignore] WrapperStore = object # type: ignore[assignment,misc,unused-ignore] else: KVStore = None # type: ignore[assignment,misc,unused-ignore] WrapperStore = object # type: ignore[assignment,misc,unused-ignore] ZARR_FORMATS = [] @pytest.fixture(scope="module", params=ZARR_FORMATS) def default_zarr_format(request) -> Generator[None, None]: if has_zarr_v3: with zarr.config.set(default_zarr_format=request.param): yield else: yield def skip_if_zarr_format_3(reason: str): if has_zarr_v3 and zarr.config["default_zarr_format"] == 3: pytest.skip(reason=f"Unsupported with zarr_format=3: {reason}") def skip_if_zarr_format_2(reason: str): if not has_zarr_v3 or (zarr.config["default_zarr_format"] == 2): pytest.skip(reason=f"Unsupported with zarr_format=2: {reason}") ON_WINDOWS = sys.platform == "win32" default_value = object() dask_array_type = array_type("dask") if TYPE_CHECKING: from xarray.backends.api import T_NetcdfEngine, T_NetcdfTypes def open_example_dataset(name, *args, **kwargs) -> Dataset: return open_dataset( os.path.join(os.path.dirname(__file__), "data", name), *args, **kwargs ) def open_example_mfdataset(names, *args, **kwargs) -> Dataset: return open_mfdataset( [os.path.join(os.path.dirname(__file__), "data", name) for name in names], *args, **kwargs, ) def create_masked_and_scaled_data(dtype: np.dtype) -> Dataset: x = np.array([np.nan, np.nan, 10, 10.1, 10.2], dtype=dtype) encoding = { "_FillValue": -1, "add_offset": dtype.type(10), "scale_factor": dtype.type(0.1), "dtype": "i2", } return Dataset({"x": ("t", x, {}, encoding)}) def create_encoded_masked_and_scaled_data(dtype: np.dtype) -> Dataset: attributes = { "_FillValue": -1, "add_offset": dtype.type(10), "scale_factor": dtype.type(0.1), } return Dataset( {"x": ("t", np.array([-1, -1, 0, 1, 2], dtype=np.int16), attributes)} ) def create_unsigned_masked_scaled_data(dtype: np.dtype) -> Dataset: encoding = { "_FillValue": -1, "_Unsigned": "true", "dtype": "i1", "add_offset": dtype.type(10), "scale_factor": dtype.type(0.1), } x = np.array([10.0, 10.1, 22.7, 22.8, np.nan], dtype=dtype) return Dataset({"x": ("t", x, {}, encoding)}) def create_encoded_unsigned_masked_scaled_data(dtype: np.dtype) -> Dataset: # These are values as written to the file: the _FillValue will # be represented in the signed form. attributes = { "_FillValue": -1, "_Unsigned": "true", "add_offset": dtype.type(10), "scale_factor": dtype.type(0.1), } # Create unsigned data corresponding to [0, 1, 127, 128, 255] unsigned sb = np.asarray([0, 1, 127, -128, -1], dtype="i1") return Dataset({"x": ("t", sb, attributes)}) def create_bad_unsigned_masked_scaled_data(dtype: np.dtype) -> Dataset: encoding = { "_FillValue": 255, "_Unsigned": True, "dtype": "i1", "add_offset": dtype.type(10), "scale_factor": dtype.type(0.1), } x = np.array([10.0, 10.1, 22.7, 22.8, np.nan], dtype=dtype) return Dataset({"x": ("t", x, {}, encoding)}) def create_bad_encoded_unsigned_masked_scaled_data(dtype: np.dtype) -> Dataset: # These are values as written to the file: the _FillValue will # be represented in the signed form. attributes = { "_FillValue": -1, "_Unsigned": True, "add_offset": dtype.type(10), "scale_factor": dtype.type(0.1), } # Create signed data corresponding to [0, 1, 127, 128, 255] unsigned sb = np.asarray([0, 1, 127, -128, -1], dtype="i1") return Dataset({"x": ("t", sb, attributes)}) def create_signed_masked_scaled_data(dtype: np.dtype) -> Dataset: encoding = { "_FillValue": -127, "_Unsigned": "false", "dtype": "i1", "add_offset": dtype.type(10), "scale_factor": dtype.type(0.1), } x = np.array([-1.0, 10.1, 22.7, np.nan], dtype=dtype) return Dataset({"x": ("t", x, {}, encoding)}) def create_encoded_signed_masked_scaled_data(dtype: np.dtype) -> Dataset: # These are values as written to the file: the _FillValue will # be represented in the signed form. attributes = { "_FillValue": -127, "_Unsigned": "false", "add_offset": dtype.type(10), "scale_factor": dtype.type(0.1), } # Create signed data corresponding to [0, 1, 127, 128, 255] unsigned sb = np.asarray([-110, 1, 127, -127], dtype="i1") return Dataset({"x": ("t", sb, attributes)}) def create_unsigned_false_masked_scaled_data(dtype: np.dtype) -> Dataset: encoding = { "_FillValue": 255, "_Unsigned": "false", "dtype": "u1", "add_offset": dtype.type(10), "scale_factor": dtype.type(0.1), } x = np.array([-1.0, 10.1, 22.7, np.nan], dtype=dtype) return Dataset({"x": ("t", x, {}, encoding)}) def create_encoded_unsigned_false_masked_scaled_data(dtype: np.dtype) -> Dataset: # These are values as written to the file: the _FillValue will # be represented in the unsigned form. attributes = { "_FillValue": 255, "_Unsigned": "false", "add_offset": dtype.type(10), "scale_factor": dtype.type(0.1), } # Create unsigned data corresponding to [-110, 1, 127, 255] signed sb = np.asarray([146, 1, 127, 255], dtype="u1") return Dataset({"x": ("t", sb, attributes)}) def create_boolean_data() -> Dataset: attributes = {"units": "-"} return Dataset( { "x": ( ("t", "x"), [[False, True, False, True], [True, False, False, True]], attributes, ) } ) class TestCommon: def test_robust_getitem(self) -> None: class UnreliableArrayFailure(Exception): pass class UnreliableArray: def __init__(self, array, failures=1): self.array = array self.failures = failures def __getitem__(self, key): if self.failures > 0: self.failures -= 1 raise UnreliableArrayFailure return self.array[key] array = UnreliableArray([0]) with pytest.raises(UnreliableArrayFailure): array[0] assert array[0] == 0 actual = robust_getitem(array, 0, catch=UnreliableArrayFailure, initial_delay=0) assert actual == 0 class NetCDF3Only: netcdf3_formats: tuple[T_NetcdfTypes, ...] = ("NETCDF3_CLASSIC", "NETCDF3_64BIT") @pytest.mark.asyncio @pytest.mark.skip(reason="NetCDF backends don't support async loading") async def test_load_async(self) -> None: pass @requires_scipy def test_dtype_coercion_error(self) -> None: """Failing dtype coercion should lead to an error""" for dtype, format in itertools.product( _nc3_dtype_coercions, self.netcdf3_formats ): if dtype == "bool": # coerced upcast (bool to int8) ==> can never fail continue # Using the largest representable value, create some data that will # no longer compare equal after the coerced downcast maxval = np.iinfo(dtype).max x = np.array([0, 1, 2, maxval], dtype=dtype) ds = Dataset({"x": ("t", x, {})}) with create_tmp_file(allow_cleanup_failure=False) as path: with pytest.raises(ValueError, match="could not safely cast"): ds.to_netcdf(path, format=format) class DatasetIOBase: engine: T_NetcdfEngine | None = None file_format: T_NetcdfTypes | None = None def create_store(self): raise NotImplementedError() @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} with create_tmp_file(allow_cleanup_failure=allow_cleanup_failure) as path: self.save(data, path, **save_kwargs) with self.open(path, **open_kwargs) as ds: yield ds @contextlib.contextmanager def roundtrip_append( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} with create_tmp_file(allow_cleanup_failure=allow_cleanup_failure) as path: for i, key in enumerate(data.variables): mode = "a" if i > 0 else "w" self.save(data[[key]], path, mode=mode, **save_kwargs) with self.open(path, **open_kwargs) as ds: yield ds # The save/open methods may be overwritten below def save(self, dataset, path, **kwargs): return dataset.to_netcdf( path, engine=self.engine, format=self.file_format, **kwargs ) @contextlib.contextmanager def open(self, path, **kwargs): with open_dataset(path, engine=self.engine, **kwargs) as ds: yield ds def test_zero_dimensional_variable(self) -> None: expected = create_test_data() expected["float_var"] = ([], 1.0e9, {"units": "units of awesome"}) expected["bytes_var"] = ([], b"foobar") expected["string_var"] = ([], "foobar") with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_write_store(self) -> None: expected = create_test_data() with self.create_store() as store: expected.dump_to_store(store) # we need to cf decode the store because it has time and # non-dimension coordinates with xr.decode_cf(store) as actual: assert_allclose(expected, actual) def check_dtypes_roundtripped(self, expected, actual): for k in expected.variables: expected_dtype = expected.variables[k].dtype # For NetCDF3, the backend should perform dtype coercion if ( isinstance(self, NetCDF3Only) and str(expected_dtype) in _nc3_dtype_coercions ): expected_dtype = np.dtype(_nc3_dtype_coercions[str(expected_dtype)]) actual_dtype = actual.variables[k].dtype # TODO: check expected behavior for string dtypes more carefully string_kinds = {"O", "S", "U"} assert expected_dtype == actual_dtype or ( expected_dtype.kind in string_kinds and actual_dtype.kind in string_kinds ) def test_roundtrip_test_data(self) -> None: expected = create_test_data() with self.roundtrip(expected) as actual: self.check_dtypes_roundtripped(expected, actual) assert_identical(expected, actual) def test_load(self) -> None: # Note: please keep this in sync with test_load_async below as much as possible! expected = create_test_data() @contextlib.contextmanager def assert_loads(vars=None): if vars is None: vars = expected with self.roundtrip(expected) as actual: for k, v in actual.variables.items(): # IndexVariables are eagerly loaded into memory assert v._in_memory == (k in actual.dims) yield actual for k, v in actual.variables.items(): if k in vars: assert v._in_memory assert_identical(expected, actual) with pytest.raises(AssertionError): # make sure the contextmanager works! with assert_loads() as ds: pass with assert_loads() as ds: ds.load() with assert_loads(["var1", "dim1", "dim2"]) as ds: ds["var1"].load() # verify we can read data even after closing the file with self.roundtrip(expected) as ds: actual = ds.load() assert_identical(expected, actual) @pytest.mark.asyncio async def test_load_async(self) -> None: # Note: please keep this in sync with test_load above as much as possible! # Copied from `test_load` on the base test class, but won't work for netcdf expected = create_test_data() @contextlib.contextmanager def assert_loads(vars=None): if vars is None: vars = expected with self.roundtrip(expected) as actual: for k, v in actual.variables.items(): # IndexVariables are eagerly loaded into memory assert v._in_memory == (k in actual.dims) yield actual for k, v in actual.variables.items(): if k in vars: assert v._in_memory assert_identical(expected, actual) with pytest.raises(AssertionError): # make sure the contextmanager works! with assert_loads() as ds: pass with assert_loads() as ds: await ds.load_async() with assert_loads(["var1", "dim1", "dim2"]) as ds: await ds["var1"].load_async() # verify we can read data even after closing the file with self.roundtrip(expected) as ds: actual = await ds.load_async() assert_identical(expected, actual) def test_dataset_compute(self) -> None: expected = create_test_data() with self.roundtrip(expected) as actual: # Test Dataset.compute() for k, v in actual.variables.items(): # IndexVariables are eagerly cached assert v._in_memory == (k in actual.dims) computed = actual.compute() for k, v in actual.variables.items(): assert v._in_memory == (k in actual.dims) for v in computed.variables.values(): assert v._in_memory assert_identical(expected, actual) assert_identical(expected, computed) def test_pickle(self) -> None: expected = Dataset({"foo": ("x", [42])}) with self.roundtrip(expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped: with roundtripped: # Windows doesn't like reopening an already open file raw_pickle = pickle.dumps(roundtripped) with pickle.loads(raw_pickle) as unpickled_ds: assert_identical(expected, unpickled_ds) @pytest.mark.filterwarnings("ignore:deallocating CachingFileManager") def test_pickle_dataarray(self) -> None: expected = Dataset({"foo": ("x", [42])}) with self.roundtrip(expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped: with roundtripped: raw_pickle = pickle.dumps(roundtripped["foo"]) # TODO: figure out how to explicitly close the file for the # unpickled DataArray? unpickled = pickle.loads(raw_pickle) assert_identical(expected["foo"], unpickled) def test_dataset_caching(self) -> None: expected = Dataset({"foo": ("x", [5, 6, 7])}) with self.roundtrip(expected) as actual: assert isinstance(actual.foo.variable._data, indexing.MemoryCachedArray) assert not actual.foo.variable._in_memory _ = actual.foo.values # cache assert actual.foo.variable._in_memory with self.roundtrip(expected, open_kwargs={"cache": False}) as actual: assert isinstance(actual.foo.variable._data, indexing.CopyOnWriteArray) assert not actual.foo.variable._in_memory _ = actual.foo.values # no caching assert not actual.foo.variable._in_memory @pytest.mark.filterwarnings("ignore:deallocating CachingFileManager") def test_roundtrip_None_variable(self) -> None: expected = Dataset({None: (("x", "y"), [[0, 1], [2, 3]])}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_object_dtype(self) -> None: floats = np.array([0.0, 0.0, 1.0, 2.0, 3.0], dtype=object) floats_nans = np.array([np.nan, np.nan, 1.0, 2.0, 3.0], dtype=object) bytes_ = np.array([b"ab", b"cdef", b"g"], dtype=object) bytes_nans = np.array([b"ab", b"cdef", np.nan], dtype=object) strings = np.array(["ab", "cdef", "g"], dtype=object) strings_nans = np.array(["ab", "cdef", np.nan], dtype=object) all_nans = np.array([np.nan, np.nan], dtype=object) original = Dataset( { "floats": ("a", floats), "floats_nans": ("a", floats_nans), "bytes": ("b", bytes_), "bytes_nans": ("b", bytes_nans), "strings": ("b", strings), "strings_nans": ("b", strings_nans), "all_nans": ("c", all_nans), "nan": ([], np.nan), } ) expected = original.copy(deep=True) with self.roundtrip(original) as actual: try: assert_identical(expected, actual) except AssertionError: # Most stores use '' for nans in strings, but some don't. # First try the ideal case (where the store returns exactly) # the original Dataset), then try a more realistic case. # This currently includes all netCDF files when encoding is not # explicitly set. # https://github.com/pydata/xarray/issues/1647 # Also Zarr expected["bytes_nans"][-1] = b"" expected["strings_nans"][-1] = "" assert_identical(expected, actual) def test_roundtrip_string_data(self) -> None: expected = Dataset({"x": ("t", ["ab", "cdef"])}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_string_encoded_characters(self) -> None: expected = Dataset({"x": ("t", ["ab", "cdef"])}) expected["x"].encoding["dtype"] = "S1" with self.roundtrip(expected) as actual: assert_identical(expected, actual) assert actual["x"].encoding["_Encoding"] == "utf-8" expected["x"].encoding["_Encoding"] = "ascii" with self.roundtrip(expected) as actual: assert_identical(expected, actual) assert actual["x"].encoding["_Encoding"] == "ascii" def test_roundtrip_numpy_datetime_data(self) -> None: times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"], unit="ns") expected = Dataset({"t": ("t", times), "t0": times[0]}) kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}} with self.roundtrip(expected, save_kwargs=kwargs) as actual: assert_identical(expected, actual) assert actual.t0.encoding["units"] == "days since 1950-01-01" @requires_cftime def test_roundtrip_cftime_datetime_data(self) -> None: from xarray.tests.test_coding_times import _all_cftime_date_types date_types = _all_cftime_date_types() for date_type in date_types.values(): times = [date_type(1, 1, 1), date_type(1, 1, 2)] expected = Dataset({"t": ("t", times), "t0": times[0]}) kwargs = {"encoding": {"t0": {"units": "days since 0001-01-01"}}} expected_decoded_t = np.array(times) expected_decoded_t0 = np.array([date_type(1, 1, 1)]) expected_calendar = times[0].calendar with warnings.catch_warnings(): if expected_calendar in {"proleptic_gregorian", "standard"}: warnings.filterwarnings("ignore", "Unable to decode time axis") with self.roundtrip(expected, save_kwargs=kwargs) as actual: # proleptic gregorian will be decoded into numpy datetime64 # fixing to expectations if actual.t.dtype.kind == "M": dtype = actual.t.dtype expected_decoded_t = expected_decoded_t.astype(dtype) expected_decoded_t0 = expected_decoded_t0.astype(dtype) assert_array_equal(actual.t.values, expected_decoded_t) assert ( actual.t.encoding["units"] == "days since 0001-01-01 00:00:00.000000" ) assert actual.t.encoding["calendar"] == expected_calendar assert_array_equal(actual.t0.values, expected_decoded_t0) assert actual.t0.encoding["units"] == "days since 0001-01-01" assert actual.t.encoding["calendar"] == expected_calendar def test_roundtrip_timedelta_data(self) -> None: # todo: suggestion from review: # roundtrip large microsecond or coarser resolution timedeltas, # though we cannot test that until we fix the timedelta decoding # to support large ranges time_deltas = pd.to_timedelta(["1h", "2h", "NaT"]).as_unit("s") # type: ignore[arg-type, unused-ignore] encoding = {"units": "seconds"} expected = Dataset({"td": ("td", time_deltas), "td0": time_deltas[0]}) expected["td"].encoding = encoding expected["td0"].encoding = encoding with self.roundtrip( expected, open_kwargs={"decode_timedelta": CFTimedeltaCoder(time_unit="ns")} ) as actual: assert_identical(expected, actual) def test_roundtrip_timedelta_data_via_dtype( self, time_unit: PDDatetimeUnitOptions ) -> None: time_deltas = pd.to_timedelta(["1h", "2h", "NaT"]).as_unit(time_unit) # type: ignore[arg-type, unused-ignore] expected = Dataset( {"td": ("td", time_deltas), "td0": time_deltas[0].to_numpy()} ) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_float64_data(self) -> None: expected = Dataset({"x": ("y", np.array([1.0, 2.0, np.pi], dtype="float64"))}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) @requires_netcdf def test_roundtrip_example_1_netcdf(self) -> None: with open_example_dataset("example_1.nc") as expected: with self.roundtrip(expected) as actual: # we allow the attributes to differ since that # will depend on the encoding used. For example, # without CF encoding 'actual' will end up with # a dtype attribute. assert_equal(expected, actual) def test_roundtrip_coordinates(self) -> None: original = Dataset( {"foo": ("x", [0, 1])}, {"x": [2, 3], "y": ("a", [42]), "z": ("x", [4, 5])} ) with self.roundtrip(original) as actual: assert_identical(original, actual) original["foo"].encoding["coordinates"] = "y" with self.roundtrip(original, open_kwargs={"decode_coords": False}) as expected: # check roundtripping when decode_coords=False with self.roundtrip( expected, open_kwargs={"decode_coords": False} ) as actual: assert_identical(expected, actual) def test_roundtrip_global_coordinates(self) -> None: original = Dataset( {"foo": ("x", [0, 1])}, {"x": [2, 3], "y": ("a", [42]), "z": ("x", [4, 5])} ) with self.roundtrip(original) as actual: assert_identical(original, actual) # test that global "coordinates" is as expected _, attrs = encode_dataset_coordinates(original) assert attrs["coordinates"] == "y" # test warning when global "coordinates" is already set original.attrs["coordinates"] = "foo" with pytest.warns(SerializationWarning): _, attrs = encode_dataset_coordinates(original) assert attrs["coordinates"] == "foo" def test_roundtrip_coordinates_with_space(self) -> None: original = Dataset(coords={"x": 0, "y z": 1}) expected = Dataset({"y z": 1}, {"x": 0}) with pytest.warns(SerializationWarning): with self.roundtrip(original) as actual: assert_identical(expected, actual) def test_roundtrip_boolean_dtype(self) -> None: original = create_boolean_data() assert original["x"].dtype == "bool" with self.roundtrip(original) as actual: assert_identical(original, actual) assert actual["x"].dtype == "bool" # this checks for preserving dtype during second roundtrip # see https://github.com/pydata/xarray/issues/7652#issuecomment-1476956975 with self.roundtrip(actual) as actual2: assert_identical(original, actual2) assert actual2["x"].dtype == "bool" with self.roundtrip(actual) as actual3: # GH10536 assert_identical(original.transpose(), actual3.transpose()) def test_orthogonal_indexing(self) -> None: in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: indexers = {"dim1": [1, 2, 0], "dim2": [3, 2, 0, 3], "dim3": np.arange(5)} expected = in_memory.isel(indexers) actual = on_disk.isel(**indexers) # make sure the array is not yet loaded into memory assert not actual["var1"].variable._in_memory assert_identical(expected, actual) # do it twice, to make sure we're switched from orthogonal -> numpy # when we cached the values actual = on_disk.isel(**indexers) assert_identical(expected, actual) def test_vectorized_indexing(self) -> None: in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: indexers = { "dim1": DataArray([0, 2, 0], dims="a"), "dim2": DataArray([0, 2, 3], dims="a"), } expected = in_memory.isel(indexers) actual = on_disk.isel(**indexers) # make sure the array is not yet loaded into memory assert not actual["var1"].variable._in_memory assert_identical(expected, actual.load()) # do it twice, to make sure we're switched from # vectorized -> numpy when we cached the values actual = on_disk.isel(**indexers) assert_identical(expected, actual) def multiple_indexing(indexers): # make sure a sequence of lazy indexings certainly works. with self.roundtrip(in_memory) as on_disk: actual = on_disk["var3"] expected = in_memory["var3"] for ind in indexers: actual = actual.isel(ind) expected = expected.isel(ind) # make sure the array is not yet loaded into memory assert not actual.variable._in_memory assert_identical(expected, actual.load()) # two-staged vectorized-indexing indexers2 = [ { "dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"]), "dim3": DataArray([[0, 4], [1, 3], [2, 2]], dims=["a", "b"]), }, {"a": DataArray([0, 1], dims=["c"]), "b": DataArray([0, 1], dims=["c"])}, ] multiple_indexing(indexers2) # vectorized-slice mixed indexers3 = [ { "dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"]), "dim3": slice(None, 10), } ] multiple_indexing(indexers3) # vectorized-integer mixed indexers4 = [ {"dim3": 0}, {"dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"])}, {"a": slice(None, None, 2)}, ] multiple_indexing(indexers4) # vectorized-integer mixed indexers5 = [ {"dim3": 0}, {"dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"])}, {"a": 1, "b": 0}, ] multiple_indexing(indexers5) def test_vectorized_indexing_negative_step(self) -> None: # use dask explicitly when present open_kwargs: dict[str, Any] | None if has_dask: open_kwargs = {"chunks": {}} else: open_kwargs = None in_memory = create_test_data() def multiple_indexing(indexers): # make sure a sequence of lazy indexings certainly works. with self.roundtrip(in_memory, open_kwargs=open_kwargs) as on_disk: actual = on_disk["var3"] expected = in_memory["var3"] for ind in indexers: actual = actual.isel(ind) expected = expected.isel(ind) # make sure the array is not yet loaded into memory assert not actual.variable._in_memory assert_identical(expected, actual.load()) # with negative step slice. indexers = [ { "dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"]), "dim3": slice(-1, 1, -1), } ] multiple_indexing(indexers) # with negative step slice. indexers = [ { "dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"]), "dim3": slice(-1, 1, -2), } ] multiple_indexing(indexers) def test_outer_indexing_reversed(self) -> None: # regression test for GH6560 ds = xr.Dataset( {"z": (("t", "p", "y", "x"), np.ones((1, 1, 31, 40)))}, ) with self.roundtrip(ds) as on_disk: subset = on_disk.isel(t=[0], p=0).z[:, ::10, ::10][:, ::-1, :] assert subset.sizes == subset.load().sizes def test_isel_dataarray(self) -> None: # Make sure isel works lazily. GH:issue:1688 in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: expected = in_memory.isel(dim2=in_memory["dim2"] < 3) actual = on_disk.isel(dim2=on_disk["dim2"] < 3) assert_identical(expected, actual) def validate_array_type(self, ds): # Make sure that only NumpyIndexingAdapter stores a bare np.ndarray. def find_and_validate_array(obj): # recursively called function. obj: array or array wrapper. if hasattr(obj, "array"): if isinstance(obj.array, indexing.ExplicitlyIndexed): find_and_validate_array(obj.array) elif isinstance(obj.array, np.ndarray): assert isinstance(obj, indexing.NumpyIndexingAdapter) elif isinstance(obj.array, dask_array_type): assert isinstance(obj, indexing.DaskIndexingAdapter) elif isinstance(obj.array, pd.Index): assert isinstance(obj, indexing.PandasIndexingAdapter) else: raise TypeError(f"{type(obj.array)} is wrapped by {type(obj)}") for v in ds.variables.values(): find_and_validate_array(v._data) def test_array_type_after_indexing(self) -> None: in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: self.validate_array_type(on_disk) indexers = {"dim1": [1, 2, 0], "dim2": [3, 2, 0, 3], "dim3": np.arange(5)} expected = in_memory.isel(indexers) actual = on_disk.isel(**indexers) assert_identical(expected, actual) self.validate_array_type(actual) # do it twice, to make sure we're switched from orthogonal -> numpy # when we cached the values actual = on_disk.isel(**indexers) assert_identical(expected, actual) self.validate_array_type(actual) def test_dropna(self) -> None: # regression test for GH:issue:1694 a = np.random.randn(4, 3) a[1, 1] = np.nan in_memory = xr.Dataset( {"a": (("y", "x"), a)}, coords={"y": np.arange(4), "x": np.arange(3)} ) assert_identical( in_memory.dropna(dim="x"), in_memory.isel(x=slice(None, None, 2)) ) with self.roundtrip(in_memory) as on_disk: self.validate_array_type(on_disk) expected = in_memory.dropna(dim="x") actual = on_disk.dropna(dim="x") assert_identical(expected, actual) def test_ondisk_after_print(self) -> None: """Make sure print does not load file into memory""" in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: repr(on_disk) assert not on_disk["var1"]._in_memory class CFEncodedBase(DatasetIOBase): def test_roundtrip_bytes_with_fill_value(self) -> None: values = np.array([b"ab", b"cdef", np.nan], dtype=object) encoding = {"_FillValue": b"X", "dtype": "S1"} original = Dataset({"x": ("t", values, {}, encoding)}) expected = original.copy(deep=True) with self.roundtrip(original) as actual: assert_identical(expected, actual) original = Dataset({"x": ("t", values, {}, {"_FillValue": b""})}) with self.roundtrip(original) as actual: assert_identical(expected, actual) def test_roundtrip_string_with_fill_value_nchar(self) -> None: values = np.array(["ab", "cdef", np.nan], dtype=object) expected = Dataset({"x": ("t", values)}) encoding = {"dtype": "S1", "_FillValue": b"X"} original = Dataset({"x": ("t", values, {}, encoding)}) # Not supported yet. with pytest.raises(NotImplementedError): with self.roundtrip(original) as actual: assert_identical(expected, actual) def test_roundtrip_empty_vlen_string_array(self) -> None: # checks preserving vlen dtype for empty arrays GH7862 dtype = create_vlen_dtype(str) original = Dataset({"a": np.array([], dtype=dtype)}) assert check_vlen_dtype(original["a"].dtype) is str with self.roundtrip(original) as actual: assert_identical(original, actual) if np.issubdtype(actual["a"].dtype, object): # only check metadata for capable backends # eg. NETCDF3 based backends do not roundtrip metadata if actual["a"].dtype.metadata is not None: assert check_vlen_dtype(actual["a"].dtype) is str else: # zarr v3 sends back " None: if hasattr(self, "zarr_version") and dtype == np.float32: pytest.skip("float32 will be treated as float64 in zarr") decoded = decoded_fn(dtype) encoded = encoded_fn(dtype) if decoded["x"].encoding["dtype"] == "u1" and not ( (self.engine == "netcdf4" and self.file_format is None) or self.file_format == "NETCDF4" ): pytest.skip("uint8 data can't be written to non-NetCDF4 data") with self.roundtrip(decoded) as actual: for k in decoded.variables: assert decoded.variables[k].dtype == actual.variables[k].dtype # CF _FillValue is always on-disk type assert ( decoded.variables[k].encoding["_FillValue"] == actual.variables[k].encoding["_FillValue"] ) assert_allclose(decoded, actual, decode_bytes=False) with self.roundtrip(decoded, open_kwargs=dict(decode_cf=False)) as actual: # TODO: this assumes that all roundtrips will first # encode. Is that something we want to test for? for k in encoded.variables: assert encoded.variables[k].dtype == actual.variables[k].dtype # CF _FillValue is always on-disk type assert ( decoded.variables[k].encoding["_FillValue"] == actual.variables[k].attrs["_FillValue"] ) assert_allclose(encoded, actual, decode_bytes=False) with self.roundtrip(encoded, open_kwargs=dict(decode_cf=False)) as actual: for k in encoded.variables: assert encoded.variables[k].dtype == actual.variables[k].dtype # CF _FillValue is always on-disk type assert ( encoded.variables[k].attrs["_FillValue"] == actual.variables[k].attrs["_FillValue"] ) assert_allclose(encoded, actual, decode_bytes=False) # make sure roundtrip encoding didn't change the # original dataset. assert_allclose(encoded, encoded_fn(dtype), decode_bytes=False) with self.roundtrip(encoded) as actual: for k in decoded.variables: assert decoded.variables[k].dtype == actual.variables[k].dtype assert_allclose(decoded, actual, decode_bytes=False) @pytest.mark.parametrize( ("fill_value", "exp_fill_warning"), [ (np.int8(-1), False), (np.uint8(255), True), (-1, False), (255, True), ], ) def test_roundtrip_unsigned(self, fill_value, exp_fill_warning): @contextlib.contextmanager def _roundtrip_with_warnings(*args, **kwargs): is_np2 = module_available("numpy", minversion="2.0.0.dev0") if exp_fill_warning and is_np2: warn_checker: contextlib.AbstractContextManager = pytest.warns( SerializationWarning, match="_FillValue attribute can't be represented", ) else: warn_checker = contextlib.nullcontext() with warn_checker: with self.roundtrip(*args, **kwargs) as actual: yield actual # regression/numpy2 test for encoding = { "_FillValue": fill_value, "_Unsigned": "true", "dtype": "i1", } x = np.array([0, 1, 127, 128, 254, np.nan], dtype=np.float32) decoded = Dataset({"x": ("t", x, {}, encoding)}) attributes = { "_FillValue": fill_value, "_Unsigned": "true", } # Create unsigned data corresponding to [0, 1, 127, 128, 255] unsigned sb = np.asarray([0, 1, 127, -128, -2, -1], dtype="i1") encoded = Dataset({"x": ("t", sb, attributes)}) unsigned_dtype = np.dtype(f"u{sb.dtype.itemsize}") with _roundtrip_with_warnings(decoded) as actual: for k in decoded.variables: assert decoded.variables[k].dtype == actual.variables[k].dtype exp_fv = decoded.variables[k].encoding["_FillValue"] if exp_fill_warning: exp_fv = np.array(exp_fv, dtype=unsigned_dtype).view(sb.dtype) assert exp_fv == actual.variables[k].encoding["_FillValue"] assert_allclose(decoded, actual, decode_bytes=False) with _roundtrip_with_warnings( decoded, open_kwargs=dict(decode_cf=False) ) as actual: for k in encoded.variables: assert encoded.variables[k].dtype == actual.variables[k].dtype exp_fv = encoded.variables[k].attrs["_FillValue"] if exp_fill_warning: exp_fv = np.array(exp_fv, dtype=unsigned_dtype).view(sb.dtype) assert exp_fv == actual.variables[k].attrs["_FillValue"] assert_allclose(encoded, actual, decode_bytes=False) @staticmethod def _create_cf_dataset(): original = Dataset( dict( variable=( ("ln_p", "latitude", "longitude"), np.arange(8, dtype="f4").reshape(2, 2, 2), {"ancillary_variables": "std_devs det_lim"}, ), std_devs=( ("ln_p", "latitude", "longitude"), np.arange(0.1, 0.9, 0.1).reshape(2, 2, 2), {"standard_name": "standard_error"}, ), det_lim=( (), 0.1, {"standard_name": "detection_minimum"}, ), ), dict( latitude=("latitude", [0, 1], {"units": "degrees_north"}), longitude=("longitude", [0, 1], {"units": "degrees_east"}), latlon=((), -1, {"grid_mapping_name": "latitude_longitude"}), latitude_bnds=(("latitude", "bnds2"), [[0, 1], [1, 2]]), longitude_bnds=(("longitude", "bnds2"), [[0, 1], [1, 2]]), areas=( ("latitude", "longitude"), [[1, 1], [1, 1]], {"units": "degree^2"}, ), ln_p=( "ln_p", [1.0, 0.5], { "standard_name": "atmosphere_ln_pressure_coordinate", "computed_standard_name": "air_pressure", }, ), P0=((), 1013.25, {"units": "hPa"}), ), ) original["variable"].encoding.update( {"cell_measures": "area: areas", "grid_mapping": "latlon"}, ) original.coords["latitude"].encoding.update( dict(grid_mapping="latlon", bounds="latitude_bnds") ) original.coords["longitude"].encoding.update( dict(grid_mapping="latlon", bounds="longitude_bnds") ) original.coords["ln_p"].encoding.update({"formula_terms": "p0: P0 lev : ln_p"}) return original def test_grid_mapping_and_bounds_are_not_coordinates_in_file(self) -> None: original = self._create_cf_dataset() with self.roundtrip(original, open_kwargs={"decode_coords": False}) as ds: assert ds.coords["latitude"].attrs["bounds"] == "latitude_bnds" assert ds.coords["longitude"].attrs["bounds"] == "longitude_bnds" assert "coordinates" not in ds["variable"].attrs assert "coordinates" not in ds.attrs def test_coordinate_variables_after_dataset_roundtrip(self) -> None: original = self._create_cf_dataset() with self.roundtrip(original, open_kwargs={"decode_coords": "all"}) as actual: assert_identical(actual, original) with self.roundtrip(original) as actual: expected = original.reset_coords( ["latitude_bnds", "longitude_bnds", "areas", "P0", "latlon"] ) # equal checks that coords and data_vars are equal which # should be enough # identical would require resetting a number of attributes # skip that. assert_equal(actual, expected) def test_grid_mapping_and_bounds_are_coordinates_after_dataarray_roundtrip( self, ) -> None: original = self._create_cf_dataset() # The DataArray roundtrip should have the same warnings as the # Dataset, but we already tested for those, so just go for the # new warnings. It would appear that there is no way to tell # pytest "This warning and also this warning should both be # present". # xarray/tests/test_conventions.py::TestCFEncodedDataStore # needs the to_dataset. The other backends should be fine # without it. with pytest.warns( UserWarning, match=( r"Variable\(s\) referenced in bounds not in variables: " r"\['l(at|ong)itude_bnds'\]" ), ): with self.roundtrip( original["variable"].to_dataset(), open_kwargs={"decode_coords": "all"} ) as actual: assert_identical(actual, original["variable"].to_dataset()) @requires_iris @requires_netcdf def test_coordinate_variables_after_iris_roundtrip(self) -> None: original = self._create_cf_dataset() iris_cube = original["variable"].to_iris() actual = DataArray.from_iris(iris_cube) # Bounds will be missing (xfail) del original.coords["latitude_bnds"], original.coords["longitude_bnds"] # Ancillary vars will be missing # Those are data_vars, and will be dropped when grabbing the variable assert_identical(actual, original["variable"]) def test_coordinates_encoding(self) -> None: def equals_latlon(obj): return obj in {"lat lon", "lon lat"} original = Dataset( {"temp": ("x", [0, 1]), "precip": ("x", [0, -1])}, {"lat": ("x", [2, 3]), "lon": ("x", [4, 5])}, ) with self.roundtrip(original) as actual: assert_identical(actual, original) with self.roundtrip(original, open_kwargs=dict(decode_coords=False)) as ds: assert equals_latlon(ds["temp"].attrs["coordinates"]) assert equals_latlon(ds["precip"].attrs["coordinates"]) assert "coordinates" not in ds.attrs assert "coordinates" not in ds["lat"].attrs assert "coordinates" not in ds["lon"].attrs modified = original.drop_vars(["temp", "precip"]) with self.roundtrip(modified) as actual: assert_identical(actual, modified) with self.roundtrip(modified, open_kwargs=dict(decode_coords=False)) as ds: assert equals_latlon(ds.attrs["coordinates"]) assert "coordinates" not in ds["lat"].attrs assert "coordinates" not in ds["lon"].attrs original["temp"].encoding["coordinates"] = "lat" with self.roundtrip(original) as actual: assert_identical(actual, original) original["precip"].encoding["coordinates"] = "lat" with self.roundtrip(original, open_kwargs=dict(decode_coords=True)) as ds: assert "lon" not in ds["temp"].encoding["coordinates"] assert "lon" not in ds["precip"].encoding["coordinates"] assert "coordinates" not in ds["lat"].encoding assert "coordinates" not in ds["lon"].encoding def test_roundtrip_endian(self) -> None: skip_if_zarr_format_3("zarr v3 has not implemented endian support yet") ds = Dataset( { "x": np.arange(3, 10, dtype=">i2"), "y": np.arange(3, 20, dtype=" None: te = (TypeError, "string or None") ve = (ValueError, "string must be length 1 or") data = np.random.random((2, 2)) da = xr.DataArray(data) for name, (error, msg) in zip( [0, (4, 5), True, ""], [te, te, te, ve], strict=True ): ds = Dataset({name: da}) with pytest.raises(error) as excinfo: with self.roundtrip(ds): pass excinfo.match(msg) excinfo.match(repr(name)) def test_encoding_kwarg(self) -> None: ds = Dataset({"x": ("y", np.arange(10.0))}) kwargs: dict[str, Any] = dict(encoding={"x": {"dtype": "f4"}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: encoded_dtype = actual.x.encoding["dtype"] # On OS X, dtype sometimes switches endianness for unclear reasons assert encoded_dtype.kind == "f" and encoded_dtype.itemsize == 4 assert ds.x.encoding == {} kwargs = dict(encoding={"x": {"foo": "bar"}}) with pytest.raises(ValueError, match=r"unexpected encoding"): with self.roundtrip(ds, save_kwargs=kwargs) as actual: pass kwargs = dict(encoding={"x": "foo"}) with pytest.raises(ValueError, match=r"must be castable"): with self.roundtrip(ds, save_kwargs=kwargs) as actual: pass kwargs = dict(encoding={"invalid": {}}) with pytest.raises(KeyError): with self.roundtrip(ds, save_kwargs=kwargs) as actual: pass def test_encoding_unlimited_dims(self) -> None: if isinstance(self, ZarrBase): pytest.skip("No unlimited_dims handled in zarr.") ds = Dataset({"x": ("y", np.arange(10.0))}) with self.roundtrip(ds, save_kwargs=dict(unlimited_dims=["y"])) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) # Regression test for https://github.com/pydata/xarray/issues/2134 with self.roundtrip(ds, save_kwargs=dict(unlimited_dims="y")) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) ds.encoding = {"unlimited_dims": ["y"]} with self.roundtrip(ds) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) # Regression test for https://github.com/pydata/xarray/issues/2134 ds.encoding = {"unlimited_dims": "y"} with self.roundtrip(ds) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) # test unlimited_dims validation # https://github.com/pydata/xarray/issues/10549 ds.encoding = {"unlimited_dims": "z"} with pytest.raises( ValueError, match=r"Unlimited dimension\(s\) .* declared in 'dataset.encoding'", ): with self.roundtrip(ds) as _: pass ds.encoding = {} with pytest.raises( ValueError, match=r"Unlimited dimension\(s\) .* declared in 'unlimited_dims-kwarg'", ): with self.roundtrip(ds, save_kwargs=dict(unlimited_dims=["z"])) as _: pass def test_encoding_kwarg_dates(self) -> None: ds = Dataset({"t": pd.date_range("2000-01-01", periods=3)}) units = "days since 1900-01-01" kwargs = dict(encoding={"t": {"units": units}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert actual.t.encoding["units"] == units assert_identical(actual, ds) def test_encoding_kwarg_fixed_width_string(self) -> None: # regression test for GH2149 for strings in [[b"foo", b"bar", b"baz"], ["foo", "bar", "baz"]]: ds = Dataset({"x": strings}) kwargs = dict(encoding={"x": {"dtype": "S1"}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert actual["x"].encoding["dtype"] == "S1" assert_identical(actual, ds) def test_default_fill_value(self) -> None: # Test default encoding for float: ds = Dataset({"x": ("y", np.arange(10.0))}) kwargs = dict(encoding={"x": {"dtype": "f4"}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert math.isnan(actual.x.encoding["_FillValue"]) assert ds.x.encoding == {} # Test default encoding for int: ds = Dataset({"x": ("y", np.arange(10.0))}) kwargs = dict(encoding={"x": {"dtype": "int16"}}) with warnings.catch_warnings(): warnings.filterwarnings("ignore", ".*floating point data as an integer") with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert "_FillValue" not in actual.x.encoding assert ds.x.encoding == {} # Test default encoding for implicit int: ds = Dataset({"x": ("y", np.arange(10, dtype="int16"))}) with self.roundtrip(ds) as actual: assert "_FillValue" not in actual.x.encoding assert ds.x.encoding == {} def test_explicitly_omit_fill_value(self) -> None: ds = Dataset({"x": ("y", [np.pi, -np.pi])}) ds.x.encoding["_FillValue"] = None with self.roundtrip(ds) as actual: assert "_FillValue" not in actual.x.encoding def test_explicitly_omit_fill_value_via_encoding_kwarg(self) -> None: ds = Dataset({"x": ("y", [np.pi, -np.pi])}) kwargs = dict(encoding={"x": {"_FillValue": None}}) # _FillValue is not a valid encoding for Zarr with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert "_FillValue" not in actual.x.encoding assert ds.y.encoding == {} def test_explicitly_omit_fill_value_in_coord(self) -> None: ds = Dataset({"x": ("y", [np.pi, -np.pi])}, coords={"y": [0.0, 1.0]}) ds.y.encoding["_FillValue"] = None with self.roundtrip(ds) as actual: assert "_FillValue" not in actual.y.encoding def test_explicitly_omit_fill_value_in_coord_via_encoding_kwarg(self) -> None: ds = Dataset({"x": ("y", [np.pi, -np.pi])}, coords={"y": [0.0, 1.0]}) kwargs = dict(encoding={"y": {"_FillValue": None}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert "_FillValue" not in actual.y.encoding assert ds.y.encoding == {} def test_encoding_same_dtype(self) -> None: ds = Dataset({"x": ("y", np.arange(10.0, dtype="f4"))}) kwargs = dict(encoding={"x": {"dtype": "f4"}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: encoded_dtype = actual.x.encoding["dtype"] # On OS X, dtype sometimes switches endianness for unclear reasons assert encoded_dtype.kind == "f" and encoded_dtype.itemsize == 4 assert ds.x.encoding == {} def test_append_write(self) -> None: # regression for GH1215 data = create_test_data() with self.roundtrip_append(data) as actual: assert_identical(data, actual) def test_append_overwrite_values(self) -> None: # regression for GH1215 data = create_test_data() with create_tmp_file(allow_cleanup_failure=False) as tmp_file: self.save(data, tmp_file, mode="w") data["var2"][:] = -999 data["var9"] = data["var2"] * 3 self.save(data[["var2", "var9"]], tmp_file, mode="a") with self.open(tmp_file) as actual: assert_identical(data, actual) def test_append_with_invalid_dim_raises(self) -> None: data = create_test_data() with create_tmp_file(allow_cleanup_failure=False) as tmp_file: self.save(data, tmp_file, mode="w") data["var9"] = data["var2"] * 3 data = data.isel(dim1=slice(2, 6)) # modify one dimension with pytest.raises( ValueError, match=r"Unable to update size for existing dimension" ): self.save(data, tmp_file, mode="a") def test_multiindex_not_implemented(self) -> None: ds = Dataset(coords={"y": ("x", [1, 2]), "z": ("x", ["a", "b"])}).set_index( x=["y", "z"] ) with pytest.raises(NotImplementedError, match=r"MultiIndex"): with self.roundtrip(ds): pass # regression GH8628 (can serialize reset multi-index level coordinates) ds_reset = ds.reset_index("x") with self.roundtrip(ds_reset) as actual: assert_identical(actual, ds_reset) @requires_dask def test_string_object_warning(self) -> None: original = Dataset( { "x": ( [ "y", ], np.array(["foo", "bar"], dtype=object), ) } ).chunk() with pytest.warns(SerializationWarning, match="dask array with dtype=object"): with self.roundtrip(original) as actual: assert_identical(original, actual) @pytest.mark.parametrize( "indexer", ( {"y": [1]}, {"y": slice(2)}, {"y": 1}, {"x": [1], "y": [1]}, {"x": ("x0", [0, 1]), "y": ("x0", [0, 1])}, ), ) def test_indexing_roundtrip(self, indexer) -> None: # regression test for GH8909 ds = xr.Dataset() ds["A"] = xr.DataArray([[1, "a"], [2, "b"]], dims=["x", "y"]) with self.roundtrip(ds) as ds2: expected = ds2.sel(indexer) with self.roundtrip(expected) as actual: assert_identical(actual, expected) class NetCDFBase(CFEncodedBase): """Tests for all netCDF3 and netCDF4 backends.""" @pytest.mark.asyncio @pytest.mark.skip(reason="NetCDF backends don't support async loading") async def test_load_async(self) -> None: await super().test_load_async() @pytest.mark.skipif( ON_WINDOWS, reason="Windows does not allow modifying open files" ) def test_refresh_from_disk(self) -> None: # regression test for https://github.com/pydata/xarray/issues/4862 with create_tmp_file() as example_1_path: with create_tmp_file() as example_1_modified_path: with open_example_dataset("example_1.nc") as example_1: self.save(example_1, example_1_path) example_1.rh.values += 100 self.save(example_1, example_1_modified_path) a = open_dataset(example_1_path, engine=self.engine).load() # Simulate external process modifying example_1.nc while this script is running shutil.copy(example_1_modified_path, example_1_path) # Reopen example_1.nc (modified) as `b`; note that `a` has NOT been closed b = open_dataset(example_1_path, engine=self.engine).load() try: assert not np.array_equal(a.rh.values, b.rh.values) finally: a.close() b.close() def test_byte_attrs(self, byte_attrs_dataset: dict[str, Any]) -> None: # test for issue #9407 input = byte_attrs_dataset["input"] expected = byte_attrs_dataset["expected"] with self.roundtrip(input) as actual: assert_identical(actual, expected) _counter = itertools.count() @contextlib.contextmanager def create_tmp_file( suffix: str = ".nc", allow_cleanup_failure: bool = False ) -> Iterator[str]: temp_dir = tempfile.mkdtemp() path = os.path.join(temp_dir, f"temp-{next(_counter)}{suffix}") try: yield path finally: try: shutil.rmtree(temp_dir) except OSError: if not allow_cleanup_failure: raise @contextlib.contextmanager def create_tmp_files( nfiles: int, suffix: str = ".nc", allow_cleanup_failure: bool = False ) -> Iterator[list[str]]: with ExitStack() as stack: files = [ stack.enter_context(create_tmp_file(suffix, allow_cleanup_failure)) for _ in range(nfiles) ] yield files class NetCDF4Base(NetCDFBase): """Tests for both netCDF4-python and h5netcdf.""" engine: T_NetcdfEngine = "netcdf4" def test_open_group(self) -> None: # Create a netCDF file with a dataset stored within a group with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, "w") as rootgrp: foogrp = rootgrp.createGroup("foo") ds = foogrp ds.createDimension("time", size=10) x = np.arange(10) ds.createVariable("x", np.int32, dimensions=("time",)) ds.variables["x"][:] = x expected = Dataset() expected["x"] = ("time", x) # check equivalent ways to specify group for group in "foo", "/foo", "foo/", "/foo/": with self.open(tmp_file, group=group) as actual: assert_equal(actual["x"], expected["x"]) # check that missing group raises appropriate exception with pytest.raises(OSError): open_dataset(tmp_file, group="bar") with pytest.raises(ValueError, match=r"must be a string"): open_dataset(tmp_file, group=(1, 2, 3)) def test_open_subgroup(self) -> None: # Create a netCDF file with a dataset stored within a group within a # group with create_tmp_file() as tmp_file: rootgrp = nc4.Dataset(tmp_file, "w") foogrp = rootgrp.createGroup("foo") bargrp = foogrp.createGroup("bar") ds = bargrp ds.createDimension("time", size=10) x = np.arange(10) ds.createVariable("x", np.int32, dimensions=("time",)) ds.variables["x"][:] = x rootgrp.close() expected = Dataset() expected["x"] = ("time", x) # check equivalent ways to specify group for group in "foo/bar", "/foo/bar", "foo/bar/", "/foo/bar/": with self.open(tmp_file, group=group) as actual: assert_equal(actual["x"], expected["x"]) def test_write_groups(self) -> None: data1 = create_test_data() data2 = data1 * 2 with create_tmp_file() as tmp_file: self.save(data1, tmp_file, group="data/1") self.save(data2, tmp_file, group="data/2", mode="a") with self.open(tmp_file, group="data/1") as actual1: assert_identical(data1, actual1) with self.open(tmp_file, group="data/2") as actual2: assert_identical(data2, actual2) def test_child_group_with_inconsistent_dimensions(self) -> None: base = Dataset(coords={"x": [1, 2]}) child = Dataset(coords={"x": [1, 2, 3]}) with create_tmp_file() as tmp_file: self.save(base, tmp_file) self.save(child, tmp_file, group="child", mode="a") with self.open(tmp_file) as actual_base: assert_identical(base, actual_base) with self.open(tmp_file, group="child") as actual_child: assert_identical(child, actual_child) @pytest.mark.parametrize( "input_strings, is_bytes", [ ([b"foo", b"bar", b"baz"], True), (["foo", "bar", "baz"], False), (["foó", "bár", "baź"], False), ], ) def test_encoding_kwarg_vlen_string( self, input_strings: list[str], is_bytes: bool ) -> None: original = Dataset({"x": input_strings}) expected_string = ["foo", "bar", "baz"] if is_bytes else input_strings expected = Dataset({"x": expected_string}) kwargs = dict(encoding={"x": {"dtype": str}}) with self.roundtrip(original, save_kwargs=kwargs) as actual: assert actual["x"].encoding["dtype"] == "=U3" assert actual["x"].dtype == "=U3" assert_identical(actual, expected) @pytest.mark.parametrize("fill_value", ["XXX", "", "bár"]) def test_roundtrip_string_with_fill_value_vlen(self, fill_value: str) -> None: values = np.array(["ab", "cdef", np.nan], dtype=object) expected = Dataset({"x": ("t", values)}) original = Dataset({"x": ("t", values, {}, {"_FillValue": fill_value})}) with self.roundtrip(original) as actual: assert_identical(expected, actual) original = Dataset({"x": ("t", values, {}, {"_FillValue": ""})}) with self.roundtrip(original) as actual: assert_identical(expected, actual) def test_roundtrip_character_array(self) -> None: with create_tmp_file() as tmp_file: values = np.array([["a", "b", "c"], ["d", "e", "f"]], dtype="S") with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("x", 2) nc.createDimension("string3", 3) v = nc.createVariable("x", np.dtype("S1"), ("x", "string3")) v[:] = values values = np.array(["abc", "def"], dtype="S") expected = Dataset({"x": ("x", values)}) with open_dataset(tmp_file) as actual: assert_identical(expected, actual) # regression test for #157 with self.roundtrip(actual) as roundtripped: assert_identical(expected, roundtripped) def test_default_to_char_arrays(self) -> None: data = Dataset({"x": np.array(["foo", "zzzz"], dtype="S")}) with self.roundtrip(data) as actual: assert_identical(data, actual) assert actual["x"].dtype == np.dtype("S4") def test_open_encodings(self) -> None: # Create a netCDF file with explicit time units # and make sure it makes it into the encodings # and survives a round trip with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, "w") as ds: ds.createDimension("time", size=10) ds.createVariable("time", np.int32, dimensions=("time",)) units = "days since 1999-01-01" ds.variables["time"].setncattr("units", units) ds.variables["time"][:] = np.arange(10) + 4 expected = Dataset() time = pd.date_range("1999-01-05", periods=10, unit="ns") encoding = {"units": units, "dtype": np.dtype("int32")} expected["time"] = ("time", time, {}, encoding) with open_dataset(tmp_file) as actual: assert_equal(actual["time"], expected["time"]) actual_encoding = { k: v for k, v in actual["time"].encoding.items() if k in expected["time"].encoding } assert actual_encoding == expected["time"].encoding def test_dump_encodings(self) -> None: # regression test for #709 ds = Dataset({"x": ("y", np.arange(10.0))}) kwargs = dict(encoding={"x": {"zlib": True}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert actual.x.encoding["zlib"] def test_dump_and_open_encodings(self) -> None: # Create a netCDF file with explicit time units # and make sure it makes it into the encodings # and survives a round trip with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, "w") as ds: ds.createDimension("time", size=10) ds.createVariable("time", np.int32, dimensions=("time",)) units = "days since 1999-01-01" ds.variables["time"].setncattr("units", units) ds.variables["time"][:] = np.arange(10) + 4 with open_dataset(tmp_file) as xarray_dataset: with create_tmp_file() as tmp_file2: xarray_dataset.to_netcdf(tmp_file2) with nc4.Dataset(tmp_file2, "r") as ds: assert ds.variables["time"].getncattr("units") == units assert_array_equal(ds.variables["time"], np.arange(10) + 4) def test_compression_encoding_legacy(self) -> None: data = create_test_data() data["var2"].encoding.update( { "zlib": True, "chunksizes": (5, 5), "fletcher32": True, "shuffle": True, "original_shape": data.var2.shape, } ) with self.roundtrip(data) as actual: for k, v in data["var2"].encoding.items(): assert v == actual["var2"].encoding[k] # regression test for #156 expected = data.isel(dim1=0) with self.roundtrip(expected) as actual: assert_equal(expected, actual) def test_encoding_kwarg_compression(self) -> None: ds = Dataset({"x": np.arange(10.0)}) encoding = dict( dtype="f4", zlib=True, complevel=9, fletcher32=True, chunksizes=(5,), shuffle=True, ) kwargs = dict(encoding=dict(x=encoding)) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert_equal(actual, ds) assert actual.x.encoding["dtype"] == "f4" assert actual.x.encoding["zlib"] assert actual.x.encoding["complevel"] == 9 assert actual.x.encoding["fletcher32"] assert actual.x.encoding["chunksizes"] == (5,) assert actual.x.encoding["shuffle"] assert ds.x.encoding == {} def test_keep_chunksizes_if_no_original_shape(self) -> None: ds = Dataset({"x": [1, 2, 3]}) chunksizes = (2,) ds.variables["x"].encoding = {"chunksizes": chunksizes} with self.roundtrip(ds) as actual: assert_identical(ds, actual) assert_array_equal( ds["x"].encoding["chunksizes"], actual["x"].encoding["chunksizes"] ) def test_preferred_chunks_is_present(self) -> None: ds = Dataset({"x": [1, 2, 3]}) chunksizes = (2,) ds.variables["x"].encoding = {"chunksizes": chunksizes} with self.roundtrip(ds) as actual: assert actual["x"].encoding["preferred_chunks"] == {"x": 2} @requires_dask def test_auto_chunking_is_based_on_disk_chunk_sizes(self) -> None: x_size = y_size = 1000 y_chunksize = y_size x_chunksize = 10 with dask.config.set({"array.chunk-size": "100KiB"}): with self.chunked_roundtrip( (1, y_size, x_size), (1, y_chunksize, x_chunksize), open_kwargs={"chunks": "auto"}, ) as ds: t_chunks, y_chunks, x_chunks = ds["image"].data.chunks assert all(np.asanyarray(y_chunks) == y_chunksize) # Check that the chunk size is a multiple of the file chunk size assert all(np.asanyarray(x_chunks) % x_chunksize == 0) @requires_dask def test_base_chunking_uses_disk_chunk_sizes(self) -> None: x_size = y_size = 1000 y_chunksize = y_size x_chunksize = 10 with self.chunked_roundtrip( (1, y_size, x_size), (1, y_chunksize, x_chunksize), open_kwargs={"chunks": {}}, ) as ds: for chunksizes, expected in zip( ds["image"].data.chunks, (1, y_chunksize, x_chunksize), strict=True ): assert all(np.asanyarray(chunksizes) == expected) @contextlib.contextmanager def chunked_roundtrip( self, array_shape: tuple[int, int, int], chunk_sizes: tuple[int, int, int], open_kwargs: dict[str, Any] | None = None, ) -> Generator[Dataset, None, None]: t_size, y_size, x_size = array_shape t_chunksize, y_chunksize, x_chunksize = chunk_sizes image = xr.DataArray( np.arange(t_size * x_size * y_size, dtype=np.int16).reshape( (t_size, y_size, x_size) ), dims=["t", "y", "x"], ) image.encoding = {"chunksizes": (t_chunksize, y_chunksize, x_chunksize)} dataset = xr.Dataset(dict(image=image)) with self.roundtrip(dataset, open_kwargs=open_kwargs) as ds: yield ds def test_preferred_chunks_are_disk_chunk_sizes(self) -> None: x_size = y_size = 1000 y_chunksize = y_size x_chunksize = 10 with self.chunked_roundtrip( (1, y_size, x_size), (1, y_chunksize, x_chunksize) ) as ds: assert ds["image"].encoding["preferred_chunks"] == { "t": 1, "y": y_chunksize, "x": x_chunksize, } def test_encoding_chunksizes_unlimited(self) -> None: # regression test for GH1225 ds = Dataset({"x": [1, 2, 3], "y": ("x", [2, 3, 4])}) ds.variables["x"].encoding = { "zlib": False, "shuffle": False, "complevel": 0, "fletcher32": False, "contiguous": False, "chunksizes": (2**20,), "original_shape": (3,), } with self.roundtrip(ds) as actual: assert_equal(ds, actual) def test_mask_and_scale(self) -> None: with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("t", 5) nc.createVariable("x", "int16", ("t",), fill_value=-1) v = nc.variables["x"] v.set_auto_maskandscale(False) v.add_offset = 10 v.scale_factor = 0.1 v[:] = np.array([-1, -1, 0, 1, 2]) dtype = type(v.scale_factor) # first make sure netCDF4 reads the masked and scaled data # correctly with nc4.Dataset(tmp_file, mode="r") as nc: expected = np.ma.array( [-1, -1, 10, 10.1, 10.2], mask=[True, True, False, False, False] ) actual = nc.variables["x"][:] assert_array_equal(expected, actual) # now check xarray with open_dataset(tmp_file) as ds: expected = create_masked_and_scaled_data(np.dtype(dtype)) assert_identical(expected, ds) def test_0dimensional_variable(self) -> None: # This fix verifies our work-around to this netCDF4-python bug: # https://github.com/Unidata/netcdf4-python/pull/220 with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: v = nc.createVariable("x", "int16") v[...] = 123 with open_dataset(tmp_file) as ds: expected = Dataset({"x": ((), 123)}) assert_identical(expected, ds) def test_read_variable_len_strings(self) -> None: with create_tmp_file() as tmp_file: values = np.array(["foo", "bar", "baz"], dtype=object) with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("x", 3) v = nc.createVariable("x", str, ("x",)) v[:] = values expected = Dataset({"x": ("x", values)}) for kwargs in [{}, {"decode_cf": True}]: with open_dataset(tmp_file, **cast(dict, kwargs)) as actual: assert_identical(expected, actual) def test_raise_on_forward_slashes_in_names(self) -> None: # test for forward slash in variable names and dimensions # see GH 7943 data_vars: list[dict[str, Any]] = [ {"PASS/FAIL": (["PASSFAIL"], np.array([0]))}, {"PASS/FAIL": np.array([0])}, {"PASSFAIL": (["PASS/FAIL"], np.array([0]))}, ] for dv in data_vars: ds = Dataset(data_vars=dv) with pytest.raises(ValueError, match="Forward slashes '/' are not allowed"): with self.roundtrip(ds): pass @requires_netCDF4 def test_encoding_enum__no_fill_value(self, recwarn): with create_tmp_file() as tmp_file: cloud_type_dict = {"clear": 0, "cloudy": 1} with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("time", size=2) cloud_type = nc.createEnumType(np.uint8, "cloud_type", cloud_type_dict) v = nc.createVariable( "clouds", cloud_type, "time", fill_value=None, ) v[:] = 1 with open_dataset(tmp_file) as original: save_kwargs = {} # We don't expect any errors. # This is effectively a void context manager expected_warnings = 0 if self.engine == "h5netcdf": if not has_h5netcdf_1_4_0_or_above: save_kwargs["invalid_netcdf"] = True expected_warnings = 1 expected_msg = "You are writing invalid netcdf features to file" else: expected_warnings = 1 expected_msg = "Creating variable with default fill_value 0 which IS defined in enum type" with self.roundtrip(original, save_kwargs=save_kwargs) as actual: assert len(recwarn) == expected_warnings if expected_warnings: assert issubclass(recwarn[0].category, UserWarning) assert str(recwarn[0].message).startswith(expected_msg) assert_equal(original, actual) assert ( actual.clouds.encoding["dtype"].metadata["enum"] == cloud_type_dict ) if not ( self.engine == "h5netcdf" and not has_h5netcdf_1_4_0_or_above ): # not implemented in h5netcdf yet assert ( actual.clouds.encoding["dtype"].metadata["enum_name"] == "cloud_type" ) @requires_netCDF4 def test_encoding_enum__multiple_variable_with_enum(self): with create_tmp_file() as tmp_file: cloud_type_dict = {"clear": 0, "cloudy": 1, "missing": 255} with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("time", size=2) cloud_type = nc.createEnumType(np.uint8, "cloud_type", cloud_type_dict) nc.createVariable( "clouds", cloud_type, "time", fill_value=255, ) nc.createVariable( "tifa", cloud_type, "time", fill_value=255, ) with open_dataset(tmp_file) as original: save_kwargs = {} if self.engine == "h5netcdf" and not has_h5netcdf_1_4_0_or_above: save_kwargs["invalid_netcdf"] = True with self.roundtrip(original, save_kwargs=save_kwargs) as actual: assert_equal(original, actual) assert ( actual.clouds.encoding["dtype"] == actual.tifa.encoding["dtype"] ) assert ( actual.clouds.encoding["dtype"].metadata == actual.tifa.encoding["dtype"].metadata ) assert ( actual.clouds.encoding["dtype"].metadata["enum"] == cloud_type_dict ) if not ( self.engine == "h5netcdf" and not has_h5netcdf_1_4_0_or_above ): # not implemented in h5netcdf yet assert ( actual.clouds.encoding["dtype"].metadata["enum_name"] == "cloud_type" ) @requires_netCDF4 def test_encoding_enum__error_multiple_variable_with_changing_enum(self): """ Given 2 variables, if they share the same enum type, the 2 enum definition should be identical. """ with create_tmp_file() as tmp_file: cloud_type_dict = {"clear": 0, "cloudy": 1, "missing": 255} with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("time", size=2) cloud_type = nc.createEnumType(np.uint8, "cloud_type", cloud_type_dict) nc.createVariable( "clouds", cloud_type, "time", fill_value=255, ) nc.createVariable( "tifa", cloud_type, "time", fill_value=255, ) with open_dataset(tmp_file) as original: assert ( original.clouds.encoding["dtype"].metadata == original.tifa.encoding["dtype"].metadata ) modified_enum = original.clouds.encoding["dtype"].metadata["enum"] modified_enum.update({"neblig": 2}) original.clouds.encoding["dtype"] = np.dtype( "u1", metadata={"enum": modified_enum, "enum_name": "cloud_type"}, ) if not (self.engine == "h5netcdf" and not has_h5netcdf_1_4_0_or_above): # not implemented yet in h5netcdf with pytest.raises( ValueError, match=( "Cannot save variable .*" " because an enum `cloud_type` already exists in the Dataset .*" ), ): with self.roundtrip(original): pass @pytest.mark.parametrize("create_default_indexes", [True, False]) def test_create_default_indexes(self, tmp_path, create_default_indexes) -> None: store_path = tmp_path / "tmp.nc" original_ds = xr.Dataset( {"data": ("x", np.arange(3))}, coords={"x": [-1, 0, 1]} ) original_ds.to_netcdf(store_path, engine=self.engine, mode="w") with open_dataset( store_path, engine=self.engine, create_default_indexes=create_default_indexes, ) as loaded_ds: if create_default_indexes: assert list(loaded_ds.xindexes) == ["x"] and isinstance( loaded_ds.xindexes["x"], PandasIndex ) else: assert len(loaded_ds.xindexes) == 0 @requires_netCDF4 class TestNetCDF4Data(NetCDF4Base): @contextlib.contextmanager def create_store(self): with create_tmp_file() as tmp_file: with backends.NetCDF4DataStore.open(tmp_file, mode="w") as store: yield store def test_variable_order(self) -> None: # doesn't work with scipy or h5py :( ds = Dataset() ds["a"] = 1 ds["z"] = 2 ds["b"] = 3 ds.coords["c"] = 4 with self.roundtrip(ds) as actual: assert list(ds.variables) == list(actual.variables) def test_unsorted_index_raises(self) -> None: # should be fixed in netcdf4 v1.2.1 random_data = np.random.random(size=(4, 6)) dim0 = [0, 1, 2, 3] dim1 = [0, 2, 1, 3, 5, 4] # We will sort this in a later step da = xr.DataArray( data=random_data, dims=("dim0", "dim1"), coords={"dim0": dim0, "dim1": dim1}, name="randovar", ) ds = da.to_dataset() with self.roundtrip(ds) as ondisk: inds = np.argsort(dim1) ds2 = ondisk.isel(dim1=inds) # Older versions of NetCDF4 raise an exception here, and if so we # want to ensure we improve (that is, replace) the error message try: _ = ds2.randovar.values except IndexError as err: assert "first by calling .load" in str(err) def test_setncattr_string(self) -> None: list_of_strings = ["list", "of", "strings"] one_element_list_of_strings = ["one element"] one_string = "one string" attrs = { "foo": list_of_strings, "bar": one_element_list_of_strings, "baz": one_string, } ds = Dataset({"x": ("y", [1, 2, 3], attrs)}, attrs=attrs) with self.roundtrip(ds) as actual: for totest in [actual, actual["x"]]: assert_array_equal(list_of_strings, totest.attrs["foo"]) assert_array_equal(one_element_list_of_strings, totest.attrs["bar"]) assert one_string == totest.attrs["baz"] @pytest.mark.parametrize( "compression", [ None, "zlib", "szip", ], ) @requires_netCDF4_1_6_2_or_above @pytest.mark.xfail(ON_WINDOWS, reason="new compression not yet implemented") def test_compression_encoding(self, compression: str | None) -> None: data = create_test_data(dim_sizes=(20, 80, 10)) encoding_params: dict[str, Any] = dict(compression=compression, blosc_shuffle=1) data["var2"].encoding.update(encoding_params) data["var2"].encoding.update( { "chunksizes": (20, 40), "original_shape": data.var2.shape, "blosc_shuffle": 1, "fletcher32": False, } ) with self.roundtrip(data) as actual: expected_encoding = data["var2"].encoding.copy() # compression does not appear in the retrieved encoding, that differs # from the input encoding. shuffle also chantges. Here we modify the # expected encoding to account for this compression = expected_encoding.pop("compression") blosc_shuffle = expected_encoding.pop("blosc_shuffle") if compression is not None: if "blosc" in compression and blosc_shuffle: expected_encoding["blosc"] = { "compressor": compression, "shuffle": blosc_shuffle, } expected_encoding["shuffle"] = False elif compression == "szip": expected_encoding["szip"] = { "coding": "nn", "pixels_per_block": 8, } expected_encoding["shuffle"] = False else: # This will set a key like zlib=true which is what appears in # the encoding when we read it. expected_encoding[compression] = True if compression == "zstd": expected_encoding["shuffle"] = False else: expected_encoding["shuffle"] = False actual_encoding = actual["var2"].encoding assert expected_encoding.items() <= actual_encoding.items() if ( encoding_params["compression"] is not None and "blosc" not in encoding_params["compression"] ): # regression test for #156 expected = data.isel(dim1=0) with self.roundtrip(expected) as actual: assert_equal(expected, actual) @pytest.mark.skip(reason="https://github.com/Unidata/netcdf4-python/issues/1195") def test_refresh_from_disk(self) -> None: super().test_refresh_from_disk() @requires_netCDF4_1_7_0_or_above def test_roundtrip_complex(self): expected = Dataset({"x": ("y", np.ones(5) + 1j * np.ones(5))}) skwargs = dict(auto_complex=True) okwargs = dict(auto_complex=True) with self.roundtrip( expected, save_kwargs=skwargs, open_kwargs=okwargs ) as actual: assert_equal(expected, actual) @requires_netCDF4 class TestNetCDF4AlreadyOpen: def test_base_case(self) -> None: with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: v = nc.createVariable("x", "int") v[...] = 42 nc = nc4.Dataset(tmp_file, mode="r") store = backends.NetCDF4DataStore(nc) with open_dataset(store) as ds: expected = Dataset({"x": ((), 42)}) assert_identical(expected, ds) def test_group(self) -> None: with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: group = nc.createGroup("g") v = group.createVariable("x", "int") v[...] = 42 nc = nc4.Dataset(tmp_file, mode="r") store = backends.NetCDF4DataStore(nc.groups["g"]) with open_dataset(store) as ds: expected = Dataset({"x": ((), 42)}) assert_identical(expected, ds) nc = nc4.Dataset(tmp_file, mode="r") store = backends.NetCDF4DataStore(nc, group="g") with open_dataset(store) as ds: expected = Dataset({"x": ((), 42)}) assert_identical(expected, ds) with nc4.Dataset(tmp_file, mode="r") as nc: with pytest.raises(ValueError, match="must supply a root"): backends.NetCDF4DataStore(nc.groups["g"], group="g") def test_deepcopy(self) -> None: # regression test for https://github.com/pydata/xarray/issues/4425 with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("x", 10) v = nc.createVariable("y", np.int32, ("x",)) v[:] = np.arange(10) h5 = nc4.Dataset(tmp_file, mode="r") store = backends.NetCDF4DataStore(h5) with open_dataset(store) as ds: copied = ds.copy(deep=True) expected = Dataset({"y": ("x", np.arange(10))}) assert_identical(expected, copied) @requires_netCDF4 @requires_dask @pytest.mark.filterwarnings("ignore:deallocating CachingFileManager") class TestNetCDF4ViaDaskData(TestNetCDF4Data): @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if open_kwargs is None: open_kwargs = {} if save_kwargs is None: save_kwargs = {} open_kwargs.setdefault("chunks", -1) with TestNetCDF4Data.roundtrip( self, data, save_kwargs, open_kwargs, allow_cleanup_failure ) as ds: yield ds def test_unsorted_index_raises(self) -> None: # Skip when using dask because dask rewrites indexers to getitem, # dask first pulls items by block. pass @pytest.mark.skip(reason="caching behavior differs for dask") def test_dataset_caching(self) -> None: pass def test_write_inconsistent_chunks(self) -> None: # Construct two variables with the same dimensions, but different # chunk sizes. x = da.zeros((100, 100), dtype="f4", chunks=(50, 100)) x = DataArray(data=x, dims=("lat", "lon"), name="x") x.encoding["chunksizes"] = (50, 100) x.encoding["original_shape"] = (100, 100) y = da.ones((100, 100), dtype="f4", chunks=(100, 50)) y = DataArray(data=y, dims=("lat", "lon"), name="y") y.encoding["chunksizes"] = (100, 50) y.encoding["original_shape"] = (100, 100) # Put them both into the same dataset ds = Dataset({"x": x, "y": y}) with self.roundtrip(ds) as actual: assert actual["x"].encoding["chunksizes"] == (50, 100) assert actual["y"].encoding["chunksizes"] == (100, 50) # Flaky test. Very open to contributions on fixing this @pytest.mark.flaky def test_roundtrip_coordinates(self) -> None: super().test_roundtrip_coordinates() @requires_cftime def test_roundtrip_cftime_bnds(self): # Regression test for issue #7794 import cftime original = xr.Dataset( { "foo": ("time", [0.0]), "time_bnds": ( ("time", "bnds"), [ [ cftime.Datetime360Day(2005, 12, 1, 0, 0, 0, 0), cftime.Datetime360Day(2005, 12, 2, 0, 0, 0, 0), ] ], ), }, {"time": [cftime.Datetime360Day(2005, 12, 1, 12, 0, 0, 0)]}, ) with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with open_dataset(tmp_file) as actual: # Operation to load actual time_bnds into memory assert_array_equal(actual.time_bnds.values, original.time_bnds.values) chunked = actual.chunk(time=1) with create_tmp_file() as tmp_file_chunked: chunked.to_netcdf(tmp_file_chunked) @requires_zarr @pytest.mark.usefixtures("default_zarr_format") class ZarrBase(CFEncodedBase): DIMENSION_KEY = "_ARRAY_DIMENSIONS" zarr_version = 2 version_kwargs: dict[str, Any] = {} def create_zarr_target(self): raise NotImplementedError @contextlib.contextmanager def create_store(self, cache_members: bool = False): with self.create_zarr_target() as store_target: yield backends.ZarrStore.open_group( store_target, mode="w", cache_members=cache_members, **self.version_kwargs, ) def save(self, dataset, store_target, **kwargs): # type: ignore[override] return dataset.to_zarr(store=store_target, **kwargs, **self.version_kwargs) @contextlib.contextmanager def open(self, path, **kwargs): with xr.open_dataset( path, engine="zarr", mode="r", **kwargs, **self.version_kwargs ) as ds: yield ds @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} with self.create_zarr_target() as store_target: self.save(data, store_target, **save_kwargs) with self.open(store_target, **open_kwargs) as ds: yield ds @pytest.mark.asyncio @pytest.mark.skipif( not has_zarr_v3, reason="zarr-python <3 did not support async loading", ) async def test_load_async(self) -> None: await super().test_load_async() def test_roundtrip_bytes_with_fill_value(self): pytest.xfail("Broken by Zarr 3.0.7") @pytest.mark.parametrize("consolidated", [False, True, None]) def test_roundtrip_consolidated(self, consolidated) -> None: expected = create_test_data() with self.roundtrip( expected, save_kwargs={"consolidated": consolidated}, open_kwargs={"backend_kwargs": {"consolidated": consolidated}}, ) as actual: self.check_dtypes_roundtripped(expected, actual) assert_identical(expected, actual) def test_read_non_consolidated_warning(self) -> None: expected = create_test_data() with self.create_zarr_target() as store: self.save( expected, store_target=store, consolidated=False, **self.version_kwargs ) if getattr(store, "supports_consolidated_metadata", True): with pytest.warns( RuntimeWarning, match="Failed to open Zarr store with consolidated", ): with xr.open_zarr(store, **self.version_kwargs) as ds: assert_identical(ds, expected) def test_non_existent_store(self) -> None: with pytest.raises( FileNotFoundError, match="(No such file or directory|Unable to find group|No group found in store)", ): xr.open_zarr(f"{uuid.uuid4()}") @pytest.mark.skipif(has_zarr_v3, reason="chunk_store not implemented in zarr v3") def test_with_chunkstore(self) -> None: expected = create_test_data() with ( self.create_zarr_target() as store_target, self.create_zarr_target() as chunk_store, ): save_kwargs = {"chunk_store": chunk_store} self.save(expected, store_target, **save_kwargs) # the chunk store must have been populated with some entries assert len(chunk_store) > 0 open_kwargs = {"backend_kwargs": {"chunk_store": chunk_store}} with self.open(store_target, **open_kwargs) as ds: assert_equal(ds, expected) @requires_dask def test_auto_chunk(self) -> None: original = create_test_data().chunk() with self.roundtrip(original, open_kwargs={"chunks": None}) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # there should be no chunks assert v.chunks is None with self.roundtrip(original, open_kwargs={"chunks": {}}) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # chunk size should be the same as original assert v.chunks == original[k].chunks @requires_dask @pytest.mark.filterwarnings("ignore:The specified chunks separate:UserWarning") def test_manual_chunk(self) -> None: original = create_test_data().chunk({"dim1": 3, "dim2": 4, "dim3": 3}) # Using chunks = None should return non-chunked arrays open_kwargs: dict[str, Any] = {"chunks": None} with self.roundtrip(original, open_kwargs=open_kwargs) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # there should be no chunks assert v.chunks is None # uniform arrays for i in range(2, 6): rechunked = original.chunk(chunks=i) open_kwargs = {"chunks": i} with self.roundtrip(original, open_kwargs=open_kwargs) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # chunk size should be the same as rechunked assert v.chunks == rechunked[k].chunks chunks = {"dim1": 2, "dim2": 3, "dim3": 5} rechunked = original.chunk(chunks=chunks) open_kwargs = { "chunks": chunks, "backend_kwargs": {"overwrite_encoded_chunks": True}, } with self.roundtrip(original, open_kwargs=open_kwargs) as actual: for k, v in actual.variables.items(): assert v.chunks == rechunked[k].chunks with self.roundtrip(actual) as auto: # encoding should have changed for k, v in actual.variables.items(): assert v.chunks == rechunked[k].chunks assert_identical(actual, auto) assert_identical(actual.load(), auto.load()) @requires_dask @pytest.mark.filterwarnings("ignore:.*does not have a Zarr V3 specification.*") def test_warning_on_bad_chunks(self) -> None: original = create_test_data().chunk({"dim1": 4, "dim2": 3, "dim3": 3}) bad_chunks = (2, {"dim2": (3, 3, 2, 1)}) for chunks in bad_chunks: kwargs = {"chunks": chunks} with pytest.warns(UserWarning): with self.roundtrip(original, open_kwargs=kwargs) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) good_chunks: tuple[dict[str, Any], ...] = ({"dim2": 3}, {"dim3": (6, 4)}, {}) for chunks in good_chunks: kwargs = {"chunks": chunks} with assert_no_warnings(): with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message=".*Zarr format 3 specification.*", category=UserWarning, ) with self.roundtrip(original, open_kwargs=kwargs) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) @requires_dask def test_deprecate_auto_chunk(self) -> None: original = create_test_data().chunk() with pytest.raises(TypeError): with self.roundtrip(original, open_kwargs={"auto_chunk": True}) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # chunk size should be the same as original assert v.chunks == original[k].chunks with pytest.raises(TypeError): with self.roundtrip(original, open_kwargs={"auto_chunk": False}) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # there should be no chunks assert v.chunks is None @requires_dask def test_write_uneven_dask_chunks(self) -> None: # regression for GH#2225 original = create_test_data().chunk({"dim1": 3, "dim2": 4, "dim3": 3}) with self.roundtrip(original, open_kwargs={"chunks": {}}) as actual: for k, v in actual.data_vars.items(): assert v.chunks == actual[k].chunks def test_chunk_encoding(self) -> None: # These datasets have no dask chunks. All chunking specified in # encoding data = create_test_data() chunks = (5, 5) data["var2"].encoding.update({"chunks": chunks}) with self.roundtrip(data) as actual: assert chunks == actual["var2"].encoding["chunks"] # expect an error with non-integer chunks data["var2"].encoding.update({"chunks": (5, 4.5)}) with pytest.raises(TypeError): with self.roundtrip(data) as actual: pass def test_shard_encoding(self) -> None: # These datasets have no dask chunks. All chunking/sharding specified in # encoding if has_zarr_v3 and zarr.config.config["default_zarr_format"] == 3: data = create_test_data() chunks = (1, 1) shards = (5, 5) data["var2"].encoding.update({"chunks": chunks}) data["var2"].encoding.update({"shards": shards}) with self.roundtrip(data) as actual: assert shards == actual["var2"].encoding["shards"] # expect an error with shards not divisible by chunks data["var2"].encoding.update({"chunks": (2, 2)}) with pytest.raises(ValueError): with self.roundtrip(data) as actual: pass @requires_dask @pytest.mark.skipif( ON_WINDOWS, reason="Very flaky on Windows CI. Can re-enable assuming it starts consistently passing.", ) def test_chunk_encoding_with_dask(self) -> None: # These datasets DO have dask chunks. Need to check for various # interactions between dask and zarr chunks ds = xr.DataArray((np.arange(12)), dims="x", name="var1").to_dataset() # - no encoding specified - # zarr automatically gets chunk information from dask chunks ds_chunk4 = ds.chunk({"x": 4}) with self.roundtrip(ds_chunk4) as actual: assert (4,) == actual["var1"].encoding["chunks"] # should fail if dask_chunks are irregular... ds_chunk_irreg = ds.chunk({"x": (5, 4, 3)}) with pytest.raises(ValueError, match=r"uniform chunk sizes."): with self.roundtrip(ds_chunk_irreg) as actual: pass # should fail if encoding["chunks"] clashes with dask_chunks badenc = ds.chunk({"x": 4}) badenc.var1.encoding["chunks"] = (6,) with pytest.raises(ValueError, match=r"named 'var1' would overlap"): with self.roundtrip(badenc) as actual: pass # unless... with self.roundtrip(badenc, save_kwargs={"safe_chunks": False}) as actual: # don't actually check equality because the data could be corrupted pass # if dask chunks (4) are an integer multiple of zarr chunks (2) it should not fail... goodenc = ds.chunk({"x": 4}) goodenc.var1.encoding["chunks"] = (2,) with self.roundtrip(goodenc) as actual: pass # if initial dask chunks are aligned, size of last dask chunk doesn't matter goodenc = ds.chunk({"x": (3, 3, 6)}) goodenc.var1.encoding["chunks"] = (3,) with self.roundtrip(goodenc) as actual: pass goodenc = ds.chunk({"x": (3, 6, 3)}) goodenc.var1.encoding["chunks"] = (3,) with self.roundtrip(goodenc) as actual: pass # ... also if the last chunk is irregular ds_chunk_irreg = ds.chunk({"x": (5, 5, 2)}) with self.roundtrip(ds_chunk_irreg) as actual: assert (5,) == actual["var1"].encoding["chunks"] # re-save Zarr arrays with self.roundtrip(ds_chunk_irreg) as original: with self.roundtrip(original) as actual: assert_identical(original, actual) # but intermediate unaligned chunks are bad badenc = ds.chunk({"x": (3, 5, 3, 1)}) badenc.var1.encoding["chunks"] = (3,) with pytest.raises(ValueError, match=r"would overlap multiple Dask chunks"): with self.roundtrip(badenc) as actual: pass # - encoding specified - # specify compatible encodings for chunk_enc in 4, (4,): ds_chunk4["var1"].encoding.update({"chunks": chunk_enc}) with self.roundtrip(ds_chunk4) as actual: assert (4,) == actual["var1"].encoding["chunks"] # TODO: remove this failure once synchronized overlapping writes are # supported by xarray ds_chunk4["var1"].encoding.update({"chunks": 5}) with pytest.raises(ValueError, match=r"named 'var1' would overlap"): with self.roundtrip(ds_chunk4) as actual: pass # override option with self.roundtrip(ds_chunk4, save_kwargs={"safe_chunks": False}) as actual: # don't actually check equality because the data could be corrupted pass @requires_netcdf def test_drop_encoding(self): with open_example_dataset("example_1.nc") as ds: encodings = {v: {**ds[v].encoding} for v in ds.data_vars} with self.create_zarr_target() as store: ds.to_zarr(store, encoding=encodings) def test_hidden_zarr_keys(self) -> None: skip_if_zarr_format_3("This test is unnecessary; no hidden Zarr keys") expected = create_test_data() with self.create_store() as store: expected.dump_to_store(store) zarr_group = store.ds # check that a variable hidden attribute is present and correct # JSON only has a single array type, which maps to list in Python. # In contrast, dims in xarray is always a tuple. for var in expected.variables.keys(): dims = zarr_group[var].attrs[self.DIMENSION_KEY] assert dims == list(expected[var].dims) with xr.decode_cf(store): # make sure it is hidden for var in expected.variables.keys(): assert self.DIMENSION_KEY not in expected[var].attrs # put it back and try removing from a variable attrs = dict(zarr_group["var2"].attrs) del attrs[self.DIMENSION_KEY] zarr_group["var2"].attrs.put(attrs) with pytest.raises(KeyError): with xr.decode_cf(store): pass def test_dimension_names(self) -> None: skip_if_zarr_format_2("No dimension names in V2") expected = create_test_data() with self.create_store() as store: expected.dump_to_store(store) zarr_group = store.ds for var in zarr_group: assert expected[var].dims == zarr_group[var].metadata.dimension_names @pytest.mark.parametrize("group", [None, "group1"]) def test_write_persistence_modes(self, group) -> None: original = create_test_data() # overwrite mode with self.roundtrip( original, save_kwargs={"mode": "w", "group": group}, open_kwargs={"group": group}, ) as actual: assert_identical(original, actual) # don't overwrite mode with self.roundtrip( original, save_kwargs={"mode": "w-", "group": group}, open_kwargs={"group": group}, ) as actual: assert_identical(original, actual) # make sure overwriting works as expected with self.create_zarr_target() as store: self.save(original, store) # should overwrite with no error self.save(original, store, mode="w", group=group) with self.open(store, group=group) as actual: assert_identical(original, actual) with pytest.raises((ValueError, FileExistsError)): self.save(original, store, mode="w-") # check append mode for normal write with self.roundtrip( original, save_kwargs={"mode": "a", "group": group}, open_kwargs={"group": group}, ) as actual: assert_identical(original, actual) # check append mode for append write ds, ds_to_append, _ = create_append_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", group=group, **self.version_kwargs) ds_to_append.to_zarr( store_target, append_dim="time", group=group, **self.version_kwargs ) original = xr.concat([ds, ds_to_append], dim="time") actual = xr.open_dataset( store_target, group=group, engine="zarr", **self.version_kwargs ) assert_identical(original, actual) def test_compressor_encoding(self) -> None: # specify a custom compressor original = create_test_data() if has_zarr_v3 and zarr.config.config["default_zarr_format"] == 3: encoding_key = "compressors" # all parameters need to be explicitly specified in order for the comparison to pass below encoding = { "serializer": zarr.codecs.BytesCodec(endian="little"), encoding_key: ( zarr.codecs.BloscCodec( cname="zstd", clevel=3, shuffle="shuffle", typesize=8, blocksize=0, ), ), } else: from numcodecs.blosc import Blosc encoding_key = "compressors" if has_zarr_v3 else "compressor" comp = Blosc(cname="zstd", clevel=3, shuffle=2) encoding = {encoding_key: (comp,) if has_zarr_v3 else comp} save_kwargs = dict(encoding={"var1": encoding}) with self.roundtrip(original, save_kwargs=save_kwargs) as ds: enc = ds["var1"].encoding[encoding_key] assert enc == encoding[encoding_key] def test_group(self) -> None: original = create_test_data() group = "some/random/path" with self.roundtrip( original, save_kwargs={"group": group}, open_kwargs={"group": group} ) as actual: assert_identical(original, actual) def test_zarr_mode_w_overwrites_encoding(self) -> None: data = Dataset({"foo": ("x", [1.0, 1.0, 1.0])}) with self.create_zarr_target() as store: data.to_zarr( store, **self.version_kwargs, encoding={"foo": {"add_offset": 1}} ) np.testing.assert_equal( zarr.open_group(store, **self.version_kwargs)["foo"], data.foo.data - 1 ) data.to_zarr( store, **self.version_kwargs, encoding={"foo": {"add_offset": 0}}, mode="w", ) np.testing.assert_equal( zarr.open_group(store, **self.version_kwargs)["foo"], data.foo.data ) def test_encoding_kwarg_fixed_width_string(self) -> None: # not relevant for zarr, since we don't use EncodedStringCoder pass def test_dataset_caching(self) -> None: super().test_dataset_caching() def test_append_write(self) -> None: super().test_append_write() def test_append_with_mode_rplus_success(self) -> None: original = Dataset({"foo": ("x", [1])}) modified = Dataset({"foo": ("x", [2])}) with self.create_zarr_target() as store: original.to_zarr(store, **self.version_kwargs) modified.to_zarr(store, mode="r+", **self.version_kwargs) with self.open(store) as actual: assert_identical(actual, modified) def test_append_with_mode_rplus_fails(self) -> None: original = Dataset({"foo": ("x", [1])}) modified = Dataset({"bar": ("x", [2])}) with self.create_zarr_target() as store: original.to_zarr(store, **self.version_kwargs) with pytest.raises( ValueError, match="dataset contains non-pre-existing variables" ): modified.to_zarr(store, mode="r+", **self.version_kwargs) def test_append_with_invalid_dim_raises(self) -> None: ds, ds_to_append, _ = create_append_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) with pytest.raises( ValueError, match="does not match any existing dataset dimensions" ): ds_to_append.to_zarr( store_target, append_dim="notvalid", **self.version_kwargs ) def test_append_with_no_dims_raises(self) -> None: with self.create_zarr_target() as store_target: Dataset({"foo": ("x", [1])}).to_zarr( store_target, mode="w", **self.version_kwargs ) with pytest.raises(ValueError, match="different dimension names"): Dataset({"foo": ("y", [2])}).to_zarr( store_target, mode="a", **self.version_kwargs ) def test_append_with_append_dim_not_set_raises(self) -> None: ds, ds_to_append, _ = create_append_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) with pytest.raises(ValueError, match="different dimension sizes"): ds_to_append.to_zarr(store_target, mode="a", **self.version_kwargs) def test_append_with_mode_not_a_raises(self) -> None: ds, ds_to_append, _ = create_append_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) with pytest.raises(ValueError, match="cannot set append_dim unless"): ds_to_append.to_zarr( store_target, mode="w", append_dim="time", **self.version_kwargs ) def test_append_with_existing_encoding_raises(self) -> None: ds, ds_to_append, _ = create_append_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) with pytest.raises(ValueError, match="but encoding was provided"): ds_to_append.to_zarr( store_target, append_dim="time", encoding={"da": {"compressor": None}}, **self.version_kwargs, ) @pytest.mark.parametrize("dtype", ["U", "S"]) def test_append_string_length_mismatch_raises(self, dtype) -> None: if has_zarr_v3 and not has_zarr_v3_dtypes: skip_if_zarr_format_3("This actually works fine with Zarr format 3") ds, ds_to_append = create_append_string_length_mismatch_test_data(dtype) with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) with pytest.raises(ValueError, match="Mismatched dtypes for variable"): ds_to_append.to_zarr( store_target, append_dim="time", **self.version_kwargs ) @pytest.mark.parametrize("dtype", ["U", "S"]) def test_append_string_length_mismatch_works(self, dtype) -> None: skip_if_zarr_format_2("This doesn't work with Zarr format 2") # ...but it probably would if we used object dtype if has_zarr_v3_dtypes: pytest.skip("This works on pre ZDtype Zarr-Python, but fails after.") ds, ds_to_append = create_append_string_length_mismatch_test_data(dtype) expected = xr.concat([ds, ds_to_append], dim="time") with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) ds_to_append.to_zarr(store_target, append_dim="time", **self.version_kwargs) actual = xr.open_dataset(store_target, engine="zarr") xr.testing.assert_identical(expected, actual) def test_check_encoding_is_consistent_after_append(self) -> None: ds, ds_to_append, _ = create_append_test_data() # check encoding consistency with self.create_zarr_target() as store_target: import numcodecs encoding_value: Any if has_zarr_v3 and zarr.config.config["default_zarr_format"] == 3: compressor = zarr.codecs.BloscCodec() else: compressor = numcodecs.Blosc() encoding_key = "compressors" if has_zarr_v3 else "compressor" encoding_value = (compressor,) if has_zarr_v3 else compressor encoding = {"da": {encoding_key: encoding_value}} ds.to_zarr(store_target, mode="w", encoding=encoding, **self.version_kwargs) original_ds = xr.open_dataset( store_target, engine="zarr", **self.version_kwargs ) original_encoding = original_ds["da"].encoding[encoding_key] ds_to_append.to_zarr(store_target, append_dim="time", **self.version_kwargs) actual_ds = xr.open_dataset( store_target, engine="zarr", **self.version_kwargs ) actual_encoding = actual_ds["da"].encoding[encoding_key] assert original_encoding == actual_encoding assert_identical( xr.open_dataset( store_target, engine="zarr", **self.version_kwargs ).compute(), xr.concat([ds, ds_to_append], dim="time"), ) def test_append_with_new_variable(self) -> None: ds, ds_to_append, ds_with_new_var = create_append_test_data() # check append mode for new variable with self.create_zarr_target() as store_target: combined = xr.concat([ds, ds_to_append], dim="time") combined.to_zarr(store_target, mode="w", **self.version_kwargs) assert_identical( combined, xr.open_dataset(store_target, engine="zarr", **self.version_kwargs), ) ds_with_new_var.to_zarr(store_target, mode="a", **self.version_kwargs) combined = xr.concat([ds, ds_to_append], dim="time") combined["new_var"] = ds_with_new_var["new_var"] assert_identical( combined, xr.open_dataset(store_target, engine="zarr", **self.version_kwargs), ) def test_append_with_append_dim_no_overwrite(self) -> None: ds, ds_to_append, _ = create_append_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) original = xr.concat([ds, ds_to_append], dim="time") original2 = xr.concat([original, ds_to_append], dim="time") # overwrite a coordinate; # for mode='a-', this will not get written to the store # because it does not have the append_dim as a dim lon = ds_to_append.lon.to_numpy().copy() lon[:] = -999 ds_to_append["lon"] = lon ds_to_append.to_zarr( store_target, mode="a-", append_dim="time", **self.version_kwargs ) actual = xr.open_dataset(store_target, engine="zarr", **self.version_kwargs) assert_identical(original, actual) # by default, mode="a" will overwrite all coordinates. ds_to_append.to_zarr(store_target, append_dim="time", **self.version_kwargs) actual = xr.open_dataset(store_target, engine="zarr", **self.version_kwargs) lon = original2.lon.to_numpy().copy() lon[:] = -999 original2["lon"] = lon assert_identical(original2, actual) @requires_dask def test_to_zarr_compute_false_roundtrip(self) -> None: from dask.delayed import Delayed original = create_test_data().chunk() with self.create_zarr_target() as store: delayed_obj = self.save(original, store, compute=False) assert isinstance(delayed_obj, Delayed) # make sure target store has not been written to yet with pytest.raises(AssertionError): with self.open(store) as actual: assert_identical(original, actual) delayed_obj.compute() with self.open(store) as actual: assert_identical(original, actual) @requires_dask def test_to_zarr_append_compute_false_roundtrip(self) -> None: from dask.delayed import Delayed ds, ds_to_append, _ = create_append_test_data() ds, ds_to_append = ds.chunk(), ds_to_append.chunk() with pytest.warns(SerializationWarning): with self.create_zarr_target() as store: delayed_obj = self.save(ds, store, compute=False, mode="w") assert isinstance(delayed_obj, Delayed) with pytest.raises(AssertionError): with self.open(store) as actual: assert_identical(ds, actual) delayed_obj.compute() with self.open(store) as actual: assert_identical(ds, actual) delayed_obj = self.save( ds_to_append, store, compute=False, append_dim="time" ) assert isinstance(delayed_obj, Delayed) with pytest.raises(AssertionError): with self.open(store) as actual: assert_identical( xr.concat([ds, ds_to_append], dim="time"), actual ) delayed_obj.compute() with self.open(store) as actual: assert_identical(xr.concat([ds, ds_to_append], dim="time"), actual) @pytest.mark.parametrize("chunk", [False, True]) def test_save_emptydim(self, chunk) -> None: if chunk and not has_dask: pytest.skip("requires dask") ds = Dataset({"x": (("a", "b"), np.empty((5, 0))), "y": ("a", [1, 2, 5, 8, 9])}) if chunk: ds = ds.chunk({}) # chunk dataset to save dask array with self.roundtrip(ds) as ds_reload: assert_identical(ds, ds_reload) @requires_dask def test_no_warning_from_open_emptydim_with_chunks(self) -> None: ds = Dataset({"x": (("a", "b"), np.empty((5, 0)))}).chunk({"a": 1}) with assert_no_warnings(): with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message=".*Zarr format 3 specification.*", category=UserWarning, ) with self.roundtrip(ds, open_kwargs=dict(chunks={"a": 1})) as ds_reload: assert_identical(ds, ds_reload) @pytest.mark.parametrize("consolidated", [False, True, None]) @pytest.mark.parametrize("compute", [False, True]) @pytest.mark.parametrize("use_dask", [False, True]) @pytest.mark.parametrize("write_empty", [False, True, None]) def test_write_region(self, consolidated, compute, use_dask, write_empty) -> None: if (use_dask or not compute) and not has_dask: pytest.skip("requires dask") zeros = Dataset({"u": (("x",), np.zeros(10))}) nonzeros = Dataset({"u": (("x",), np.arange(1, 11))}) if use_dask: zeros = zeros.chunk(2) nonzeros = nonzeros.chunk(2) with self.create_zarr_target() as store: zeros.to_zarr( store, consolidated=consolidated, compute=compute, encoding={"u": dict(chunks=2)}, **self.version_kwargs, ) if compute: with xr.open_zarr( store, consolidated=consolidated, **self.version_kwargs ) as actual: assert_identical(actual, zeros) for i in range(0, 10, 2): region = {"x": slice(i, i + 2)} nonzeros.isel(region).to_zarr( store, region=region, consolidated=consolidated, write_empty_chunks=write_empty, **self.version_kwargs, ) with xr.open_zarr( store, consolidated=consolidated, **self.version_kwargs ) as actual: assert_identical(actual, nonzeros) @pytest.mark.parametrize("mode", [None, "r+", "a"]) def test_write_region_mode(self, mode) -> None: zeros = Dataset({"u": (("x",), np.zeros(10))}) nonzeros = Dataset({"u": (("x",), np.arange(1, 11))}) with self.create_zarr_target() as store: zeros.to_zarr(store, **self.version_kwargs) for region in [{"x": slice(5)}, {"x": slice(5, 10)}]: nonzeros.isel(region).to_zarr( store, region=region, mode=mode, **self.version_kwargs ) with xr.open_zarr(store, **self.version_kwargs) as actual: assert_identical(actual, nonzeros) @requires_dask def test_write_preexisting_override_metadata(self) -> None: """Metadata should be overridden if mode="a" but not in mode="r+".""" original = Dataset( {"u": (("x",), np.zeros(10), {"variable": "original"})}, attrs={"global": "original"}, ) both_modified = Dataset( {"u": (("x",), np.ones(10), {"variable": "modified"})}, attrs={"global": "modified"}, ) global_modified = Dataset( {"u": (("x",), np.ones(10), {"variable": "original"})}, attrs={"global": "modified"}, ) only_new_data = Dataset( {"u": (("x",), np.ones(10), {"variable": "original"})}, attrs={"global": "original"}, ) with self.create_zarr_target() as store: original.to_zarr(store, compute=False, **self.version_kwargs) both_modified.to_zarr(store, mode="a", **self.version_kwargs) with self.open(store) as actual: # NOTE: this arguably incorrect -- we should probably be # overriding the variable metadata, too. See the TODO note in # ZarrStore.set_variables. assert_identical(actual, global_modified) with self.create_zarr_target() as store: original.to_zarr(store, compute=False, **self.version_kwargs) both_modified.to_zarr(store, mode="r+", **self.version_kwargs) with self.open(store) as actual: assert_identical(actual, only_new_data) with self.create_zarr_target() as store: original.to_zarr(store, compute=False, **self.version_kwargs) # with region, the default mode becomes r+ both_modified.to_zarr( store, region={"x": slice(None)}, **self.version_kwargs ) with self.open(store) as actual: assert_identical(actual, only_new_data) def test_write_region_errors(self) -> None: data = Dataset({"u": (("x",), np.arange(5))}) data2 = Dataset({"u": (("x",), np.array([10, 11]))}) @contextlib.contextmanager def setup_and_verify_store(expected=data): with self.create_zarr_target() as store: data.to_zarr(store, **self.version_kwargs) yield store with self.open(store) as actual: assert_identical(actual, expected) # verify the base case works expected = Dataset({"u": (("x",), np.array([10, 11, 2, 3, 4]))}) with setup_and_verify_store(expected) as store: data2.to_zarr(store, region={"x": slice(2)}, **self.version_kwargs) with setup_and_verify_store() as store: with pytest.raises( ValueError, match=re.escape( "cannot set region unless mode='a', mode='a-', mode='r+' or mode=None" ), ): data.to_zarr( store, region={"x": slice(None)}, mode="w", **self.version_kwargs ) with setup_and_verify_store() as store: with pytest.raises(TypeError, match=r"must be a dict"): data.to_zarr(store, region=slice(None), **self.version_kwargs) # type: ignore[call-overload] with setup_and_verify_store() as store: with pytest.raises(TypeError, match=r"must be slice objects"): data2.to_zarr(store, region={"x": [0, 1]}, **self.version_kwargs) # type: ignore[dict-item] with setup_and_verify_store() as store: with pytest.raises(ValueError, match=r"step on all slices"): data2.to_zarr( store, region={"x": slice(None, None, 2)}, **self.version_kwargs ) with setup_and_verify_store() as store: with pytest.raises( ValueError, match=r"all keys in ``region`` are not in Dataset dimensions", ): data.to_zarr(store, region={"y": slice(None)}, **self.version_kwargs) with setup_and_verify_store() as store: with pytest.raises( ValueError, match=r"all variables in the dataset to write must have at least one dimension in common", ): data2.assign(v=2).to_zarr( store, region={"x": slice(2)}, **self.version_kwargs ) with setup_and_verify_store() as store: with pytest.raises( ValueError, match=r"cannot list the same dimension in both" ): data.to_zarr( store, region={"x": slice(None)}, append_dim="x", **self.version_kwargs, ) with setup_and_verify_store() as store: with pytest.raises( ValueError, match=r"variable 'u' already exists with different dimension sizes", ): data2.to_zarr(store, region={"x": slice(3)}, **self.version_kwargs) @requires_dask def test_encoding_chunksizes(self) -> None: # regression test for GH2278 # see also test_encoding_chunksizes_unlimited nx, ny, nt = 4, 4, 5 original = xr.Dataset( {}, coords={ "x": np.arange(nx), "y": np.arange(ny), "t": np.arange(nt), }, ) original["v"] = xr.Variable(("x", "y", "t"), np.zeros((nx, ny, nt))) original = original.chunk({"t": 1, "x": 2, "y": 2}) with self.roundtrip(original) as ds1: assert_equal(ds1, original) with self.roundtrip(ds1.isel(t=0)) as ds2: assert_equal(ds2, original.isel(t=0)) @requires_dask def test_chunk_encoding_with_partial_dask_chunks(self) -> None: original = xr.Dataset( {"x": xr.DataArray(np.random.random(size=(6, 8)), dims=("a", "b"))} ).chunk({"a": 3}) with self.roundtrip( original, save_kwargs={"encoding": {"x": {"chunks": [3, 2]}}} ) as ds1: assert_equal(ds1, original) @requires_dask def test_chunk_encoding_with_larger_dask_chunks(self) -> None: original = xr.Dataset({"a": ("x", [1, 2, 3, 4])}).chunk({"x": 2}) with self.roundtrip( original, save_kwargs={"encoding": {"a": {"chunks": [1]}}} ) as ds1: assert_equal(ds1, original) @requires_cftime def test_open_zarr_use_cftime(self) -> None: ds = create_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, **self.version_kwargs) ds_a = xr.open_zarr(store_target, **self.version_kwargs) assert_identical(ds, ds_a) decoder = CFDatetimeCoder(use_cftime=True) ds_b = xr.open_zarr( store_target, decode_times=decoder, **self.version_kwargs ) assert xr.coding.times.contains_cftime_datetimes(ds_b.time.variable) def test_write_read_select_write(self) -> None: # Test for https://github.com/pydata/xarray/issues/4084 ds = create_test_data() # NOTE: using self.roundtrip, which uses open_dataset, will not trigger the bug. with self.create_zarr_target() as initial_store: ds.to_zarr(initial_store, mode="w", **self.version_kwargs) ds1 = xr.open_zarr(initial_store, **self.version_kwargs) # Combination of where+squeeze triggers error on write. ds_sel = ds1.where(ds1.coords["dim3"] == "a", drop=True).squeeze("dim3") with self.create_zarr_target() as final_store: ds_sel.to_zarr(final_store, mode="w", **self.version_kwargs) @pytest.mark.parametrize("obj", [Dataset(), DataArray(name="foo")]) def test_attributes(self, obj) -> None: obj = obj.copy() obj.attrs["good"] = {"key": "value"} ds = obj if isinstance(obj, Dataset) else obj.to_dataset() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, **self.version_kwargs) assert_identical(ds, xr.open_zarr(store_target, **self.version_kwargs)) obj.attrs["bad"] = DataArray() ds = obj if isinstance(obj, Dataset) else obj.to_dataset() with self.create_zarr_target() as store_target: with pytest.raises(TypeError, match=r"Invalid attribute in Dataset.attrs."): ds.to_zarr(store_target, **self.version_kwargs) @requires_dask @pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"]) def test_chunked_datetime64_or_timedelta64(self, dtype) -> None: # Generalized from @malmans2's test in PR #8253 original = create_test_data().astype(dtype).chunk(1) with self.roundtrip( original, open_kwargs={ "chunks": {}, "decode_timedelta": CFTimedeltaCoder(time_unit="ns"), }, ) as actual: for name, actual_var in actual.variables.items(): assert original[name].chunks == actual_var.chunks assert original.chunks == actual.chunks @requires_cftime @requires_dask def test_chunked_cftime_datetime(self) -> None: # Based on @malmans2's test in PR #8253 times = date_range("2000", freq="D", periods=3, use_cftime=True) original = xr.Dataset(data_vars={"chunked_times": (["time"], times)}) original = original.chunk({"time": 1}) with self.roundtrip(original, open_kwargs={"chunks": {}}) as actual: for name, actual_var in actual.variables.items(): assert original[name].chunks == actual_var.chunks assert original.chunks == actual.chunks def test_cache_members(self) -> None: """ Ensure that if `ZarrStore` is created with `cache_members` set to `True`, a `ZarrStore` only inspects the underlying zarr group once, and that the results of that inspection are cached. Otherwise, `ZarrStore.members` should inspect the underlying zarr group each time it is invoked """ with self.create_zarr_target() as store_target: zstore_mut = backends.ZarrStore.open_group( store_target, mode="w", cache_members=False ) # ensure that the keys are sorted array_keys = sorted(("foo", "bar")) # create some arrays for ak in array_keys: zstore_mut.zarr_group.create(name=ak, shape=(1,), dtype="uint8") zstore_stat = backends.ZarrStore.open_group( store_target, mode="r", cache_members=True ) observed_keys_0 = sorted(zstore_stat.array_keys()) assert observed_keys_0 == array_keys # create a new array new_key = "baz" zstore_mut.zarr_group.create(name=new_key, shape=(1,), dtype="uint8") observed_keys_1 = sorted(zstore_stat.array_keys()) assert observed_keys_1 == array_keys observed_keys_2 = sorted(zstore_mut.array_keys()) assert observed_keys_2 == sorted(array_keys + [new_key]) @requires_dask @pytest.mark.parametrize("dtype", [int, float]) def test_zarr_fill_value_setting(self, dtype): # When zarr_format=2, _FillValue sets fill_value # When zarr_format=3, fill_value is set independently # We test this by writing a dask array with compute=False, # on read we should receive chunks filled with `fill_value` fv = -1 ds = xr.Dataset( {"foo": ("x", dask.array.from_array(np.array([0, 0, 0], dtype=dtype)))} ) expected = xr.Dataset({"foo": ("x", [fv] * 3)}) zarr_format_2 = ( has_zarr_v3 and zarr.config.get("default_zarr_format") == 2 ) or not has_zarr_v3 if zarr_format_2: attr = "_FillValue" expected.foo.attrs[attr] = fv else: attr = "fill_value" if dtype is float: # for floats, Xarray inserts a default `np.nan` expected.foo.attrs["_FillValue"] = np.nan # turn off all decoding so we see what Zarr returns to us. # Since chunks, are not written, we should receive on `fill_value` open_kwargs = { "mask_and_scale": False, "consolidated": False, "use_zarr_fill_value_as_mask": False, } save_kwargs = dict(compute=False, consolidated=False) with self.roundtrip( ds, save_kwargs=ChainMap(save_kwargs, dict(encoding={"foo": {attr: fv}})), open_kwargs=open_kwargs, ) as actual: assert_identical(actual, expected) ds.foo.encoding[attr] = fv with self.roundtrip( ds, save_kwargs=save_kwargs, open_kwargs=open_kwargs ) as actual: assert_identical(actual, expected) if zarr_format_2: ds = ds.drop_encoding() with pytest.raises(ValueError, match="_FillValue"): with self.roundtrip( ds, save_kwargs=ChainMap( save_kwargs, dict(encoding={"foo": {"fill_value": fv}}) ), open_kwargs=open_kwargs, ): pass # TODO: this doesn't fail because of the # ``raise_on_invalid=vn in check_encoding_set`` line in zarr.py # ds.foo.encoding["fill_value"] = fv @requires_zarr @pytest.mark.skipif( KVStore is None, reason="zarr-python 2.x or ZARR_V3_EXPERIMENTAL_API is unset." ) class TestInstrumentedZarrStore: if has_zarr_v3: methods = [ "get", "set", "list_dir", "list_prefix", ] else: methods = [ "__iter__", "__contains__", "__setitem__", "__getitem__", "listdir", "list_prefix", ] @contextlib.contextmanager def create_zarr_target(self): if Version(zarr.__version__) < Version("2.18.0"): pytest.skip("Instrumented tests only work on latest Zarr.") if has_zarr_v3: kwargs = {"read_only": False} else: kwargs = {} # type: ignore[arg-type,unused-ignore] store = KVStore({}, **kwargs) # type: ignore[arg-type,unused-ignore] yield store def make_patches(self, store): from unittest.mock import MagicMock return { method: MagicMock( f"KVStore.{method}", side_effect=getattr(store, method), autospec=True, ) for method in self.methods } def summarize(self, patches): summary = {} for name, patch_ in patches.items(): count = 0 for call in patch_.mock_calls: if "zarr.json" not in call.args: count += 1 summary[name.strip("_")] = count return summary def check_requests(self, expected, patches): summary = self.summarize(patches) for k in summary: assert summary[k] <= expected[k], (k, summary) def test_append(self) -> None: original = Dataset({"foo": ("x", [1])}, coords={"x": [0]}) modified = Dataset({"foo": ("x", [2])}, coords={"x": [1]}) with self.create_zarr_target() as store: if has_zarr_v3: # TODO: verify these expected = { "set": 5, "get": 4, "list_dir": 2, "list_prefix": 1, } else: expected = { "iter": 1, "contains": 18, "setitem": 10, "getitem": 13, "listdir": 0, "list_prefix": 3, } patches = self.make_patches(store) with patch.multiple(KVStore, **patches): original.to_zarr(store) self.check_requests(expected, patches) patches = self.make_patches(store) # v2024.03.0: {'iter': 6, 'contains': 2, 'setitem': 5, 'getitem': 10, 'listdir': 6, 'list_prefix': 0} # 6057128b: {'iter': 5, 'contains': 2, 'setitem': 5, 'getitem': 10, "listdir": 5, "list_prefix": 0} if has_zarr_v3: expected = { "set": 4, "get": 9, # TODO: fixme upstream (should be 8) "list_dir": 2, # TODO: fixme upstream (should be 2) "list_prefix": 0, } else: expected = { "iter": 1, "contains": 11, "setitem": 6, "getitem": 15, "listdir": 0, "list_prefix": 1, } with patch.multiple(KVStore, **patches): modified.to_zarr(store, mode="a", append_dim="x") self.check_requests(expected, patches) patches = self.make_patches(store) if has_zarr_v3: expected = { "set": 4, "get": 9, # TODO: fixme upstream (should be 8) "list_dir": 2, # TODO: fixme upstream (should be 2) "list_prefix": 0, } else: expected = { "iter": 1, "contains": 11, "setitem": 6, "getitem": 15, "listdir": 0, "list_prefix": 1, } with patch.multiple(KVStore, **patches): modified.to_zarr(store, mode="a-", append_dim="x") self.check_requests(expected, patches) with open_dataset(store, engine="zarr") as actual: assert_identical( actual, xr.concat([original, modified, modified], dim="x") ) @requires_dask def test_region_write(self) -> None: ds = Dataset({"foo": ("x", [1, 2, 3])}, coords={"x": [1, 2, 3]}).chunk() with self.create_zarr_target() as store: if has_zarr_v3: expected = { "set": 5, "get": 2, "list_dir": 2, "list_prefix": 4, } else: expected = { "iter": 1, "contains": 16, "setitem": 9, "getitem": 13, "listdir": 0, "list_prefix": 5, } patches = self.make_patches(store) with patch.multiple(KVStore, **patches): ds.to_zarr(store, mode="w", compute=False) self.check_requests(expected, patches) # v2024.03.0: {'iter': 5, 'contains': 2, 'setitem': 1, 'getitem': 6, 'listdir': 5, 'list_prefix': 0} # 6057128b: {'iter': 4, 'contains': 2, 'setitem': 1, 'getitem': 5, 'listdir': 4, 'list_prefix': 0} if has_zarr_v3: expected = { "set": 1, "get": 3, "list_dir": 0, "list_prefix": 0, } else: expected = { "iter": 1, "contains": 6, "setitem": 1, "getitem": 7, "listdir": 0, "list_prefix": 0, } patches = self.make_patches(store) with patch.multiple(KVStore, **patches): ds.to_zarr(store, region={"x": slice(None)}) self.check_requests(expected, patches) # v2024.03.0: {'iter': 6, 'contains': 4, 'setitem': 1, 'getitem': 11, 'listdir': 6, 'list_prefix': 0} # 6057128b: {'iter': 4, 'contains': 2, 'setitem': 1, 'getitem': 7, 'listdir': 4, 'list_prefix': 0} if has_zarr_v3: expected = { "set": 1, "get": 4, "list_dir": 0, "list_prefix": 0, } else: expected = { "iter": 1, "contains": 6, "setitem": 1, "getitem": 8, "listdir": 0, "list_prefix": 0, } patches = self.make_patches(store) with patch.multiple(KVStore, **patches): ds.to_zarr(store, region="auto") self.check_requests(expected, patches) if has_zarr_v3: expected = { "set": 0, "get": 5, "list_dir": 0, "list_prefix": 0, } else: expected = { "iter": 1, "contains": 6, "setitem": 0, "getitem": 8, "listdir": 0, "list_prefix": 0, } patches = self.make_patches(store) with patch.multiple(KVStore, **patches): with open_dataset(store, engine="zarr") as actual: assert_identical(actual, ds) self.check_requests(expected, patches) @requires_zarr class TestZarrDictStore(ZarrBase): @contextlib.contextmanager def create_zarr_target(self): if has_zarr_v3: yield zarr.storage.MemoryStore({}, read_only=False) else: yield {} def test_chunk_key_encoding_v2(self) -> None: encoding = {"name": "v2", "configuration": {"separator": "/"}} # Create a dataset with a variable name containing a period data = np.ones((4, 4)) original = Dataset({"var1": (("x", "y"), data)}) # Set up chunk key encoding with slash separator encoding = { "var1": { "chunk_key_encoding": encoding, "chunks": (2, 2), } } # Write to store with custom encoding with self.create_zarr_target() as store: original.to_zarr(store, encoding=encoding) # Verify the chunk keys in store use the slash separator if not has_zarr_v3: chunk_keys = [k for k in store.keys() if k.startswith("var1/")] assert len(chunk_keys) > 0 for key in chunk_keys: assert "/" in key assert "." not in key.split("/")[1:] # No dots in chunk coordinates # Read back and verify data with xr.open_zarr(store) as actual: assert_identical(original, actual) # Verify chunks are preserved assert actual["var1"].encoding["chunks"] == (2, 2) @pytest.mark.asyncio @requires_zarr_v3 async def test_async_load_multiple_variables(self) -> None: target_class = zarr.AsyncArray method_name = "getitem" original_method = getattr(target_class, method_name) # the indexed coordinate variables is not lazy, so the create_test_dataset has 4 lazy variables in total N_LAZY_VARS = 4 original = create_test_data() with self.create_zarr_target() as store: original.to_zarr(store, zarr_format=3, consolidated=False) with patch.object( target_class, method_name, wraps=original_method, autospec=True ) as mocked_meth: # blocks upon loading the coordinate variables here ds = xr.open_zarr(store, consolidated=False, chunks=None) # TODO we're not actually testing that these indexing methods are not blocking... result_ds = await ds.load_async() mocked_meth.assert_called() assert mocked_meth.call_count == N_LAZY_VARS mocked_meth.assert_awaited() xrt.assert_identical(result_ds, ds.load()) @pytest.mark.asyncio @requires_zarr_v3 @pytest.mark.parametrize("cls_name", ["Variable", "DataArray", "Dataset"]) async def test_concurrent_load_multiple_objects( self, cls_name, ) -> None: N_OBJECTS = 5 N_LAZY_VARS = { "Variable": 1, "DataArray": 1, "Dataset": 4, } # specific to the create_test_data() used target_class = zarr.AsyncArray method_name = "getitem" original_method = getattr(target_class, method_name) original = create_test_data() with self.create_zarr_target() as store: original.to_zarr(store, consolidated=False, zarr_format=3) with patch.object( target_class, method_name, wraps=original_method, autospec=True ) as mocked_meth: xr_obj = get_xr_obj(store, cls_name) # TODO we're not actually testing that these indexing methods are not blocking... coros = [xr_obj.load_async() for _ in range(N_OBJECTS)] results = await asyncio.gather(*coros) mocked_meth.assert_called() assert mocked_meth.call_count == N_OBJECTS * N_LAZY_VARS[cls_name] mocked_meth.assert_awaited() for result in results: xrt.assert_identical(result, xr_obj.load()) @pytest.mark.asyncio @requires_zarr_v3 @pytest.mark.parametrize("cls_name", ["Variable", "DataArray", "Dataset"]) @pytest.mark.parametrize( "indexer, method, target_zarr_class", [ pytest.param({}, "sel", "zarr.AsyncArray", id="no-indexing-sel"), pytest.param({}, "isel", "zarr.AsyncArray", id="no-indexing-isel"), pytest.param({"dim2": 1.0}, "sel", "zarr.AsyncArray", id="basic-int-sel"), pytest.param({"dim2": 2}, "isel", "zarr.AsyncArray", id="basic-int-isel"), pytest.param( {"dim2": slice(1.0, 3.0)}, "sel", "zarr.AsyncArray", id="basic-slice-sel", ), pytest.param( {"dim2": slice(1, 3)}, "isel", "zarr.AsyncArray", id="basic-slice-isel" ), pytest.param( {"dim2": [1.0, 3.0]}, "sel", "zarr.core.indexing.AsyncOIndex", id="outer-sel", ), pytest.param( {"dim2": [1, 3]}, "isel", "zarr.core.indexing.AsyncOIndex", id="outer-isel", ), pytest.param( { "dim1": xr.Variable(data=[2, 3], dims="points"), "dim2": xr.Variable(data=[1.0, 2.0], dims="points"), }, "sel", "zarr.core.indexing.AsyncVIndex", id="vectorized-sel", ), pytest.param( { "dim1": xr.Variable(data=[2, 3], dims="points"), "dim2": xr.Variable(data=[1, 3], dims="points"), }, "isel", "zarr.core.indexing.AsyncVIndex", id="vectorized-isel", ), ], ) async def test_indexing( self, cls_name, method, indexer, target_zarr_class, ) -> None: if not has_zarr_v3_async_oindex and target_zarr_class in ( "zarr.core.indexing.AsyncOIndex", "zarr.core.indexing.AsyncVIndex", ): pytest.skip( "current version of zarr does not support orthogonal or vectorized async indexing" ) if cls_name == "Variable" and method == "sel": pytest.skip("Variable doesn't have a .sel method") # Each type of indexing ends up calling a different zarr indexing method # They all use a method named .getitem, but on a different internal zarr class def _resolve_class_from_string(class_path: str) -> type[Any]: """Resolve a string class path like 'zarr.AsyncArray' to the actual class.""" module_path, class_name = class_path.rsplit(".", 1) module = import_module(module_path) return getattr(module, class_name) target_class = _resolve_class_from_string(target_zarr_class) method_name = "getitem" original_method = getattr(target_class, method_name) original = create_test_data() with self.create_zarr_target() as store: original.to_zarr(store, consolidated=False, zarr_format=3) with patch.object( target_class, method_name, wraps=original_method, autospec=True ) as mocked_meth: xr_obj = get_xr_obj(store, cls_name) # TODO we're not actually testing that these indexing methods are not blocking... result = await getattr(xr_obj, method)(**indexer).load_async() mocked_meth.assert_called() mocked_meth.assert_awaited() assert mocked_meth.call_count > 0 expected = getattr(xr_obj, method)(**indexer).load() xrt.assert_identical(result, expected) @pytest.mark.asyncio @pytest.mark.parametrize( ("indexer", "expected_err_msg"), [ pytest.param( {"dim2": 2}, "basic async indexing", marks=pytest.mark.skipif( has_zarr_v3, reason="current version of zarr has basic async indexing", ), ), # tests basic indexing pytest.param( {"dim2": [1, 3]}, "orthogonal async indexing", marks=pytest.mark.skipif( has_zarr_v3_async_oindex, reason="current version of zarr has async orthogonal indexing", ), ), # tests oindexing pytest.param( { "dim1": xr.Variable(data=[2, 3], dims="points"), "dim2": xr.Variable(data=[1, 3], dims="points"), }, "vectorized async indexing", marks=pytest.mark.skipif( has_zarr_v3_async_oindex, reason="current version of zarr has async vectorized indexing", ), ), # tests vindexing ], ) @parametrize_zarr_format async def test_raise_on_older_zarr_version( self, indexer, expected_err_msg, zarr_format, ): """Test that trying to use async load with insufficiently new version of zarr raises a clear error""" original = create_test_data() with self.create_zarr_target() as store: original.to_zarr(store, consolidated=False, zarr_format=zarr_format) ds = xr.open_zarr(store, consolidated=False, chunks=None) var = ds["var1"].variable with pytest.raises(NotImplementedError, match=expected_err_msg): await var.isel(**indexer).load_async() def get_xr_obj( store: zarr.abc.store.Store, cls_name: Literal["Variable", "DataArray", "Dataset"] ): ds = xr.open_zarr(store, consolidated=False, chunks=None) match cls_name: case "Variable": return ds["var1"].variable case "DataArray": return ds["var1"] case "Dataset": return ds class NoConsolidatedMetadataSupportStore(WrapperStore): """ Store that explicitly does not support consolidated metadata. Useful as a proxy for stores like Icechunk, see https://github.com/zarr-developers/zarr-python/pull/3119. """ supports_consolidated_metadata = False def __init__( self, store, *, read_only: bool = False, ) -> None: self._store = store.with_read_only(read_only=read_only) def with_read_only( self, read_only: bool = False ) -> NoConsolidatedMetadataSupportStore: return type(self)( store=self._store, read_only=read_only, ) @requires_zarr_v3 class TestZarrNoConsolidatedMetadataSupport(ZarrBase): @contextlib.contextmanager def create_zarr_target(self): # TODO the zarr version would need to be >3.08 for the supports_consolidated_metadata property to have any effect yield NoConsolidatedMetadataSupportStore( zarr.storage.MemoryStore({}, read_only=False) ) @requires_zarr @pytest.mark.skipif( ON_WINDOWS, reason="Very flaky on Windows CI. Can re-enable assuming it starts consistently passing.", ) class TestZarrDirectoryStore(ZarrBase): @contextlib.contextmanager def create_zarr_target(self): with create_tmp_file(suffix=".zarr") as tmp: yield tmp @requires_zarr class TestZarrWriteEmpty(TestZarrDirectoryStore): @contextlib.contextmanager def temp_dir(self) -> Iterator[tuple[str, str]]: with tempfile.TemporaryDirectory() as d: store = os.path.join(d, "test.zarr") yield d, store @contextlib.contextmanager def roundtrip_dir( self, data, store, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False, ) -> Iterator[Dataset]: if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} data.to_zarr(store, **save_kwargs, **self.version_kwargs) with xr.open_dataset( store, engine="zarr", **open_kwargs, **self.version_kwargs ) as ds: yield ds @pytest.mark.parametrize("consolidated", [True, False, None]) @pytest.mark.parametrize("write_empty", [True, False, None]) def test_write_empty( self, consolidated: bool | None, write_empty: bool | None, ) -> None: def assert_expected_files(expected: list[str], store: str) -> None: """Convenience for comparing with actual files written""" ls = [] test_root = os.path.join(store, "test") for root, _, files in os.walk(test_root): ls.extend( [ os.path.join(root, f).removeprefix(test_root).lstrip("/") for f in files ] ) assert set(expected) == { file.lstrip("c/") for file in ls if (file not in (".zattrs", ".zarray", "zarr.json")) } # The zarr format is set by the `default_zarr_format` # pytest fixture that acts on a superclass zarr_format_3 = has_zarr_v3 and zarr.config.config["default_zarr_format"] == 3 if (write_empty is False) or (write_empty is None and has_zarr_v3): expected = ["0.1.0"] else: expected = [ "0.0.0", "0.0.1", "0.1.0", "0.1.1", ] if zarr_format_3: data = np.array([0.0, 0, 1.0, 0]).reshape((1, 2, 2)) # transform to the path style of zarr 3 # e.g. 0/0/1 expected = [e.replace(".", "/") for e in expected] else: # use nan for default fill_value behaviour data = np.array([np.nan, np.nan, 1.0, np.nan]).reshape((1, 2, 2)) ds = xr.Dataset(data_vars={"test": (("Z", "Y", "X"), data)}) if has_dask: ds["test"] = ds["test"].chunk(1) encoding = None else: encoding = {"test": {"chunks": (1, 1, 1)}} with self.temp_dir() as (d, store): ds.to_zarr( store, mode="w", encoding=encoding, write_empty_chunks=write_empty, ) # check expected files after a write assert_expected_files(expected, store) with self.roundtrip_dir( ds, store, save_kwargs={ "mode": "a", "append_dim": "Z", "write_empty_chunks": write_empty, }, ) as a_ds: expected_ds = xr.concat([ds, ds], dim="Z") assert_identical(a_ds, expected_ds.compute()) # add the new files we expect to be created by the append # that was performed by the roundtrip_dir if (write_empty is False) or (write_empty is None and has_zarr_v3): expected.append("1.1.0") elif not has_zarr_v3: # TODO: remove zarr3 if once zarr issue is fixed # https://github.com/zarr-developers/zarr-python/issues/2931 expected.extend( [ "1.1.0", "1.0.0", "1.0.1", "1.1.1", ] ) else: expected.append("1.1.0") if zarr_format_3: expected = [e.replace(".", "/") for e in expected] assert_expected_files(expected, store) def test_avoid_excess_metadata_calls(self) -> None: """Test that chunk requests do not trigger redundant metadata requests. This test targets logic in backends.zarr.ZarrArrayWrapper, asserting that calls to retrieve chunk data after initialization do not trigger additional metadata requests. https://github.com/pydata/xarray/issues/8290 """ ds = xr.Dataset(data_vars={"test": (("Z",), np.array([123]).reshape(1))}) # The call to retrieve metadata performs a group lookup. We patch Group.__getitem__ # so that we can inspect calls to this method - specifically count of calls. # Use of side_effect means that calls are passed through to the original method # rather than a mocked method. Group: Any if has_zarr_v3: Group = zarr.AsyncGroup patched = patch.object( Group, "getitem", side_effect=Group.getitem, autospec=True ) else: Group = zarr.Group patched = patch.object( Group, "__getitem__", side_effect=Group.__getitem__, autospec=True ) with self.create_zarr_target() as store, patched as mock: ds.to_zarr(store, mode="w") # We expect this to request array metadata information, so call_count should be == 1, xrds = xr.open_zarr(store) call_count = mock.call_count assert call_count == 1 # compute() requests array data, which should not trigger additional metadata requests # we assert that the number of calls has not increased after fetchhing the array xrds.test.compute(scheduler="sync") assert mock.call_count == call_count @requires_zarr @requires_fsspec @pytest.mark.skipif(has_zarr_v3, reason="Difficult to test.") def test_zarr_storage_options() -> None: pytest.importorskip("aiobotocore") ds = create_test_data() store_target = "memory://test.zarr" ds.to_zarr(store_target, storage_options={"test": "zarr_write"}) ds_a = xr.open_zarr(store_target, storage_options={"test": "zarr_read"}) assert_identical(ds, ds_a) @requires_zarr def test_zarr_version_deprecated() -> None: ds = create_test_data() store: Any if has_zarr_v3: store = KVStore() else: store = {} with pytest.warns(FutureWarning, match="zarr_version"): ds.to_zarr(store=store, zarr_version=2) with pytest.warns(FutureWarning, match="zarr_version"): xr.open_zarr(store=store, zarr_version=2) with pytest.raises(ValueError, match="zarr_format"): xr.open_zarr(store=store, zarr_version=2, zarr_format=3) @requires_scipy class TestScipyInMemoryData(NetCDF3Only, CFEncodedBase): engine: T_NetcdfEngine = "scipy" @contextlib.contextmanager def create_store(self): fobj = BytesIO() yield backends.ScipyDataStore(fobj, "w") @pytest.mark.asyncio @pytest.mark.skip(reason="NetCDF backends don't support async loading") async def test_load_async(self) -> None: await super().test_load_async() def test_to_netcdf_explicit_engine(self) -> None: with pytest.warns( FutureWarning, match=re.escape("return value of to_netcdf() without a target"), ): Dataset({"foo": 42}).to_netcdf(engine="scipy") def test_roundtrip_via_bytes(self) -> None: original = create_test_data() with pytest.warns( FutureWarning, match=re.escape("return value of to_netcdf() without a target"), ): netcdf_bytes = original.to_netcdf(engine="scipy") roundtrip = open_dataset(netcdf_bytes, engine="scipy") assert_identical(roundtrip, original) def test_bytes_pickle(self) -> None: data = Dataset({"foo": ("x", [1, 2, 3])}) with pytest.warns( FutureWarning, match=re.escape("return value of to_netcdf() without a target"), ): fobj = data.to_netcdf() with self.open(fobj) as ds: unpickled = pickle.loads(pickle.dumps(ds)) assert_identical(unpickled, data) @requires_scipy class TestScipyFileObject(NetCDF3Only, CFEncodedBase): # TODO: Consider consolidating some of these cases (e.g., # test_file_remains_open) with TestH5NetCDFFileObject engine: T_NetcdfEngine = "scipy" @contextlib.contextmanager def create_store(self): fobj = BytesIO() yield backends.ScipyDataStore(fobj, "w") @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} with create_tmp_file() as tmp_file: with open(tmp_file, "wb") as f: self.save(data, f, **save_kwargs) with open(tmp_file, "rb") as f: with self.open(f, **open_kwargs) as ds: yield ds @pytest.mark.xfail( reason="scipy.io.netcdf_file closes files upon garbage collection" ) def test_file_remains_open(self) -> None: data = Dataset({"foo": ("x", [1, 2, 3])}) f = BytesIO() data.to_netcdf(f, engine="scipy") assert not f.closed restored = open_dataset(f, engine="scipy") assert not f.closed assert_identical(restored, data) restored.close() assert not f.closed @pytest.mark.skip(reason="cannot pickle file objects") def test_pickle(self) -> None: pass @pytest.mark.skip(reason="cannot pickle file objects") def test_pickle_dataarray(self) -> None: pass @pytest.mark.parametrize("create_default_indexes", [True, False]) def test_create_default_indexes(self, tmp_path, create_default_indexes) -> None: store_path = tmp_path / "tmp.nc" original_ds = xr.Dataset( {"data": ("x", np.arange(3))}, coords={"x": [-1, 0, 1]} ) original_ds.to_netcdf(store_path, engine=self.engine, mode="w") with open_dataset( store_path, engine=self.engine, create_default_indexes=create_default_indexes, ) as loaded_ds: if create_default_indexes: assert list(loaded_ds.xindexes) == ["x"] and isinstance( loaded_ds.xindexes["x"], PandasIndex ) else: assert len(loaded_ds.xindexes) == 0 @requires_scipy class TestScipyFilePath(NetCDF3Only, CFEncodedBase): engine: T_NetcdfEngine = "scipy" @contextlib.contextmanager def create_store(self): with create_tmp_file() as tmp_file: with backends.ScipyDataStore(tmp_file, mode="w") as store: yield store def test_array_attrs(self) -> None: ds = Dataset(attrs={"foo": [[1, 2], [3, 4]]}) with pytest.raises(ValueError, match=r"must be 1-dimensional"): with self.roundtrip(ds): pass def test_roundtrip_example_1_netcdf_gz(self) -> None: with open_example_dataset("example_1.nc.gz") as expected: with open_example_dataset("example_1.nc") as actual: assert_identical(expected, actual) def test_netcdf3_endianness(self) -> None: # regression test for GH416 with open_example_dataset("bears.nc", engine="scipy") as expected: for var in expected.variables.values(): assert var.dtype.isnative @requires_netCDF4 def test_nc4_scipy(self) -> None: with create_tmp_file(allow_cleanup_failure=True) as tmp_file: with nc4.Dataset(tmp_file, "w", format="NETCDF4") as rootgrp: rootgrp.createGroup("foo") with pytest.raises(TypeError, match=r"pip install netcdf4"): open_dataset(tmp_file, engine="scipy") @requires_netCDF4 class TestNetCDF3ViaNetCDF4Data(NetCDF3Only, CFEncodedBase): engine: T_NetcdfEngine = "netcdf4" file_format: T_NetcdfTypes = "NETCDF3_CLASSIC" @contextlib.contextmanager def create_store(self): with create_tmp_file() as tmp_file: with backends.NetCDF4DataStore.open( tmp_file, mode="w", format="NETCDF3_CLASSIC" ) as store: yield store def test_encoding_kwarg_vlen_string(self) -> None: original = Dataset({"x": ["foo", "bar", "baz"]}) kwargs = dict(encoding={"x": {"dtype": str}}) with pytest.raises(ValueError, match=r"encoding dtype=str for vlen"): with self.roundtrip(original, save_kwargs=kwargs): pass @requires_netCDF4 class TestNetCDF4ClassicViaNetCDF4Data(NetCDF3Only, CFEncodedBase): engine: T_NetcdfEngine = "netcdf4" file_format: T_NetcdfTypes = "NETCDF4_CLASSIC" @contextlib.contextmanager def create_store(self): with create_tmp_file() as tmp_file: with backends.NetCDF4DataStore.open( tmp_file, mode="w", format="NETCDF4_CLASSIC" ) as store: yield store @requires_scipy_or_netCDF4 class TestGenericNetCDFData(NetCDF3Only, CFEncodedBase): # verify that we can read and write netCDF3 files as long as we have scipy # or netCDF4-python installed file_format: T_NetcdfTypes = "NETCDF3_64BIT" def test_write_store(self) -> None: # there's no specific store to test here pass @requires_scipy def test_engine(self) -> None: data = create_test_data() with pytest.raises(ValueError, match=r"unrecognized engine"): data.to_netcdf("foo.nc", engine="foobar") # type: ignore[call-overload] with pytest.raises(ValueError, match=r"invalid engine"): data.to_netcdf(engine="netcdf4") with create_tmp_file() as tmp_file: data.to_netcdf(tmp_file) with pytest.raises(ValueError, match=r"unrecognized engine"): open_dataset(tmp_file, engine="foobar") bytes_io = BytesIO() data.to_netcdf(bytes_io, engine="scipy") with pytest.raises(ValueError, match=r"unrecognized engine"): open_dataset(bytes_io, engine="foobar") def test_cross_engine_read_write_netcdf3(self) -> None: data = create_test_data() valid_engines: set[T_NetcdfEngine] = set() if has_netCDF4: valid_engines.add("netcdf4") if has_scipy: valid_engines.add("scipy") for write_engine in valid_engines: for format in self.netcdf3_formats: with create_tmp_file() as tmp_file: data.to_netcdf(tmp_file, format=format, engine=write_engine) for read_engine in valid_engines: with open_dataset(tmp_file, engine=read_engine) as actual: # hack to allow test to work: # coord comes back as DataArray rather than coord, # and so need to loop through here rather than in # the test function (or we get recursion) [ assert_allclose(data[k].variable, actual[k].variable) for k in data.variables ] def test_encoding_unlimited_dims(self) -> None: ds = Dataset({"x": ("y", np.arange(10.0))}) with self.roundtrip(ds, save_kwargs=dict(unlimited_dims=["y"])) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) # Regression test for https://github.com/pydata/xarray/issues/2134 with self.roundtrip(ds, save_kwargs=dict(unlimited_dims="y")) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) ds.encoding = {"unlimited_dims": ["y"]} with self.roundtrip(ds) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) # Regression test for https://github.com/pydata/xarray/issues/2134 ds.encoding = {"unlimited_dims": "y"} with self.roundtrip(ds) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) @requires_scipy def test_roundtrip_via_bytes(self) -> None: original = create_test_data() with pytest.warns( FutureWarning, match=re.escape("return value of to_netcdf() without a target"), ): netcdf_bytes = original.to_netcdf() roundtrip = open_dataset(netcdf_bytes) assert_identical(roundtrip, original) @pytest.mark.xfail( reason="scipy.io.netcdf_file closes files upon garbage collection" ) @requires_scipy def test_roundtrip_via_file_object(self) -> None: original = create_test_data() f = BytesIO() original.to_netcdf(f) assert not f.closed restored = open_dataset(f) assert not f.closed assert_identical(restored, original) restored.close() assert not f.closed @requires_h5netcdf @requires_netCDF4 @pytest.mark.filterwarnings("ignore:use make_scale(name) instead") class TestH5NetCDFData(NetCDF4Base): engine: T_NetcdfEngine = "h5netcdf" @contextlib.contextmanager def create_store(self): with create_tmp_file() as tmp_file: yield backends.H5NetCDFStore.open(tmp_file, "w") @pytest.mark.skipif( has_h5netcdf_1_4_0_or_above, reason="only valid for h5netcdf < 1.4.0" ) def test_complex(self) -> None: expected = Dataset({"x": ("y", np.ones(5) + 1j * np.ones(5))}) save_kwargs = {"invalid_netcdf": True} with pytest.warns(UserWarning, match="You are writing invalid netcdf features"): with self.roundtrip(expected, save_kwargs=save_kwargs) as actual: assert_equal(expected, actual) @pytest.mark.skipif( has_h5netcdf_1_4_0_or_above, reason="only valid for h5netcdf < 1.4.0" ) @pytest.mark.parametrize("invalid_netcdf", [None, False]) def test_complex_error(self, invalid_netcdf) -> None: import h5netcdf expected = Dataset({"x": ("y", np.ones(5) + 1j * np.ones(5))}) save_kwargs = {"invalid_netcdf": invalid_netcdf} with pytest.raises( h5netcdf.CompatibilityError, match="are not a supported NetCDF feature" ): with self.roundtrip(expected, save_kwargs=save_kwargs) as actual: assert_equal(expected, actual) def test_numpy_bool_(self) -> None: # h5netcdf loads booleans as numpy.bool_, this type needs to be supported # when writing invalid_netcdf datasets in order to support a roundtrip expected = Dataset({"x": ("y", np.ones(5), {"numpy_bool": np.bool_(True)})}) save_kwargs = {"invalid_netcdf": True} with pytest.warns(UserWarning, match="You are writing invalid netcdf features"): with self.roundtrip(expected, save_kwargs=save_kwargs) as actual: assert_identical(expected, actual) def test_cross_engine_read_write_netcdf4(self) -> None: # Drop dim3, because its labels include strings. These appear to be # not properly read with python-netCDF4, which converts them into # unicode instead of leaving them as bytes. data = create_test_data().drop_vars("dim3") data.attrs["foo"] = "bar" valid_engines: list[T_NetcdfEngine] = ["netcdf4", "h5netcdf"] for write_engine in valid_engines: with create_tmp_file() as tmp_file: data.to_netcdf(tmp_file, engine=write_engine) for read_engine in valid_engines: with open_dataset(tmp_file, engine=read_engine) as actual: assert_identical(data, actual) def test_read_byte_attrs_as_unicode(self) -> None: with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, "w") as nc: nc.foo = b"bar" with open_dataset(tmp_file) as actual: expected = Dataset(attrs={"foo": "bar"}) assert_identical(expected, actual) def test_compression_encoding_h5py(self) -> None: ENCODINGS: tuple[tuple[dict[str, Any], dict[str, Any]], ...] = ( # h5py style compression with gzip codec will be converted to # NetCDF4-Python style on round-trip ( {"compression": "gzip", "compression_opts": 9}, {"zlib": True, "complevel": 9}, ), # What can't be expressed in NetCDF4-Python style is # round-tripped unaltered ( {"compression": "lzf", "compression_opts": None}, {"compression": "lzf", "compression_opts": None}, ), # If both styles are used together, h5py format takes precedence ( { "compression": "lzf", "compression_opts": None, "zlib": True, "complevel": 9, }, {"compression": "lzf", "compression_opts": None}, ), ) for compr_in, compr_out in ENCODINGS: data = create_test_data() compr_common = { "chunksizes": (5, 5), "fletcher32": True, "shuffle": True, "original_shape": data.var2.shape, } data["var2"].encoding.update(compr_in) data["var2"].encoding.update(compr_common) compr_out.update(compr_common) data["scalar"] = ("scalar_dim", np.array([2.0])) data["scalar"] = data["scalar"][0] with self.roundtrip(data) as actual: for k, v in compr_out.items(): assert v == actual["var2"].encoding[k] def test_compression_check_encoding_h5py(self) -> None: """When mismatched h5py and NetCDF4-Python encodings are expressed in to_netcdf(encoding=...), must raise ValueError """ data = Dataset({"x": ("y", np.arange(10.0))}) # Compatible encodings are graciously supported with create_tmp_file() as tmp_file: data.to_netcdf( tmp_file, engine="h5netcdf", encoding={ "x": { "compression": "gzip", "zlib": True, "compression_opts": 6, "complevel": 6, } }, ) with open_dataset(tmp_file, engine="h5netcdf") as actual: assert actual.x.encoding["zlib"] is True assert actual.x.encoding["complevel"] == 6 # Incompatible encodings cause a crash with create_tmp_file() as tmp_file: with pytest.raises( ValueError, match=r"'zlib' and 'compression' encodings mismatch" ): data.to_netcdf( tmp_file, engine="h5netcdf", encoding={"x": {"compression": "lzf", "zlib": True}}, ) with create_tmp_file() as tmp_file: with pytest.raises( ValueError, match=r"'complevel' and 'compression_opts' encodings mismatch", ): data.to_netcdf( tmp_file, engine="h5netcdf", encoding={ "x": { "compression": "gzip", "compression_opts": 5, "complevel": 6, } }, ) def test_dump_encodings_h5py(self) -> None: # regression test for #709 ds = Dataset({"x": ("y", np.arange(10.0))}) kwargs = {"encoding": {"x": {"compression": "gzip", "compression_opts": 9}}} with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert actual.x.encoding["zlib"] assert actual.x.encoding["complevel"] == 9 kwargs = {"encoding": {"x": {"compression": "lzf", "compression_opts": None}}} with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert actual.x.encoding["compression"] == "lzf" assert actual.x.encoding["compression_opts"] is None def test_decode_utf8_warning(self) -> None: title = b"\xc3" with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, "w") as f: f.title = title with pytest.warns(UnicodeWarning, match="returning bytes undecoded") as w: ds = xr.load_dataset(tmp_file, engine="h5netcdf") assert ds.title == title assert "attribute 'title' of h5netcdf object '/'" in str(w[0].message) def test_byte_attrs(self, byte_attrs_dataset: dict[str, Any]) -> None: with pytest.raises(ValueError, match=byte_attrs_dataset["h5netcdf_error"]): super().test_byte_attrs(byte_attrs_dataset) @requires_h5netcdf_1_4_0_or_above def test_roundtrip_complex(self): expected = Dataset({"x": ("y", np.ones(5) + 1j * np.ones(5))}) with self.roundtrip(expected) as actual: assert_equal(expected, actual) def test_phony_dims_warning(self) -> None: import h5py foo_data = np.arange(125).reshape(5, 5, 5) bar_data = np.arange(625).reshape(25, 5, 5) var = {"foo1": foo_data, "foo2": bar_data, "foo3": foo_data, "foo4": bar_data} with create_tmp_file() as tmp_file: with h5py.File(tmp_file, "w") as f: grps = ["bar", "baz"] for grp in grps: fx = f.create_group(grp) for k, v in var.items(): fx.create_dataset(k, data=v) with pytest.warns(UserWarning, match="The 'phony_dims' kwarg"): with xr.open_dataset(tmp_file, engine="h5netcdf", group="bar") as ds: assert ds.sizes == { "phony_dim_0": 5, "phony_dim_1": 5, "phony_dim_2": 5, "phony_dim_3": 25, } @requires_h5netcdf @requires_netCDF4 class TestH5NetCDFAlreadyOpen: def test_open_dataset_group(self) -> None: import h5netcdf with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: group = nc.createGroup("g") v = group.createVariable("x", "int") v[...] = 42 kwargs = {"decode_vlen_strings": True} h5 = h5netcdf.File(tmp_file, mode="r", **kwargs) store = backends.H5NetCDFStore(h5["g"]) with open_dataset(store) as ds: expected = Dataset({"x": ((), 42)}) assert_identical(expected, ds) h5 = h5netcdf.File(tmp_file, mode="r", **kwargs) store = backends.H5NetCDFStore(h5, group="g") with open_dataset(store) as ds: expected = Dataset({"x": ((), 42)}) assert_identical(expected, ds) def test_deepcopy(self) -> None: import h5netcdf with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("x", 10) v = nc.createVariable("y", np.int32, ("x",)) v[:] = np.arange(10) kwargs = {"decode_vlen_strings": True} h5 = h5netcdf.File(tmp_file, mode="r", **kwargs) store = backends.H5NetCDFStore(h5) with open_dataset(store) as ds: copied = ds.copy(deep=True) expected = Dataset({"y": ("x", np.arange(10))}) assert_identical(expected, copied) @requires_h5netcdf class TestH5NetCDFFileObject(TestH5NetCDFData): engine: T_NetcdfEngine = "h5netcdf" def test_open_badbytes(self) -> None: with pytest.raises( ValueError, match=r"match in any of xarray's currently installed IO" ): with open_dataset(b"garbage"): pass with pytest.raises(ValueError, match=r"can only read bytes"): with open_dataset(b"garbage", engine="netcdf4"): pass with pytest.raises( ValueError, match=r"not the signature of a valid netCDF4 file" ): with open_dataset(BytesIO(b"garbage"), engine="h5netcdf"): pass def test_open_twice(self) -> None: expected = create_test_data() expected.attrs["foo"] = "bar" with create_tmp_file() as tmp_file: expected.to_netcdf(tmp_file, engine="h5netcdf") with open(tmp_file, "rb") as f: with open_dataset(f, engine="h5netcdf"): with open_dataset(f, engine="h5netcdf"): pass @requires_scipy def test_open_fileobj(self) -> None: # open in-memory datasets instead of local file paths expected = create_test_data().drop_vars("dim3") expected.attrs["foo"] = "bar" with create_tmp_file() as tmp_file: expected.to_netcdf(tmp_file, engine="h5netcdf") with open(tmp_file, "rb") as f: with open_dataset(f, engine="h5netcdf") as actual: assert_identical(expected, actual) f.seek(0) with open_dataset(f) as actual: assert_identical(expected, actual) f.seek(0) with BytesIO(f.read()) as bio: with open_dataset(bio, engine="h5netcdf") as actual: assert_identical(expected, actual) f.seek(0) with pytest.raises(TypeError, match="not a valid NetCDF 3"): open_dataset(f, engine="scipy") # TODO: this additional open is required since scipy seems to close the file # when it fails on the TypeError (though didn't when we used # `raises_regex`?). Ref https://github.com/pydata/xarray/pull/5191 with open(tmp_file, "rb") as f: f.seek(8) with open_dataset(f): # ensure file gets closed pass def test_file_remains_open(self) -> None: data = Dataset({"foo": ("x", [1, 2, 3])}) f = BytesIO() data.to_netcdf(f, engine="h5netcdf") assert not f.closed restored = open_dataset(f, engine="h5netcdf") assert not f.closed assert_identical(restored, data) restored.close() assert not f.closed @requires_h5netcdf class TestH5NetCDFInMemoryData: def test_roundtrip_via_bytes(self) -> None: original = create_test_data() netcdf_bytes = original.to_netcdf(engine="h5netcdf") roundtrip = open_dataset(netcdf_bytes, engine="h5netcdf") assert_identical(roundtrip, original) def test_roundtrip_group_via_bytes(self) -> None: original = create_test_data() netcdf_bytes = original.to_netcdf(group="sub", engine="h5netcdf") roundtrip = open_dataset(netcdf_bytes, group="sub", engine="h5netcdf") assert_identical(roundtrip, original) @requires_h5netcdf @requires_dask @pytest.mark.filterwarnings("ignore:deallocating CachingFileManager") class TestH5NetCDFViaDaskData(TestH5NetCDFData): @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} open_kwargs.setdefault("chunks", -1) with TestH5NetCDFData.roundtrip( self, data, save_kwargs, open_kwargs, allow_cleanup_failure ) as ds: yield ds @pytest.mark.skip(reason="caching behavior differs for dask") def test_dataset_caching(self) -> None: pass def test_write_inconsistent_chunks(self) -> None: # Construct two variables with the same dimensions, but different # chunk sizes. x = da.zeros((100, 100), dtype="f4", chunks=(50, 100)) x = DataArray(data=x, dims=("lat", "lon"), name="x") x.encoding["chunksizes"] = (50, 100) x.encoding["original_shape"] = (100, 100) y = da.ones((100, 100), dtype="f4", chunks=(100, 50)) y = DataArray(data=y, dims=("lat", "lon"), name="y") y.encoding["chunksizes"] = (100, 50) y.encoding["original_shape"] = (100, 100) # Put them both into the same dataset ds = Dataset({"x": x, "y": y}) with self.roundtrip(ds) as actual: assert actual["x"].encoding["chunksizes"] == (50, 100) assert actual["y"].encoding["chunksizes"] == (100, 50) @requires_h5netcdf_ros3 class TestH5NetCDFDataRos3Driver(TestCommon): engine: T_NetcdfEngine = "h5netcdf" test_remote_dataset: str = ( "https://www.unidata.ucar.edu/software/netcdf/examples/OMI-Aura_L2-example.nc" ) @pytest.mark.skip("Skipping due to download needed") @pytest.mark.filterwarnings("ignore:Duplicate dimension names") def test_get_variable_list(self) -> None: with open_dataset( self.test_remote_dataset, engine="h5netcdf", backend_kwargs={"driver": "ros3"}, ) as actual: assert "Temperature" in list(actual) @pytest.mark.skip("Skipping due to download needed") @pytest.mark.filterwarnings("ignore:Duplicate dimension names") def test_get_variable_list_empty_driver_kwds(self) -> None: driver_kwds = { "secret_id": b"", "secret_key": b"", } backend_kwargs = {"driver": "ros3", "driver_kwds": driver_kwds} with open_dataset( self.test_remote_dataset, engine="h5netcdf", backend_kwargs=backend_kwargs ) as actual: assert "Temperature" in list(actual) @pytest.fixture(params=["scipy", "netcdf4", "h5netcdf", "zarr"]) def readengine(request): return request.param @pytest.fixture(params=[1, 20]) def nfiles(request): return request.param @pytest.fixture(params=[5, None]) def file_cache_maxsize(request): maxsize = request.param if maxsize is not None: with set_options(file_cache_maxsize=maxsize): yield maxsize else: yield maxsize @pytest.fixture(params=[True, False]) def parallel(request): return request.param @pytest.fixture(params=[None, 5]) def chunks(request): return request.param @pytest.fixture(params=["tmp_path", "ZipStore", "Dict"]) def tmp_store(request, tmp_path): if request.param == "tmp_path": return tmp_path elif request.param == "ZipStore": from zarr.storage import ZipStore path = tmp_path / "store.zip" return ZipStore(path) elif request.param == "Dict": return dict() else: raise ValueError("not supported") # using pytest.mark.skipif does not work so this a work around def skip_if_not_engine(engine): if engine == "netcdf4": pytest.importorskip("netCDF4") else: pytest.importorskip(engine) @requires_dask @pytest.mark.filterwarnings("ignore:use make_scale(name) instead") @pytest.mark.skip( reason="Flaky test which can cause the worker to crash (so don't xfail). Very open to contributions fixing this" ) def test_open_mfdataset_manyfiles( readengine, nfiles, parallel, chunks, file_cache_maxsize ): # skip certain combinations skip_if_not_engine(readengine) randdata = np.random.randn(nfiles) original = Dataset({"foo": ("x", randdata)}) # test standard open_mfdataset approach with too many files with create_tmp_files(nfiles) as tmpfiles: # split into multiple sets of temp files for ii in original.x.values: subds = original.isel(x=slice(ii, ii + 1)) if readengine != "zarr": subds.to_netcdf(tmpfiles[ii], engine=readengine) else: # if writeengine == "zarr": subds.to_zarr(store=tmpfiles[ii]) # check that calculation on opened datasets works properly with open_mfdataset( tmpfiles, combine="nested", concat_dim="x", engine=readengine, parallel=parallel, chunks=chunks if (not chunks and readengine != "zarr") else "auto", ) as actual: # check that using open_mfdataset returns dask arrays for variables assert isinstance(actual["foo"].data, dask_array_type) assert_identical(original, actual) @requires_netCDF4 @requires_dask def test_open_mfdataset_can_open_path_objects() -> None: dataset = os.path.join(os.path.dirname(__file__), "data", "example_1.nc") with open_mfdataset(Path(dataset)) as actual: assert isinstance(actual, Dataset) @requires_netCDF4 @requires_dask def test_open_mfdataset_list_attr() -> None: """ Case when an attribute of type list differs across the multiple files """ from netCDF4 import Dataset with create_tmp_files(2) as nfiles: for i in range(2): with Dataset(nfiles[i], "w") as f: f.createDimension("x", 3) vlvar = f.createVariable("test_var", np.int32, ("x")) # here create an attribute as a list vlvar.test_attr = [f"string a {i}", f"string b {i}"] vlvar[:] = np.arange(3) with open_dataset(nfiles[0]) as ds1: with open_dataset(nfiles[1]) as ds2: original = xr.concat([ds1, ds2], dim="x") with xr.open_mfdataset( [nfiles[0], nfiles[1]], combine="nested", concat_dim="x" ) as actual: assert_identical(actual, original) @requires_scipy_or_netCDF4 @requires_dask class TestOpenMFDatasetWithDataVarsAndCoordsKw: coord_name = "lon" var_name = "v1" @contextlib.contextmanager def setup_files_and_datasets(self, *, fuzz=0, new_combine_kwargs: bool = False): ds1, ds2 = self.gen_datasets_with_common_coord_and_time() # to test join='exact' ds1["x"] = ds1.x + fuzz with create_tmp_file() as tmpfile1: with create_tmp_file() as tmpfile2: # save data to the temporary files ds1.to_netcdf(tmpfile1) ds2.to_netcdf(tmpfile2) with set_options(use_new_combine_kwarg_defaults=new_combine_kwargs): yield [tmpfile1, tmpfile2], [ds1, ds2] def gen_datasets_with_common_coord_and_time(self): # create coordinate data nx = 10 nt = 10 x = np.arange(nx) t1 = np.arange(nt) t2 = np.arange(nt, 2 * nt, 1) v1 = np.random.randn(nt, nx) v2 = np.random.randn(nt, nx) ds1 = Dataset( data_vars={self.var_name: (["t", "x"], v1), self.coord_name: ("x", 2 * x)}, coords={"t": (["t"], t1), "x": (["x"], x)}, ) ds2 = Dataset( data_vars={self.var_name: (["t", "x"], v2), self.coord_name: ("x", 2 * x)}, coords={"t": (["t"], t2), "x": (["x"], x)}, ) return ds1, ds2 @pytest.mark.parametrize( "combine, concat_dim", [("nested", "t"), ("by_coords", None)] ) @pytest.mark.parametrize("opt", ["all", "minimal", "different"]) @pytest.mark.parametrize("join", ["outer", "inner", "left", "right"]) def test_open_mfdataset_does_same_as_concat( self, combine, concat_dim, opt, join ) -> None: with self.setup_files_and_datasets() as (files, [ds1, ds2]): if combine == "by_coords": files.reverse() with open_mfdataset( files, data_vars=opt, combine=combine, concat_dim=concat_dim, join=join, compat="equals", ) as ds: ds_expect = xr.concat( [ds1, ds2], data_vars=opt, dim="t", join=join, compat="equals" ) assert_identical(ds, ds_expect) @pytest.mark.parametrize("use_new_combine_kwarg_defaults", [True, False]) @pytest.mark.parametrize( ["combine_attrs", "attrs", "expected", "expect_error"], ( pytest.param("drop", [{"a": 1}, {"a": 2}], {}, False, id="drop"), pytest.param( "override", [{"a": 1}, {"a": 2}], {"a": 1}, False, id="override" ), pytest.param( "no_conflicts", [{"a": 1}, {"a": 2}], None, True, id="no_conflicts" ), pytest.param( "identical", [{"a": 1, "b": 2}, {"a": 1, "c": 3}], None, True, id="identical", ), pytest.param( "drop_conflicts", [{"a": 1, "b": 2}, {"b": -1, "c": 3}], {"a": 1, "c": 3}, False, id="drop_conflicts", ), ), ) def test_open_mfdataset_dataset_combine_attrs( self, use_new_combine_kwarg_defaults, combine_attrs, attrs, expected, expect_error, ): with self.setup_files_and_datasets() as (files, [ds1, ds2]): # Give the files an inconsistent attribute for i, f in enumerate(files): ds = open_dataset(f).load() ds.attrs = attrs[i] ds.close() ds.to_netcdf(f) with set_options( use_new_combine_kwarg_defaults=use_new_combine_kwarg_defaults ): warning: contextlib.AbstractContextManager = ( pytest.warns(FutureWarning) if not use_new_combine_kwarg_defaults else contextlib.nullcontext() ) error: contextlib.AbstractContextManager = ( pytest.raises(xr.MergeError) if expect_error else contextlib.nullcontext() ) with warning: with error: with xr.open_mfdataset( files, combine="nested", concat_dim="t", combine_attrs=combine_attrs, ) as ds: assert ds.attrs == expected def test_open_mfdataset_dataset_attr_by_coords(self) -> None: """ Case when an attribute differs across the multiple files """ with self.setup_files_and_datasets() as (files, [ds1, ds2]): # Give the files an inconsistent attribute for i, f in enumerate(files): ds = open_dataset(f).load() ds.attrs["test_dataset_attr"] = 10 + i ds.close() ds.to_netcdf(f) with set_options(use_new_combine_kwarg_defaults=True): with xr.open_mfdataset(files, combine="nested", concat_dim="t") as ds: assert ds.test_dataset_attr == 10 def test_open_mfdataset_dataarray_attr_by_coords(self) -> None: """ Case when an attribute of a member DataArray differs across the multiple files """ with self.setup_files_and_datasets(new_combine_kwargs=True) as ( files, [ds1, ds2], ): # Give the files an inconsistent attribute for i, f in enumerate(files): ds = open_dataset(f).load() ds["v1"].attrs["test_dataarray_attr"] = i ds.close() ds.to_netcdf(f) with xr.open_mfdataset( files, data_vars=None, combine="nested", concat_dim="t" ) as ds: assert ds["v1"].test_dataarray_attr == 0 @pytest.mark.parametrize( "combine, concat_dim", [("nested", "t"), ("by_coords", None)] ) @pytest.mark.parametrize( "kwargs", [ {"data_vars": "all"}, {"data_vars": "minimal"}, { "data_vars": "all", "coords": "different", "compat": "no_conflicts", }, # old defaults { "data_vars": None, "coords": "minimal", "compat": "override", }, # new defaults {"data_vars": "different", "compat": "no_conflicts"}, {}, ], ) def test_open_mfdataset_exact_join_raises_error( self, combine, concat_dim, kwargs ) -> None: with self.setup_files_and_datasets(fuzz=0.1, new_combine_kwargs=True) as ( files, _, ): if combine == "by_coords": files.reverse() with pytest.raises( ValueError, match="cannot align objects with join='exact'" ): open_mfdataset( files, **kwargs, combine=combine, concat_dim=concat_dim, join="exact", ) def test_open_mfdataset_defaults_with_exact_join_warns_as_well_as_raising( self, ) -> None: with self.setup_files_and_datasets(fuzz=0.1, new_combine_kwargs=True) as ( files, _, ): files.reverse() with pytest.raises( ValueError, match="cannot align objects with join='exact'" ): open_mfdataset(files, combine="by_coords") def test_common_coord_when_datavars_all(self) -> None: opt: Final = "all" with self.setup_files_and_datasets() as (files, [ds1, ds2]): # open the files with the data_var option with open_mfdataset( files, data_vars=opt, combine="nested", concat_dim="t" ) as ds: coord_shape = ds[self.coord_name].shape coord_shape1 = ds1[self.coord_name].shape coord_shape2 = ds2[self.coord_name].shape var_shape = ds[self.var_name].shape assert var_shape == coord_shape assert coord_shape1 != coord_shape assert coord_shape2 != coord_shape def test_common_coord_when_datavars_minimal(self) -> None: opt: Final = "minimal" with self.setup_files_and_datasets(new_combine_kwargs=True) as ( files, [ds1, ds2], ): # open the files using data_vars option with open_mfdataset( files, data_vars=opt, combine="nested", concat_dim="t" ) as ds: coord_shape = ds[self.coord_name].shape coord_shape1 = ds1[self.coord_name].shape coord_shape2 = ds2[self.coord_name].shape var_shape = ds[self.var_name].shape assert var_shape != coord_shape assert coord_shape1 == coord_shape assert coord_shape2 == coord_shape def test_invalid_data_vars_value_should_fail(self) -> None: with self.setup_files_and_datasets() as (files, _): with pytest.raises(ValueError): with open_mfdataset(files, data_vars="minimum", combine="by_coords"): # type: ignore[arg-type] pass # test invalid coord parameter with pytest.raises(ValueError): with open_mfdataset(files, coords="minimum", combine="by_coords"): pass @pytest.mark.parametrize( "combine, concat_dim", [("nested", "t"), ("by_coords", None)] ) @pytest.mark.parametrize( "kwargs", [{"data_vars": "different"}, {"coords": "different"}] ) def test_open_mfdataset_warns_when_kwargs_set_to_different( self, combine, concat_dim, kwargs ) -> None: with self.setup_files_and_datasets(new_combine_kwargs=True) as ( files, [ds1, ds2], ): if combine == "by_coords": files.reverse() with pytest.raises( ValueError, match="Previously the default was `compat='no_conflicts'`" ): open_mfdataset(files, combine=combine, concat_dim=concat_dim, **kwargs) with pytest.raises( ValueError, match="Previously the default was `compat='equals'`" ): xr.concat([ds1, ds2], dim="t", **kwargs) with set_options(use_new_combine_kwarg_defaults=False): expectation: contextlib.AbstractContextManager = ( pytest.warns( FutureWarning, match="will change from data_vars='all'", ) if "data_vars" not in kwargs else contextlib.nullcontext() ) with pytest.warns( FutureWarning, match="will change from compat='equals'", ): with expectation: ds_expect = xr.concat([ds1, ds2], dim="t", **kwargs) with pytest.warns( FutureWarning, match="will change from compat='no_conflicts'" ): with expectation: with open_mfdataset( files, combine=combine, concat_dim=concat_dim, **kwargs ) as ds: assert_identical(ds, ds_expect) @requires_dask @requires_scipy @requires_netCDF4 class TestDask(DatasetIOBase): @contextlib.contextmanager def create_store(self): yield Dataset() @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): yield data.chunk() # Override methods in DatasetIOBase - not applicable to dask def test_roundtrip_string_encoded_characters(self) -> None: pass def test_roundtrip_coordinates_with_space(self) -> None: pass def test_roundtrip_numpy_datetime_data(self) -> None: # Override method in DatasetIOBase - remove not applicable # save_kwargs times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"], unit="ns") expected = Dataset({"t": ("t", times), "t0": times[0]}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_cftime_datetime_data(self) -> None: # Override method in DatasetIOBase - remove not applicable # save_kwargs from xarray.tests.test_coding_times import _all_cftime_date_types date_types = _all_cftime_date_types() for date_type in date_types.values(): times = [date_type(1, 1, 1), date_type(1, 1, 2)] expected = Dataset({"t": ("t", times), "t0": times[0]}) expected_decoded_t = np.array(times) expected_decoded_t0 = np.array([date_type(1, 1, 1)]) with self.roundtrip(expected) as actual: assert_array_equal(actual.t.values, expected_decoded_t) assert_array_equal(actual.t0.values, expected_decoded_t0) def test_write_store(self) -> None: # Override method in DatasetIOBase - not applicable to dask pass def test_dataset_caching(self) -> None: expected = Dataset({"foo": ("x", [5, 6, 7])}) with self.roundtrip(expected) as actual: assert not actual.foo.variable._in_memory _ = actual.foo.values # no caching assert not actual.foo.variable._in_memory def test_open_mfdataset(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: original.isel(x=slice(5)).to_netcdf(tmp1) original.isel(x=slice(5, 10)).to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: assert isinstance(actual.foo.variable.data, da.Array) assert actual.foo.variable.data.chunks == ((5, 5),) assert_identical(original, actual) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested", chunks={"x": 3} ) as actual: assert actual.foo.variable.data.chunks == ((3, 2, 3, 2),) with pytest.raises(OSError, match=r"no files to open"): open_mfdataset("foo-bar-baz-*.nc") with pytest.raises(ValueError, match=r"wild-card"): open_mfdataset("http://some/remote/uri") @requires_fsspec def test_open_mfdataset_no_files(self) -> None: pytest.importorskip("aiobotocore") # glob is attempted as of #4823, but finds no files with pytest.raises(OSError, match=r"no files"): open_mfdataset("http://some/remote/uri", engine="zarr") def test_open_mfdataset_2d(self) -> None: original = Dataset({"foo": (["x", "y"], np.random.randn(10, 8))}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: with create_tmp_file() as tmp3: with create_tmp_file() as tmp4: original.isel(x=slice(5), y=slice(4)).to_netcdf(tmp1) original.isel(x=slice(5, 10), y=slice(4)).to_netcdf(tmp2) original.isel(x=slice(5), y=slice(4, 8)).to_netcdf(tmp3) original.isel(x=slice(5, 10), y=slice(4, 8)).to_netcdf(tmp4) with open_mfdataset( [[tmp1, tmp2], [tmp3, tmp4]], combine="nested", concat_dim=["y", "x"], ) as actual: assert isinstance(actual.foo.variable.data, da.Array) assert actual.foo.variable.data.chunks == ((5, 5), (4, 4)) assert_identical(original, actual) with open_mfdataset( [[tmp1, tmp2], [tmp3, tmp4]], combine="nested", concat_dim=["y", "x"], chunks={"x": 3, "y": 2}, ) as actual: assert actual.foo.variable.data.chunks == ( (3, 2, 3, 2), (2, 2, 2, 2), ) def test_open_mfdataset_pathlib(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmps1: with create_tmp_file() as tmps2: tmp1 = Path(tmps1) tmp2 = Path(tmps2) original.isel(x=slice(5)).to_netcdf(tmp1) original.isel(x=slice(5, 10)).to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: assert_identical(original, actual) def test_open_mfdataset_2d_pathlib(self) -> None: original = Dataset({"foo": (["x", "y"], np.random.randn(10, 8))}) with create_tmp_file() as tmps1: with create_tmp_file() as tmps2: with create_tmp_file() as tmps3: with create_tmp_file() as tmps4: tmp1 = Path(tmps1) tmp2 = Path(tmps2) tmp3 = Path(tmps3) tmp4 = Path(tmps4) original.isel(x=slice(5), y=slice(4)).to_netcdf(tmp1) original.isel(x=slice(5, 10), y=slice(4)).to_netcdf(tmp2) original.isel(x=slice(5), y=slice(4, 8)).to_netcdf(tmp3) original.isel(x=slice(5, 10), y=slice(4, 8)).to_netcdf(tmp4) with open_mfdataset( [[tmp1, tmp2], [tmp3, tmp4]], combine="nested", concat_dim=["y", "x"], ) as actual: assert_identical(original, actual) def test_open_mfdataset_2(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: original.isel(x=slice(5)).to_netcdf(tmp1) original.isel(x=slice(5, 10)).to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: assert_identical(original, actual) def test_open_mfdataset_with_ignore(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_files(2) as (tmp1, tmp2): ds1 = original.isel(x=slice(5)) ds2 = original.isel(x=slice(5, 10)) ds1.to_netcdf(tmp1) ds2.to_netcdf(tmp2) with open_mfdataset( [tmp1, "non-existent-file.nc", tmp2], concat_dim="x", combine="nested", errors="ignore", ) as actual: assert_identical(original, actual) def test_open_mfdataset_with_warn(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with pytest.warns(UserWarning, match="Ignoring."): with create_tmp_files(2) as (tmp1, tmp2): ds1 = original.isel(x=slice(5)) ds2 = original.isel(x=slice(5, 10)) ds1.to_netcdf(tmp1) ds2.to_netcdf(tmp2) with open_mfdataset( [tmp1, "non-existent-file.nc", tmp2], concat_dim="x", combine="nested", errors="warn", ) as actual: assert_identical(original, actual) def test_open_mfdataset_2d_with_ignore(self) -> None: original = Dataset({"foo": (["x", "y"], np.random.randn(10, 8))}) with create_tmp_files(4) as (tmp1, tmp2, tmp3, tmp4): original.isel(x=slice(5), y=slice(4)).to_netcdf(tmp1) original.isel(x=slice(5, 10), y=slice(4)).to_netcdf(tmp2) original.isel(x=slice(5), y=slice(4, 8)).to_netcdf(tmp3) original.isel(x=slice(5, 10), y=slice(4, 8)).to_netcdf(tmp4) with open_mfdataset( [[tmp1, tmp2], ["non-existent-file.nc", tmp3, tmp4]], combine="nested", concat_dim=["y", "x"], errors="ignore", ) as actual: assert_identical(original, actual) def test_open_mfdataset_2d_with_warn(self) -> None: original = Dataset({"foo": (["x", "y"], np.random.randn(10, 8))}) with pytest.warns(UserWarning, match="Ignoring."): with create_tmp_files(4) as (tmp1, tmp2, tmp3, tmp4): original.isel(x=slice(5), y=slice(4)).to_netcdf(tmp1) original.isel(x=slice(5, 10), y=slice(4)).to_netcdf(tmp2) original.isel(x=slice(5), y=slice(4, 8)).to_netcdf(tmp3) original.isel(x=slice(5, 10), y=slice(4, 8)).to_netcdf(tmp4) with open_mfdataset( [[tmp1, tmp2, "non-existent-file.nc"], [tmp3, tmp4]], combine="nested", concat_dim=["y", "x"], errors="warn", ) as actual: assert_identical(original, actual) def test_attrs_mfdataset(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: ds1 = original.isel(x=slice(5)) ds2 = original.isel(x=slice(5, 10)) ds1.attrs["test1"] = "foo" ds2.attrs["test2"] = "bar" ds1.to_netcdf(tmp1) ds2.to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: # presumes that attributes inherited from # first dataset loaded assert actual.test1 == ds1.test1 # attributes from ds2 are not retained, e.g., with pytest.raises(AttributeError, match=r"no attribute"): _ = actual.test2 def test_open_mfdataset_attrs_file(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_files(2) as (tmp1, tmp2): ds1 = original.isel(x=slice(5)) ds2 = original.isel(x=slice(5, 10)) ds1.attrs["test1"] = "foo" ds2.attrs["test2"] = "bar" ds1.to_netcdf(tmp1) ds2.to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested", attrs_file=tmp2 ) as actual: # attributes are inherited from the master file assert actual.attrs["test2"] == ds2.attrs["test2"] # attributes from ds1 are not retained, e.g., assert "test1" not in actual.attrs def test_open_mfdataset_attrs_file_path(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_files(2) as (tmps1, tmps2): tmp1 = Path(tmps1) tmp2 = Path(tmps2) ds1 = original.isel(x=slice(5)) ds2 = original.isel(x=slice(5, 10)) ds1.attrs["test1"] = "foo" ds2.attrs["test2"] = "bar" ds1.to_netcdf(tmp1) ds2.to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested", attrs_file=tmp2 ) as actual: # attributes are inherited from the master file assert actual.attrs["test2"] == ds2.attrs["test2"] # attributes from ds1 are not retained, e.g., assert "test1" not in actual.attrs def test_open_mfdataset_auto_combine(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10)), "x": np.arange(10)}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: original.isel(x=slice(5)).to_netcdf(tmp1) original.isel(x=slice(5, 10)).to_netcdf(tmp2) with open_mfdataset([tmp2, tmp1], combine="by_coords") as actual: assert_identical(original, actual) def test_open_mfdataset_raise_on_bad_combine_args(self) -> None: # Regression test for unhelpful error shown in #5230 original = Dataset({"foo": ("x", np.random.randn(10)), "x": np.arange(10)}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: original.isel(x=slice(5)).to_netcdf(tmp1) original.isel(x=slice(5, 10)).to_netcdf(tmp2) with pytest.raises(ValueError, match="`concat_dim` has no effect"): open_mfdataset([tmp1, tmp2], concat_dim="x") def test_encoding_mfdataset(self) -> None: original = Dataset( { "foo": ("t", np.random.randn(10)), "t": ("t", pd.date_range(start="2010-01-01", periods=10, freq="1D")), } ) original.t.encoding["units"] = "days since 2010-01-01" with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: ds1 = original.isel(t=slice(5)) ds2 = original.isel(t=slice(5, 10)) ds1.t.encoding["units"] = "days since 2010-01-01" ds2.t.encoding["units"] = "days since 2000-01-01" ds1.to_netcdf(tmp1) ds2.to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], combine="nested", concat_dim="t" ) as actual: assert actual.t.encoding["units"] == original.t.encoding["units"] assert actual.t.encoding["units"] == ds1.t.encoding["units"] assert actual.t.encoding["units"] != ds2.t.encoding["units"] def test_encoding_mfdataset_new_defaults(self) -> None: original = Dataset( { "foo": ("t", np.random.randn(10)), "t": ("t", pd.date_range(start="2010-01-01", periods=10, freq="1D")), } ) original.t.encoding["units"] = "days since 2010-01-01" with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: ds1 = original.isel(t=slice(5)) ds2 = original.isel(t=slice(5, 10)) ds1.t.encoding["units"] = "days since 2010-01-01" ds2.t.encoding["units"] = "days since 2000-01-01" ds1.to_netcdf(tmp1) ds2.to_netcdf(tmp2) for setting in [True, False]: with set_options(use_new_combine_kwarg_defaults=setting): with open_mfdataset( [tmp1, tmp2], combine="nested", concat_dim="t" ) as old: assert ( old.t.encoding["units"] == original.t.encoding["units"] ) assert old.t.encoding["units"] == ds1.t.encoding["units"] assert old.t.encoding["units"] != ds2.t.encoding["units"] with set_options(use_new_combine_kwarg_defaults=True): with pytest.raises( AlignmentError, match="If you are intending to concatenate" ): open_mfdataset([tmp1, tmp2], combine="nested") def test_preprocess_mfdataset(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp: original.to_netcdf(tmp) def preprocess(ds): return ds.assign_coords(z=0) expected = preprocess(original) with open_mfdataset( tmp, preprocess=preprocess, combine="by_coords" ) as actual: assert_identical(expected, actual) def test_save_mfdataset_roundtrip(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) datasets = [original.isel(x=slice(5)), original.isel(x=slice(5, 10))] with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: save_mfdataset(datasets, [tmp1, tmp2]) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: assert_identical(actual, original) def test_save_mfdataset_invalid(self) -> None: ds = Dataset() with pytest.raises(ValueError, match=r"cannot use mode"): save_mfdataset([ds, ds], ["same", "same"]) with pytest.raises(ValueError, match=r"same length"): save_mfdataset([ds, ds], ["only one path"]) def test_save_mfdataset_invalid_dataarray(self) -> None: # regression test for GH1555 da = DataArray([1, 2]) with pytest.raises(TypeError, match=r"supports writing Dataset"): save_mfdataset([da], ["dataarray"]) def test_save_mfdataset_pathlib_roundtrip(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) datasets = [original.isel(x=slice(5)), original.isel(x=slice(5, 10))] with create_tmp_file() as tmps1: with create_tmp_file() as tmps2: tmp1 = Path(tmps1) tmp2 = Path(tmps2) save_mfdataset(datasets, [tmp1, tmp2]) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: assert_identical(actual, original) def test_save_mfdataset_pass_kwargs(self) -> None: # create a timeseries to store in a netCDF file times = [0, 1] time = xr.DataArray(times, dims=("time",)) # create a simple dataset to write using save_mfdataset test_ds = xr.Dataset() test_ds["time"] = time # make sure the times are written as double and # turn off fill values encoding = dict(time=dict(dtype="double")) unlimited_dims = ["time"] # set the output file name output_path = "test.nc" # attempt to write the dataset with the encoding and unlimited args # passed through xr.save_mfdataset( [test_ds], [output_path], encoding=encoding, unlimited_dims=unlimited_dims ) def test_open_and_do_math(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp: original.to_netcdf(tmp) with open_mfdataset(tmp, combine="by_coords") as ds: actual = 1.0 * ds assert_allclose(original, actual, decode_bytes=False) @pytest.mark.parametrize( "kwargs", [pytest.param({"concat_dim": None}, id="none"), pytest.param({}, id="default")], ) def test_open_mfdataset_concat_dim(self, kwargs) -> None: with set_options(use_new_combine_kwarg_defaults=True): with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: data = Dataset({"x": 0}) data.to_netcdf(tmp1) Dataset({"x": np.nan}).to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], **kwargs, combine="nested" ) as actual: assert_identical(data, actual) def test_open_dataset(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp: original.to_netcdf(tmp) with open_dataset(tmp, chunks={"x": 5}) as actual: assert isinstance(actual.foo.variable.data, da.Array) assert actual.foo.variable.data.chunks == ((5, 5),) assert_identical(original, actual) with open_dataset(tmp, chunks=5) as actual: assert_identical(original, actual) with open_dataset(tmp) as actual: assert isinstance(actual.foo.variable.data, np.ndarray) assert_identical(original, actual) def test_open_single_dataset(self) -> None: # Test for issue GH #1988. This makes sure that the # concat_dim is utilized when specified in open_mfdataset(). rnddata = np.random.randn(10) original = Dataset({"foo": ("x", rnddata)}) dim = DataArray([100], name="baz", dims="baz") expected = Dataset( {"foo": (("baz", "x"), rnddata[np.newaxis, :])}, {"baz": [100]} ) with create_tmp_file() as tmp: original.to_netcdf(tmp) with open_mfdataset( [tmp], concat_dim=dim, data_vars="all", combine="nested" ) as actual: assert_identical(expected, actual) def test_open_multi_dataset(self) -> None: # Test for issue GH #1988 and #2647. This makes sure that the # concat_dim is utilized when specified in open_mfdataset(). # The additional wrinkle is to ensure that a length greater # than one is tested as well due to numpy's implicit casting # of 1-length arrays to booleans in tests, which allowed # #2647 to still pass the test_open_single_dataset(), # which is itself still needed as-is because the original # bug caused one-length arrays to not be used correctly # in concatenation. rnddata = np.random.randn(10) original = Dataset({"foo": ("x", rnddata)}) dim = DataArray([100, 150], name="baz", dims="baz") expected = Dataset( {"foo": (("baz", "x"), np.tile(rnddata[np.newaxis, :], (2, 1)))}, {"baz": [100, 150]}, ) with create_tmp_file() as tmp1, create_tmp_file() as tmp2: original.to_netcdf(tmp1) original.to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim=dim, data_vars="all", combine="nested" ) as actual: assert_identical(expected, actual) # Flaky test. Very open to contributions on fixing this @pytest.mark.flaky def test_dask_roundtrip(self) -> None: with create_tmp_file() as tmp: data = create_test_data() data.to_netcdf(tmp) chunks = {"dim1": 4, "dim2": 4, "dim3": 4, "time": 10} with open_dataset(tmp, chunks=chunks) as dask_ds: assert_identical(data, dask_ds) with create_tmp_file() as tmp2: dask_ds.to_netcdf(tmp2) with open_dataset(tmp2) as on_disk: assert_identical(data, on_disk) def test_deterministic_names(self) -> None: with create_tmp_file() as tmp: data = create_test_data() data.to_netcdf(tmp) with open_mfdataset(tmp, combine="by_coords") as ds: original_names = {k: v.data.name for k, v in ds.data_vars.items()} with open_mfdataset(tmp, combine="by_coords") as ds: repeat_names = {k: v.data.name for k, v in ds.data_vars.items()} for var_name, dask_name in original_names.items(): assert var_name in dask_name assert dask_name[:13] == "open_dataset-" assert original_names == repeat_names def test_dataarray_compute(self) -> None: # Test DataArray.compute() on dask backend. # The test for Dataset.compute() is already in DatasetIOBase; # however dask is the only tested backend which supports DataArrays actual = DataArray([1, 2]).chunk() computed = actual.compute() assert not actual._in_memory assert computed._in_memory assert_allclose(actual, computed, decode_bytes=False) def test_save_mfdataset_compute_false_roundtrip(self) -> None: from dask.delayed import Delayed original = Dataset({"foo": ("x", np.random.randn(10))}).chunk() datasets = [original.isel(x=slice(5)), original.isel(x=slice(5, 10))] with create_tmp_file(allow_cleanup_failure=ON_WINDOWS) as tmp1: with create_tmp_file(allow_cleanup_failure=ON_WINDOWS) as tmp2: delayed_obj = save_mfdataset( datasets, [tmp1, tmp2], engine=self.engine, compute=False ) assert isinstance(delayed_obj, Delayed) delayed_obj.compute() with open_mfdataset( [tmp1, tmp2], combine="nested", concat_dim="x" ) as actual: assert_identical(actual, original) def test_load_dataset(self) -> None: with create_tmp_file() as tmp: original = Dataset({"foo": ("x", np.random.randn(10))}) original.to_netcdf(tmp) ds = load_dataset(tmp) # this would fail if we used open_dataset instead of load_dataset ds.to_netcdf(tmp) def test_load_dataarray(self) -> None: with create_tmp_file() as tmp: original = Dataset({"foo": ("x", np.random.randn(10))}) original.to_netcdf(tmp) ds = load_dataarray(tmp) # this would fail if we used open_dataarray instead of # load_dataarray ds.to_netcdf(tmp) @pytest.mark.skipif( ON_WINDOWS, reason="counting number of tasks in graph fails on windows for some reason", ) def test_inline_array(self) -> None: with create_tmp_file() as tmp: original = Dataset({"foo": ("x", np.random.randn(10))}) original.to_netcdf(tmp) chunks = {"time": 10} def num_graph_nodes(obj): return len(obj.__dask_graph__()) with ( open_dataset(tmp, inline_array=False, chunks=chunks) as not_inlined_ds, open_dataset(tmp, inline_array=True, chunks=chunks) as inlined_ds, ): assert num_graph_nodes(inlined_ds) < num_graph_nodes(not_inlined_ds) with ( open_dataarray( tmp, inline_array=False, chunks=chunks ) as not_inlined_da, open_dataarray(tmp, inline_array=True, chunks=chunks) as inlined_da, ): assert num_graph_nodes(inlined_da) < num_graph_nodes(not_inlined_da) @requires_scipy_or_netCDF4 @requires_pydap @pytest.mark.filterwarnings("ignore:The binary mode of fromstring is deprecated") class TestPydap: def convert_to_pydap_dataset(self, original): from pydap.model import BaseType, DatasetType ds = DatasetType("bears", **original.attrs) for key, var in original.data_vars.items(): ds[key] = BaseType( key, var.values, dtype=var.values.dtype.kind, dims=var.dims, **var.attrs ) # check all dims are stored in ds for d in original.coords: ds[d] = BaseType(d, original[d].values, dims=(d,), **original[d].attrs) return ds @contextlib.contextmanager def create_datasets(self, **kwargs): with open_example_dataset("bears.nc") as expected: # print("QQ0:", expected["bears"].load()) pydap_ds = self.convert_to_pydap_dataset(expected) actual = open_dataset(PydapDataStore(pydap_ds)) # netcdf converts string to byte not unicode # fixed in pydap 3.5.6. https://github.com/pydap/pydap/issues/510 actual["bears"].values = actual["bears"].values.astype("S") yield actual, expected def test_cmp_local_file(self) -> None: with self.create_datasets() as (actual, expected): assert_equal(actual, expected) # global attributes should be global attributes on the dataset assert "NC_GLOBAL" not in actual.attrs assert "history" in actual.attrs # we don't check attributes exactly with assertDatasetIdentical() # because the test DAP server seems to insert some extra # attributes not found in the netCDF file. assert actual.attrs.keys() == expected.attrs.keys() with self.create_datasets() as (actual, expected): assert_equal(actual[{"l": 2}], expected[{"l": 2}]) with self.create_datasets() as (actual, expected): # always return arrays and not scalars # scalars will be promoted to unicode for numpy >= 2.3.0 assert_equal(actual.isel(i=[0], j=[-1]), expected.isel(i=[0], j=[-1])) with self.create_datasets() as (actual, expected): assert_equal(actual.isel(j=slice(1, 2)), expected.isel(j=slice(1, 2))) with self.create_datasets() as (actual, expected): indexers = {"i": [1, 0, 0], "j": [1, 2, 0, 1]} assert_equal(actual.isel(**indexers), expected.isel(**indexers)) with self.create_datasets() as (actual, expected): indexers2 = { "i": DataArray([0, 1, 0], dims="a"), "j": DataArray([0, 2, 1], dims="a"), } assert_equal(actual.isel(**indexers2), expected.isel(**indexers2)) def test_compatible_to_netcdf(self) -> None: # make sure it can be saved as a netcdf with self.create_datasets() as (actual, expected): with create_tmp_file() as tmp_file: actual.to_netcdf(tmp_file) with open_dataset(tmp_file) as actual2: assert_equal(actual2, expected) @requires_dask def test_dask(self) -> None: with self.create_datasets(chunks={"j": 2}) as (actual, expected): assert_equal(actual, expected) @network @requires_scipy_or_netCDF4 @requires_pydap class TestPydapOnline(TestPydap): @contextlib.contextmanager def create_dap2_datasets(self, **kwargs): # in pydap 3.5.0, urls defaults to dap2. url = "http://test.opendap.org/opendap/data/nc/bears.nc" actual = open_dataset(url, engine="pydap", **kwargs) # pydap <3.5.6 converts to unicode dtype=|U. Not what # xarray expects. Thus force to bytes dtype. pydap >=3.5.6 # does not convert to unicode. https://github.com/pydap/pydap/issues/510 actual["bears"].values = actual["bears"].values.astype("S") with open_example_dataset("bears.nc") as expected: yield actual, expected def output_grid_deprecation_warning_dap2dataset(self): with pytest.warns(DeprecationWarning, match="`output_grid` is deprecated"): with self.create_dap2_datasets(output_grid=True) as (actual, expected): assert_equal(actual, expected) def create_dap4_dataset(self, **kwargs): url = "dap4://test.opendap.org/opendap/data/nc/bears.nc" actual = open_dataset(url, engine="pydap", **kwargs) with open_example_dataset("bears.nc") as expected: # workaround to restore string which is converted to byte # only needed for pydap <3.5.6 https://github.com/pydap/pydap/issues/510 expected["bears"].values = expected["bears"].values.astype("S") yield actual, expected def test_session(self) -> None: from requests import Session session = Session() # blank requests.Session object with mock.patch("pydap.client.open_url") as mock_func: xr.backends.PydapDataStore.open("http://test.url", session=session) mock_func.assert_called_with( url="http://test.url", application=None, session=session, output_grid=False, timeout=120, verify=True, user_charset=None, ) class TestEncodingInvalid: def test_extract_nc4_variable_encoding(self) -> None: var = xr.Variable(("x",), [1, 2, 3], {}, {"foo": "bar"}) with pytest.raises(ValueError, match=r"unexpected encoding"): _extract_nc4_variable_encoding(var, raise_on_invalid=True) var = xr.Variable(("x",), [1, 2, 3], {}, {"chunking": (2, 1)}) encoding = _extract_nc4_variable_encoding(var) assert {} == encoding # regression test var = xr.Variable(("x",), [1, 2, 3], {}, {"shuffle": True}) encoding = _extract_nc4_variable_encoding(var, raise_on_invalid=True) assert {"shuffle": True} == encoding # Variables with unlim dims must be chunked on output. var = xr.Variable(("x",), [1, 2, 3], {}, {"contiguous": True}) encoding = _extract_nc4_variable_encoding(var, unlimited_dims=("x",)) assert {} == encoding @requires_netCDF4 def test_extract_nc4_variable_encoding_netcdf4(self): # New netCDF4 1.6.0 compression argument. var = xr.Variable(("x",), [1, 2, 3], {}, {"compression": "szlib"}) _extract_nc4_variable_encoding(var, backend="netCDF4", raise_on_invalid=True) @pytest.mark.xfail def test_extract_h5nc_encoding(self) -> None: # not supported with h5netcdf (yet) var = xr.Variable(("x",), [1, 2, 3], {}, {"least_significant_digit": 2}) with pytest.raises(ValueError, match=r"unexpected encoding"): _extract_nc4_variable_encoding(var, raise_on_invalid=True) class MiscObject: pass @requires_netCDF4 class TestValidateAttrs: def test_validating_attrs(self) -> None: def new_dataset(): return Dataset({"data": ("y", np.arange(10.0))}, {"y": np.arange(10)}) def new_dataset_and_dataset_attrs(): ds = new_dataset() return ds, ds.attrs def new_dataset_and_data_attrs(): ds = new_dataset() return ds, ds.data.attrs def new_dataset_and_coord_attrs(): ds = new_dataset() return ds, ds.coords["y"].attrs for new_dataset_and_attrs in [ new_dataset_and_dataset_attrs, new_dataset_and_data_attrs, new_dataset_and_coord_attrs, ]: ds, attrs = new_dataset_and_attrs() attrs[123] = "test" with pytest.raises(TypeError, match=r"Invalid name for attr: 123"): ds.to_netcdf("test.nc") ds, attrs = new_dataset_and_attrs() attrs[MiscObject()] = "test" with pytest.raises(TypeError, match=r"Invalid name for attr: "): ds.to_netcdf("test.nc") ds, attrs = new_dataset_and_attrs() attrs[""] = "test" with pytest.raises(ValueError, match=r"Invalid name for attr '':"): ds.to_netcdf("test.nc") # This one should work ds, attrs = new_dataset_and_attrs() attrs["test"] = "test" with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = {"a": 5} with pytest.raises(TypeError, match=r"Invalid value for attr 'test'"): ds.to_netcdf("test.nc") ds, attrs = new_dataset_and_attrs() attrs["test"] = MiscObject() with pytest.raises(TypeError, match=r"Invalid value for attr 'test'"): ds.to_netcdf("test.nc") ds, attrs = new_dataset_and_attrs() attrs["test"] = 5 with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = 3.14 with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = [1, 2, 3, 4] with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = (1.9, 2.5) with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = np.arange(5) with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = "This is a string" with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = "" with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) @requires_scipy_or_netCDF4 class TestDataArrayToNetCDF: def test_dataarray_to_netcdf_no_name(self) -> None: original_da = DataArray(np.arange(12).reshape((3, 4))) with create_tmp_file() as tmp: original_da.to_netcdf(tmp) with open_dataarray(tmp) as loaded_da: assert_identical(original_da, loaded_da) def test_dataarray_to_netcdf_with_name(self) -> None: original_da = DataArray(np.arange(12).reshape((3, 4)), name="test") with create_tmp_file() as tmp: original_da.to_netcdf(tmp) with open_dataarray(tmp) as loaded_da: assert_identical(original_da, loaded_da) def test_dataarray_to_netcdf_coord_name_clash(self) -> None: original_da = DataArray( np.arange(12).reshape((3, 4)), dims=["x", "y"], name="x" ) with create_tmp_file() as tmp: original_da.to_netcdf(tmp) with open_dataarray(tmp) as loaded_da: assert_identical(original_da, loaded_da) def test_open_dataarray_options(self) -> None: data = DataArray(np.arange(5), coords={"y": ("x", range(5))}, dims=["x"]) with create_tmp_file() as tmp: data.to_netcdf(tmp) expected = data.drop_vars("y") with open_dataarray(tmp, drop_variables=["y"]) as loaded: assert_identical(expected, loaded) @requires_scipy def test_dataarray_to_netcdf_return_bytes(self) -> None: # regression test for GH1410 data = xr.DataArray([1, 2, 3]) with pytest.warns( FutureWarning, match=re.escape("return value of to_netcdf() without a target"), ): output = data.to_netcdf(engine="scipy") assert isinstance(output, bytes) def test_dataarray_to_netcdf_no_name_pathlib(self) -> None: original_da = DataArray(np.arange(12).reshape((3, 4))) with create_tmp_file() as tmps: tmp = Path(tmps) original_da.to_netcdf(tmp) with open_dataarray(tmp) as loaded_da: assert_identical(original_da, loaded_da) @requires_zarr class TestDataArrayToZarr: def skip_if_zarr_python_3_and_zip_store(self, store) -> None: if has_zarr_v3 and isinstance(store, zarr.storage.ZipStore): pytest.skip( reason="zarr-python 3.x doesn't support reopening ZipStore with a new mode." ) def test_dataarray_to_zarr_no_name(self, tmp_store) -> None: self.skip_if_zarr_python_3_and_zip_store(tmp_store) original_da = DataArray(np.arange(12).reshape((3, 4))) original_da.to_zarr(tmp_store) with open_dataarray(tmp_store, engine="zarr") as loaded_da: assert_identical(original_da, loaded_da) def test_dataarray_to_zarr_with_name(self, tmp_store) -> None: self.skip_if_zarr_python_3_and_zip_store(tmp_store) original_da = DataArray(np.arange(12).reshape((3, 4)), name="test") original_da.to_zarr(tmp_store) with open_dataarray(tmp_store, engine="zarr") as loaded_da: assert_identical(original_da, loaded_da) def test_dataarray_to_zarr_coord_name_clash(self, tmp_store) -> None: self.skip_if_zarr_python_3_and_zip_store(tmp_store) original_da = DataArray( np.arange(12).reshape((3, 4)), dims=["x", "y"], name="x" ) original_da.to_zarr(tmp_store) with open_dataarray(tmp_store, engine="zarr") as loaded_da: assert_identical(original_da, loaded_da) def test_open_dataarray_options(self, tmp_store) -> None: self.skip_if_zarr_python_3_and_zip_store(tmp_store) data = DataArray(np.arange(5), coords={"y": ("x", range(1, 6))}, dims=["x"]) data.to_zarr(tmp_store) expected = data.drop_vars("y") with open_dataarray(tmp_store, engine="zarr", drop_variables=["y"]) as loaded: assert_identical(expected, loaded) @requires_dask def test_dataarray_to_zarr_compute_false(self, tmp_store) -> None: from dask.delayed import Delayed skip_if_zarr_format_3(tmp_store) original_da = DataArray(np.arange(12).reshape((3, 4))) output = original_da.to_zarr(tmp_store, compute=False) assert isinstance(output, Delayed) output.compute() with open_dataarray(tmp_store, engine="zarr") as loaded_da: assert_identical(original_da, loaded_da) @requires_dask def test_dataarray_to_zarr_align_chunks_true(self, tmp_store) -> None: # TODO: Improve data integrity checks when using Dask. # Detecting automatic alignment issues in Dask can be tricky, # as unintended misalignment might lead to subtle data corruption. # For now, ensure that the parameter is present, but explore # more robust verification methods to confirm data consistency. skip_if_zarr_format_3(tmp_store) arr = DataArray( np.arange(4), dims=["a"], coords={"a": np.arange(4)}, name="foo" ).chunk(a=(2, 1, 1)) arr.to_zarr( tmp_store, align_chunks=True, encoding={"foo": {"chunks": (3,)}}, ) with open_dataarray(tmp_store, engine="zarr") as loaded_da: assert_identical(arr, loaded_da) @requires_scipy_or_netCDF4 def test_no_warning_from_dask_effective_get() -> None: with create_tmp_file() as tmpfile: with assert_no_warnings(): ds = Dataset() ds.to_netcdf(tmpfile) @requires_scipy_or_netCDF4 def test_source_encoding_always_present() -> None: # Test for GH issue #2550. rnddata = np.random.randn(10) original = Dataset({"foo": ("x", rnddata)}) with create_tmp_file() as tmp: original.to_netcdf(tmp) with open_dataset(tmp) as ds: assert ds.encoding["source"] == tmp @requires_scipy_or_netCDF4 def test_source_encoding_always_present_with_pathlib() -> None: # Test for GH issue #5888. rnddata = np.random.randn(10) original = Dataset({"foo": ("x", rnddata)}) with create_tmp_file() as tmp: original.to_netcdf(tmp) with open_dataset(Path(tmp)) as ds: assert ds.encoding["source"] == tmp @requires_h5netcdf @requires_fsspec def test_source_encoding_always_present_with_fsspec() -> None: import fsspec rnddata = np.random.randn(10) original = Dataset({"foo": ("x", rnddata)}) with create_tmp_file() as tmp: original.to_netcdf(tmp) fs = fsspec.filesystem("file") with fs.open(tmp) as f, open_dataset(f) as ds: assert ds.encoding["source"] == tmp with fs.open(tmp) as f, open_mfdataset([f]) as ds: assert "foo" in ds def _assert_no_dates_out_of_range_warning(record): undesired_message = "dates out of range" for warning in record: assert undesired_message not in str(warning.message) @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", _STANDARD_CALENDARS) def test_use_cftime_standard_calendar_default_in_range(calendar) -> None: x = [0, 1] time = [0, 720] units_date = "2000-01-01" units = "days since 2000-01-01" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar x_timedeltas = np.array(x).astype("timedelta64[D]") time_timedeltas = np.array(time).astype("timedelta64[D]") decoded_x = np.datetime64(units_date, "ns") + x_timedeltas decoded_time = np.datetime64(units_date, "ns") + time_timedeltas expected_x = DataArray(decoded_x, [("time", decoded_time)], name="x") expected_time = DataArray(decoded_time, [("time", decoded_time)], name="time") with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with warnings.catch_warnings(record=True) as record: with open_dataset(tmp_file) as ds: assert_identical(expected_x, ds.x) assert_identical(expected_time, ds.time) _assert_no_dates_out_of_range_warning(record) @requires_cftime @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", ["standard", "gregorian"]) def test_use_cftime_standard_calendar_default_out_of_range(calendar) -> None: # todo: check, if we still need to test for two dates import cftime x = [0, 1] time = [0, 720] units = "days since 1582-01-01" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar decoded_x = cftime.num2date(x, units, calendar, only_use_cftime_datetimes=True) decoded_time = cftime.num2date( time, units, calendar, only_use_cftime_datetimes=True ) expected_x = DataArray(decoded_x, [("time", decoded_time)], name="x") expected_time = DataArray(decoded_time, [("time", decoded_time)], name="time") with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with pytest.warns(SerializationWarning): with open_dataset(tmp_file) as ds: assert_identical(expected_x, ds.x) assert_identical(expected_time, ds.time) @requires_cftime @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", _ALL_CALENDARS) @pytest.mark.parametrize("units_year", [1500, 2000, 2500]) def test_use_cftime_true(calendar, units_year) -> None: import cftime x = [0, 1] time = [0, 720] units = f"days since {units_year}-01-01" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar decoded_x = cftime.num2date(x, units, calendar, only_use_cftime_datetimes=True) decoded_time = cftime.num2date( time, units, calendar, only_use_cftime_datetimes=True ) expected_x = DataArray(decoded_x, [("time", decoded_time)], name="x") expected_time = DataArray(decoded_time, [("time", decoded_time)], name="time") with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with warnings.catch_warnings(record=True) as record: decoder = CFDatetimeCoder(use_cftime=True) with open_dataset(tmp_file, decode_times=decoder) as ds: assert_identical(expected_x, ds.x) assert_identical(expected_time, ds.time) _assert_no_dates_out_of_range_warning(record) @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", _STANDARD_CALENDARS) @pytest.mark.xfail # ( has_numpy_2, reason="https://github.com/pandas-dev/pandas/issues/56996") def test_use_cftime_false_standard_calendar_in_range(calendar) -> None: x = [0, 1] time = [0, 720] units_date = "2000-01-01" units = "days since 2000-01-01" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar x_timedeltas = np.array(x).astype("timedelta64[D]") time_timedeltas = np.array(time).astype("timedelta64[D]") decoded_x = np.datetime64(units_date, "ns") + x_timedeltas decoded_time = np.datetime64(units_date, "ns") + time_timedeltas expected_x = DataArray(decoded_x, [("time", decoded_time)], name="x") expected_time = DataArray(decoded_time, [("time", decoded_time)], name="time") with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with warnings.catch_warnings(record=True) as record: coder = xr.coders.CFDatetimeCoder(use_cftime=False) with open_dataset(tmp_file, decode_times=coder) as ds: assert_identical(expected_x, ds.x) assert_identical(expected_time, ds.time) _assert_no_dates_out_of_range_warning(record) @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", ["standard", "gregorian"]) def test_use_cftime_false_standard_calendar_out_of_range(calendar) -> None: x = [0, 1] time = [0, 720] units = "days since 1582-01-01" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with pytest.raises((OutOfBoundsDatetime, ValueError)): decoder = CFDatetimeCoder(use_cftime=False) open_dataset(tmp_file, decode_times=decoder) @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", _NON_STANDARD_CALENDARS) @pytest.mark.parametrize("units_year", [1500, 2000, 2500]) def test_use_cftime_false_nonstandard_calendar(calendar, units_year) -> None: x = [0, 1] time = [0, 720] units = f"days since {units_year}" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with pytest.raises((OutOfBoundsDatetime, ValueError)): decoder = CFDatetimeCoder(use_cftime=False) open_dataset(tmp_file, decode_times=decoder) @pytest.mark.parametrize("engine", ["netcdf4", "scipy"]) def test_invalid_netcdf_raises(engine) -> None: data = create_test_data() with pytest.raises(ValueError, match=r"unrecognized option 'invalid_netcdf'"): data.to_netcdf("foo.nc", engine=engine, invalid_netcdf=True) @requires_zarr def test_encode_zarr_attr_value() -> None: # array -> list arr = np.array([1, 2, 3]) expected1 = [1, 2, 3] actual1 = backends.zarr.encode_zarr_attr_value(arr) assert isinstance(actual1, list) assert actual1 == expected1 # scalar array -> scalar sarr = np.array(1)[()] expected2 = 1 actual2 = backends.zarr.encode_zarr_attr_value(sarr) assert isinstance(actual2, int) assert actual2 == expected2 # string -> string (no change) expected3 = "foo" actual3 = backends.zarr.encode_zarr_attr_value(expected3) assert isinstance(actual3, str) assert actual3 == expected3 @requires_zarr def test_extract_zarr_variable_encoding() -> None: var = xr.Variable("x", [1, 2]) actual = backends.zarr.extract_zarr_variable_encoding(var, zarr_format=3) assert "chunks" in actual assert actual["chunks"] == ("auto" if has_zarr_v3 else None) var = xr.Variable("x", [1, 2], encoding={"chunks": (1,)}) actual = backends.zarr.extract_zarr_variable_encoding(var, zarr_format=3) assert actual["chunks"] == (1,) # does not raise on invalid var = xr.Variable("x", [1, 2], encoding={"foo": (1,)}) actual = backends.zarr.extract_zarr_variable_encoding(var, zarr_format=3) # raises on invalid var = xr.Variable("x", [1, 2], encoding={"foo": (1,)}) with pytest.raises(ValueError, match=r"unexpected encoding parameters"): actual = backends.zarr.extract_zarr_variable_encoding( var, raise_on_invalid=True, zarr_format=3 ) @requires_zarr @requires_fsspec @pytest.mark.filterwarnings("ignore:deallocating CachingFileManager") def test_open_fsspec() -> None: import fsspec if not hasattr(zarr.storage, "FSStore") or not hasattr( zarr.storage.FSStore, "getitems" ): pytest.skip("zarr too old") ds = open_dataset(os.path.join(os.path.dirname(__file__), "data", "example_1.nc")) m = fsspec.filesystem("memory") mm = m.get_mapper("out1.zarr") ds.to_zarr(mm) # old interface ds0 = ds.copy() # pd.to_timedelta returns ns-precision, but the example data is in second precision # so we need to fix this ds0["time"] = ds.time + np.timedelta64(1, "D") mm = m.get_mapper("out2.zarr") ds0.to_zarr(mm) # old interface # single dataset url = "memory://out2.zarr" ds2 = open_dataset(url, engine="zarr") xr.testing.assert_equal(ds0, ds2) # single dataset with caching url = "simplecache::memory://out2.zarr" ds2 = open_dataset(url, engine="zarr") xr.testing.assert_equal(ds0, ds2) # open_mfdataset requires dask if has_dask: # multi dataset url = "memory://out*.zarr" ds2 = open_mfdataset(url, engine="zarr") xr.testing.assert_equal(xr.concat([ds, ds0], dim="time"), ds2) # multi dataset with caching url = "simplecache::memory://out*.zarr" ds2 = open_mfdataset(url, engine="zarr") xr.testing.assert_equal(xr.concat([ds, ds0], dim="time"), ds2) @requires_h5netcdf @requires_netCDF4 def test_load_single_value_h5netcdf(tmp_path: Path) -> None: """Test that numeric single-element vector attributes are handled fine. At present (h5netcdf v0.8.1), the h5netcdf exposes single-valued numeric variable attributes as arrays of length 1, as opposed to scalars for the NetCDF4 backend. This was leading to a ValueError upon loading a single value from a file, see #4471. Test that loading causes no failure. """ ds = xr.Dataset( { "test": xr.DataArray( np.array([0]), dims=("x",), attrs={"scale_factor": 1, "add_offset": 0} ) } ) ds.to_netcdf(tmp_path / "test.nc") with xr.open_dataset(tmp_path / "test.nc", engine="h5netcdf") as ds2: ds2["test"][0].load() @requires_zarr @requires_dask @pytest.mark.parametrize( "chunks", ["auto", -1, {}, {"x": "auto"}, {"x": -1}, {"x": "auto", "y": -1}] ) def test_open_dataset_chunking_zarr(chunks, tmp_path: Path) -> None: encoded_chunks = 100 dask_arr = da.from_array( np.ones((500, 500), dtype="float64"), chunks=encoded_chunks ) ds = xr.Dataset( { "test": xr.DataArray( dask_arr, dims=("x", "y"), ) } ) ds["test"].encoding["chunks"] = encoded_chunks ds.to_zarr(tmp_path / "test.zarr") with dask.config.set({"array.chunk-size": "1MiB"}): expected = ds.chunk(chunks) with open_dataset( tmp_path / "test.zarr", engine="zarr", chunks=chunks ) as actual: xr.testing.assert_chunks_equal(actual, expected) @requires_zarr @requires_dask @pytest.mark.parametrize( "chunks", ["auto", -1, {}, {"x": "auto"}, {"x": -1}, {"x": "auto", "y": -1}] ) @pytest.mark.filterwarnings("ignore:The specified chunks separate") def test_chunking_consintency(chunks, tmp_path: Path) -> None: encoded_chunks: dict[str, Any] = {} dask_arr = da.from_array( np.ones((500, 500), dtype="float64"), chunks=encoded_chunks ) ds = xr.Dataset( { "test": xr.DataArray( dask_arr, dims=("x", "y"), ) } ) ds["test"].encoding["chunks"] = encoded_chunks ds.to_zarr(tmp_path / "test.zarr") ds.to_netcdf(tmp_path / "test.nc") with dask.config.set({"array.chunk-size": "1MiB"}): expected = ds.chunk(chunks) with xr.open_dataset( tmp_path / "test.zarr", engine="zarr", chunks=chunks ) as actual: xr.testing.assert_chunks_equal(actual, expected) with xr.open_dataset(tmp_path / "test.nc", chunks=chunks) as actual: xr.testing.assert_chunks_equal(actual, expected) def _check_guess_can_open_and_open(entrypoint, obj, engine, expected): assert entrypoint.guess_can_open(obj) with open_dataset(obj, engine=engine) as actual: assert_identical(expected, actual) @requires_netCDF4 def test_netcdf4_entrypoint(tmp_path: Path) -> None: entrypoint = NetCDF4BackendEntrypoint() ds = create_test_data() path = tmp_path / "foo" ds.to_netcdf(path, format="NETCDF3_CLASSIC") _check_guess_can_open_and_open(entrypoint, path, engine="netcdf4", expected=ds) _check_guess_can_open_and_open(entrypoint, str(path), engine="netcdf4", expected=ds) path = tmp_path / "bar" ds.to_netcdf(path, format="NETCDF4_CLASSIC") _check_guess_can_open_and_open(entrypoint, path, engine="netcdf4", expected=ds) _check_guess_can_open_and_open(entrypoint, str(path), engine="netcdf4", expected=ds) assert entrypoint.guess_can_open("http://something/remote") assert entrypoint.guess_can_open("something-local.nc") assert entrypoint.guess_can_open("something-local.nc4") assert entrypoint.guess_can_open("something-local.cdf") assert not entrypoint.guess_can_open("not-found-and-no-extension") path = tmp_path / "baz" with open(path, "wb") as f: f.write(b"not-a-netcdf-file") assert not entrypoint.guess_can_open(path) @requires_scipy def test_scipy_entrypoint(tmp_path: Path) -> None: entrypoint = ScipyBackendEntrypoint() ds = create_test_data() path = tmp_path / "foo" ds.to_netcdf(path, engine="scipy") _check_guess_can_open_and_open(entrypoint, path, engine="scipy", expected=ds) _check_guess_can_open_and_open(entrypoint, str(path), engine="scipy", expected=ds) with open(path, "rb") as f: _check_guess_can_open_and_open(entrypoint, f, engine="scipy", expected=ds) with pytest.warns( FutureWarning, match=re.escape("return value of to_netcdf() without a target") ): contents = ds.to_netcdf(engine="scipy") _check_guess_can_open_and_open(entrypoint, contents, engine="scipy", expected=ds) _check_guess_can_open_and_open( entrypoint, BytesIO(contents), engine="scipy", expected=ds ) path = tmp_path / "foo.nc.gz" with gzip.open(path, mode="wb") as f: f.write(contents) _check_guess_can_open_and_open(entrypoint, path, engine="scipy", expected=ds) _check_guess_can_open_and_open(entrypoint, str(path), engine="scipy", expected=ds) assert entrypoint.guess_can_open("something-local.nc") assert entrypoint.guess_can_open("something-local.nc.gz") assert not entrypoint.guess_can_open("not-found-and-no-extension") assert not entrypoint.guess_can_open(b"not-a-netcdf-file") @requires_h5netcdf def test_h5netcdf_entrypoint(tmp_path: Path) -> None: entrypoint = H5netcdfBackendEntrypoint() ds = create_test_data() path = tmp_path / "foo" ds.to_netcdf(path, engine="h5netcdf") _check_guess_can_open_and_open(entrypoint, path, engine="h5netcdf", expected=ds) _check_guess_can_open_and_open( entrypoint, str(path), engine="h5netcdf", expected=ds ) with open(path, "rb") as f: _check_guess_can_open_and_open(entrypoint, f, engine="h5netcdf", expected=ds) assert entrypoint.guess_can_open("something-local.nc") assert entrypoint.guess_can_open("something-local.nc4") assert entrypoint.guess_can_open("something-local.cdf") assert not entrypoint.guess_can_open("not-found-and-no-extension") @requires_netCDF4 @pytest.mark.parametrize("str_type", (str, np.str_)) def test_write_file_from_np_str(str_type: type[str | np.str_], tmpdir: str) -> None: # https://github.com/pydata/xarray/pull/5264 scenarios = [str_type(v) for v in ["scenario_a", "scenario_b", "scenario_c"]] years = range(2015, 2100 + 1) tdf = pd.DataFrame( data=np.random.random((len(scenarios), len(years))), columns=years, index=scenarios, ) tdf.index.name = "scenario" tdf.columns.name = "year" tdf = cast(pd.DataFrame, tdf.stack()) tdf.name = "tas" txr = tdf.to_xarray() txr.to_netcdf(tmpdir.join("test.nc")) @requires_zarr @requires_netCDF4 class TestNCZarr: @property def netcdfc_version(self): return Version(nc4.getlibversion().split()[0].split("-development")[0]) def _create_nczarr(self, filename): if self.netcdfc_version < Version("4.8.1"): pytest.skip("requires netcdf-c>=4.8.1") if platform.system() == "Windows" and self.netcdfc_version == Version("4.8.1"): # Bug in netcdf-c==4.8.1 (typo: Nan instead of NaN) # https://github.com/Unidata/netcdf-c/issues/2265 pytest.skip("netcdf-c==4.8.1 has issues on Windows") ds = create_test_data() # Drop dim3: netcdf-c does not support dtype=' None: with create_tmp_file(suffix=".zarr") as tmp: expected = self._create_nczarr(tmp) actual = xr.open_zarr(tmp, consolidated=False) assert_identical(expected, actual) def test_overwriting_nczarr(self) -> None: with create_tmp_file(suffix=".zarr") as tmp: ds = self._create_nczarr(tmp) expected = ds[["var1"]] expected.to_zarr(tmp, mode="w") actual = xr.open_zarr(tmp, consolidated=False) assert_identical(expected, actual) @pytest.mark.parametrize("mode", ["a", "r+"]) @pytest.mark.filterwarnings("ignore:.*non-consolidated metadata.*") def test_raise_writing_to_nczarr(self, mode) -> None: if self.netcdfc_version > Version("4.8.1"): pytest.skip("netcdf-c>4.8.1 adds the _ARRAY_DIMENSIONS attribute") with create_tmp_file(suffix=".zarr") as tmp: ds = self._create_nczarr(tmp) with pytest.raises( KeyError, match="missing the attribute `_ARRAY_DIMENSIONS`," ): ds.to_zarr(tmp, mode=mode) @requires_netCDF4 @requires_dask @pytest.mark.usefixtures("default_zarr_format") def test_pickle_open_mfdataset_dataset(): with open_example_mfdataset(["bears.nc"]) as ds: assert_identical(ds, pickle.loads(pickle.dumps(ds))) @requires_zarr @pytest.mark.usefixtures("default_zarr_format") def test_zarr_closing_internal_zip_store(): store_name = "tmp.zarr.zip" original_da = DataArray(np.arange(12).reshape((3, 4))) original_da.to_zarr(store_name, mode="w") with open_dataarray(store_name, engine="zarr") as loaded_da: assert_identical(original_da, loaded_da) @requires_zarr @pytest.mark.parametrize("create_default_indexes", [True, False]) def test_zarr_create_default_indexes(tmp_path, create_default_indexes) -> None: from xarray.core.indexes import PandasIndex store_path = tmp_path / "tmp.zarr" original_ds = xr.Dataset({"data": ("x", np.arange(3))}, coords={"x": [-1, 0, 1]}) original_ds.to_zarr(store_path, mode="w") with open_dataset( store_path, engine="zarr", create_default_indexes=create_default_indexes ) as loaded_ds: if create_default_indexes: assert list(loaded_ds.xindexes) == ["x"] and isinstance( loaded_ds.xindexes["x"], PandasIndex ) else: assert len(loaded_ds.xindexes) == 0 @requires_zarr @pytest.mark.usefixtures("default_zarr_format") def test_raises_key_error_on_invalid_zarr_store(tmp_path): root = zarr.open_group(tmp_path / "tmp.zarr") if Version(zarr.__version__) < Version("3.0.0"): root.create_dataset("bar", shape=(3, 5), dtype=np.float32) else: root.create_array("bar", shape=(3, 5), dtype=np.float32) with pytest.raises(KeyError, match=r"xarray to determine variable dimensions"): xr.open_zarr(tmp_path / "tmp.zarr", consolidated=False) @requires_zarr @pytest.mark.usefixtures("default_zarr_format") class TestZarrRegionAuto: """These are separated out since we should not need to test this logic with every store.""" @contextlib.contextmanager def create_zarr_target(self): with create_tmp_file(suffix=".zarr") as tmp: yield tmp @contextlib.contextmanager def create(self): x = np.arange(0, 50, 10) y = np.arange(0, 20, 2) data = np.ones((5, 10)) ds = xr.Dataset( {"test": xr.DataArray(data, dims=("x", "y"), coords={"x": x, "y": y})} ) with self.create_zarr_target() as target: self.save(target, ds) yield target, ds def save(self, target, ds, **kwargs): ds.to_zarr(target, **kwargs) @pytest.mark.parametrize( "region", [ pytest.param("auto", id="full-auto"), pytest.param({"x": "auto", "y": slice(6, 8)}, id="mixed-auto"), ], ) def test_zarr_region_auto(self, region): with self.create() as (target, ds): ds_region = 1 + ds.isel(x=slice(2, 4), y=slice(6, 8)) self.save(target, ds_region, region=region) ds_updated = xr.open_zarr(target) expected = ds.copy() expected["test"][2:4, 6:8] += 1 assert_identical(ds_updated, expected) def test_zarr_region_auto_noncontiguous(self): with self.create() as (target, ds): with pytest.raises(ValueError): self.save(target, ds.isel(x=[0, 2, 3], y=[5, 6]), region="auto") dsnew = ds.copy() dsnew["x"] = dsnew.x + 5 with pytest.raises(KeyError): self.save(target, dsnew, region="auto") def test_zarr_region_index_write(self, tmp_path): region: Mapping[str, slice] | Literal["auto"] region_slice = dict(x=slice(2, 4), y=slice(6, 8)) with self.create() as (target, ds): ds_region = 1 + ds.isel(region_slice) for region in [region_slice, "auto"]: # type: ignore[assignment] with patch.object( ZarrStore, "set_variables", side_effect=ZarrStore.set_variables, autospec=True, ) as mock: self.save(target, ds_region, region=region, mode="r+") # should write the data vars but never the index vars with auto mode for call in mock.call_args_list: written_variables = call.args[1].keys() assert "test" in written_variables assert "x" not in written_variables assert "y" not in written_variables def test_zarr_region_append(self): with self.create() as (target, ds): x_new = np.arange(40, 70, 10) data_new = np.ones((3, 10)) ds_new = xr.Dataset( { "test": xr.DataArray( data_new, dims=("x", "y"), coords={"x": x_new, "y": ds.y}, ) } ) # Now it is valid to use auto region detection with the append mode, # but it is still unsafe to modify dimensions or metadata using the region # parameter. with pytest.raises(KeyError): self.save(target, ds_new, mode="a", append_dim="x", region="auto") def test_zarr_region(self): with self.create() as (target, ds): ds_transposed = ds.transpose("y", "x") ds_region = 1 + ds_transposed.isel(x=[0], y=[0]) self.save(target, ds_region, region={"x": slice(0, 1), "y": slice(0, 1)}) # Write without region self.save(target, ds_transposed, mode="r+") @requires_dask def test_zarr_region_chunk_partial(self): """ Check that writing to partial chunks with `region` fails, assuming `safe_chunks=False`. """ ds = ( xr.DataArray(np.arange(120).reshape(4, 3, -1), dims=list("abc")) .rename("var1") .to_dataset() ) with self.create_zarr_target() as target: self.save(target, ds.chunk(5), compute=False, mode="w") with pytest.raises(ValueError): for r in range(ds.sizes["a"]): self.save( target, ds.chunk(3).isel(a=[r]), region=dict(a=slice(r, r + 1)) ) @requires_dask def test_zarr_append_chunk_partial(self): t_coords = np.array([np.datetime64("2020-01-01").astype("datetime64[ns]")]) data = np.ones((10, 10)) da = xr.DataArray( data.reshape((-1, 10, 10)), dims=["time", "x", "y"], coords={"time": t_coords}, name="foo", ) new_time = np.array([np.datetime64("2021-01-01").astype("datetime64[ns]")]) da2 = xr.DataArray( data.reshape((-1, 10, 10)), dims=["time", "x", "y"], coords={"time": new_time}, name="foo", ) with self.create_zarr_target() as target: self.save(target, da, mode="w", encoding={"foo": {"chunks": (5, 5, 1)}}) with pytest.raises(ValueError, match="encoding was provided"): self.save( target, da2, append_dim="time", mode="a", encoding={"foo": {"chunks": (1, 1, 1)}}, ) # chunking with dask sidesteps the encoding check, so we need a different check with pytest.raises(ValueError, match="Specified Zarr chunks"): self.save( target, da2.chunk({"x": 1, "y": 1, "time": 1}), append_dim="time", mode="a", ) @requires_dask def test_zarr_region_chunk_partial_offset(self): # https://github.com/pydata/xarray/pull/8459#issuecomment-1819417545 with self.create_zarr_target() as store: data = np.ones((30,)) da = xr.DataArray( data, dims=["x"], coords={"x": range(30)}, name="foo" ).chunk(x=10) self.save(store, da, compute=False) self.save(store, da.isel(x=slice(10)).chunk(x=(10,)), region="auto") self.save( store, da.isel(x=slice(5, 25)).chunk(x=(10, 10)), safe_chunks=False, region="auto", ) with pytest.raises(ValueError): self.save( store, da.isel(x=slice(5, 25)).chunk(x=(10, 10)), region="auto" ) @requires_dask def test_zarr_safe_chunk_append_dim(self): with self.create_zarr_target() as store: data = np.ones((20,)) da = xr.DataArray( data, dims=["x"], coords={"x": range(20)}, name="foo" ).chunk(x=5) self.save(store, da.isel(x=slice(0, 7)), safe_chunks=True, mode="w") with pytest.raises(ValueError): # If the first chunk is smaller than the border size then raise an error self.save( store, da.isel(x=slice(7, 11)).chunk(x=(2, 2)), append_dim="x", safe_chunks=True, ) self.save(store, da.isel(x=slice(0, 7)), safe_chunks=True, mode="w") # If the first chunk is of the size of the border size then it is valid self.save( store, da.isel(x=slice(7, 11)).chunk(x=(3, 1)), safe_chunks=True, append_dim="x", ) assert xr.open_zarr(store)["foo"].equals(da.isel(x=slice(0, 11))) self.save(store, da.isel(x=slice(0, 7)), safe_chunks=True, mode="w") # If the first chunk is of the size of the border size + N * zchunk then it is valid self.save( store, da.isel(x=slice(7, 17)).chunk(x=(8, 2)), safe_chunks=True, append_dim="x", ) assert xr.open_zarr(store)["foo"].equals(da.isel(x=slice(0, 17))) self.save(store, da.isel(x=slice(0, 7)), safe_chunks=True, mode="w") with pytest.raises(ValueError): # If the first chunk is valid but the other are not then raise an error self.save( store, da.isel(x=slice(7, 14)).chunk(x=(3, 3, 1)), append_dim="x", safe_chunks=True, ) self.save(store, da.isel(x=slice(0, 7)), safe_chunks=True, mode="w") with pytest.raises(ValueError): # If the first chunk have a size bigger than the border size but not enough # to complete the size of the next chunk then an error must be raised self.save( store, da.isel(x=slice(7, 14)).chunk(x=(4, 3)), append_dim="x", safe_chunks=True, ) self.save(store, da.isel(x=slice(0, 7)), safe_chunks=True, mode="w") # Append with a single chunk it's totally valid, # and it does not matter the size of the chunk self.save( store, da.isel(x=slice(7, 19)).chunk(x=-1), append_dim="x", safe_chunks=True, ) assert xr.open_zarr(store)["foo"].equals(da.isel(x=slice(0, 19))) @requires_dask @pytest.mark.parametrize("mode", ["r+", "a"]) def test_zarr_safe_chunk_region(self, mode: Literal["r+", "a"]): with self.create_zarr_target() as store: arr = xr.DataArray( list(range(11)), dims=["a"], coords={"a": list(range(11))}, name="foo" ).chunk(a=3) self.save(store, arr, mode="w") with pytest.raises(ValueError): # There are two Dask chunks on the same Zarr chunk, # which means that it is unsafe in any mode self.save( store, arr.isel(a=slice(0, 3)).chunk(a=(2, 1)), region="auto", mode=mode, ) with pytest.raises(ValueError): # the first chunk is covering the border size, but it is not # completely covering the second chunk, which means that it is # unsafe in any mode self.save( store, arr.isel(a=slice(1, 5)).chunk(a=(3, 1)), region="auto", mode=mode, ) with pytest.raises(ValueError): # The first chunk is safe but the other two chunks are overlapping with # the same Zarr chunk self.save( store, arr.isel(a=slice(0, 5)).chunk(a=(3, 1, 1)), region="auto", mode=mode, ) # Fully update two contiguous chunks is safe in any mode self.save(store, arr.isel(a=slice(3, 9)), region="auto", mode=mode) # The last chunk is considered full based on their current size (2) self.save(store, arr.isel(a=slice(9, 11)), region="auto", mode=mode) self.save( store, arr.isel(a=slice(6, None)).chunk(a=-1), region="auto", mode=mode ) # Write the last chunk of a region partially is safe in "a" mode self.save(store, arr.isel(a=slice(3, 8)), region="auto", mode="a") with pytest.raises(ValueError): # with "r+" mode it is invalid to write partial chunk self.save(store, arr.isel(a=slice(3, 8)), region="auto", mode="r+") # This is safe with mode "a", the border size is covered by the first chunk of Dask self.save( store, arr.isel(a=slice(1, 4)).chunk(a=(2, 1)), region="auto", mode="a" ) with pytest.raises(ValueError): # This is considered unsafe in mode "r+" because it is writing in a partial chunk self.save( store, arr.isel(a=slice(1, 4)).chunk(a=(2, 1)), region="auto", mode="r+", ) # This is safe on mode "a" because there is a single dask chunk self.save( store, arr.isel(a=slice(1, 5)).chunk(a=(4,)), region="auto", mode="a" ) with pytest.raises(ValueError): # This is unsafe on mode "r+", because the Dask chunk is partially writing # in the first chunk of Zarr self.save( store, arr.isel(a=slice(1, 5)).chunk(a=(4,)), region="auto", mode="r+", ) # The first chunk is completely covering the first Zarr chunk # and the last chunk is a partial one self.save( store, arr.isel(a=slice(0, 5)).chunk(a=(3, 2)), region="auto", mode="a" ) with pytest.raises(ValueError): # The last chunk is partial, so it is considered unsafe on mode "r+" self.save( store, arr.isel(a=slice(0, 5)).chunk(a=(3, 2)), region="auto", mode="r+", ) # The first chunk is covering the border size (2 elements) # and also the second chunk (3 elements), so it is valid self.save( store, arr.isel(a=slice(1, 8)).chunk(a=(5, 2)), region="auto", mode="a" ) with pytest.raises(ValueError): # The first chunk is not fully covering the first zarr chunk self.save( store, arr.isel(a=slice(1, 8)).chunk(a=(5, 2)), region="auto", mode="r+", ) with pytest.raises(ValueError): # Validate that the border condition is not affecting the "r+" mode self.save(store, arr.isel(a=slice(1, 9)), region="auto", mode="r+") self.save(store, arr.isel(a=slice(10, 11)), region="auto", mode="a") with pytest.raises(ValueError): # Validate that even if we write with a single Dask chunk on the last Zarr # chunk it is still unsafe if it is not fully covering it # (the last Zarr chunk has size 2) self.save(store, arr.isel(a=slice(10, 11)), region="auto", mode="r+") # Validate the same as the above test but in the beginning of the last chunk self.save(store, arr.isel(a=slice(9, 10)), region="auto", mode="a") with pytest.raises(ValueError): self.save(store, arr.isel(a=slice(9, 10)), region="auto", mode="r+") self.save( store, arr.isel(a=slice(7, None)).chunk(a=-1), region="auto", mode="a" ) with pytest.raises(ValueError): # Test that even a Dask chunk that covers the last Zarr chunk can be unsafe # if it is partial covering other Zarr chunks self.save( store, arr.isel(a=slice(7, None)).chunk(a=-1), region="auto", mode="r+", ) with pytest.raises(ValueError): # If the chunk is of size equal to the one in the Zarr encoding, but # it is partially writing in the first chunk then raise an error self.save( store, arr.isel(a=slice(8, None)).chunk(a=3), region="auto", mode="r+", ) with pytest.raises(ValueError): self.save( store, arr.isel(a=slice(5, -1)).chunk(a=5), region="auto", mode="r+" ) # Test if the code is detecting the last chunk correctly data = np.random.default_rng(0).random((2920, 25, 53)) ds = xr.Dataset({"temperature": (("time", "lat", "lon"), data)}) chunks = {"time": 1000, "lat": 25, "lon": 53} self.save(store, ds.chunk(chunks), compute=False, mode="w") region = {"time": slice(1000, 2000, 1)} chunk = ds.isel(region) chunk = chunk.chunk() self.save(store, chunk.chunk(), region=region) @requires_h5netcdf @requires_fsspec def test_h5netcdf_storage_options() -> None: with create_tmp_files(2, allow_cleanup_failure=ON_WINDOWS) as (f1, f2): ds1 = create_test_data() ds1.to_netcdf(f1, engine="h5netcdf") ds2 = create_test_data() ds2.to_netcdf(f2, engine="h5netcdf") files = [f"file://{f}" for f in [f1, f2]] with xr.open_mfdataset( files, engine="h5netcdf", concat_dim="time", data_vars="all", combine="nested", storage_options={"skip_instance_cache": False}, ) as ds: assert_identical(xr.concat([ds1, ds2], dim="time", data_vars="all"), ds)